Category: Communication

How ChatGPT Shapes the Future

Posted on by Jean Chalopin

In recent years, the AI industry has grown significantly, with forecasts that the worldwide market will reach $190.61 billion by 2025, expanding at a CAGR of 36.2% between 2020 and 2025. The Covid-19 pandemic has only hastened this rise, as businesses have been forced to adjust swiftly to remote working and growing digitisation. 

The pandemic has brought to light the significance of technology in industries such as healthcare and e-commerce.

Introducing ChatGPT

ChatGPT is an AI model created by OpenAI that can potentially influence the AI market’s evolution in various ways. ChatGPT may be linked to a wide range of applications and services that need natural language processing (NLP), such as customer service, chatbots, and virtual assistants. This may raise the need for NLP-based AI solutions, which would help the AI industry flourish.

ChatGPT may also be used to train other AI models, which can accelerate the development and implementation of AI-powered apps and services. This can improve the efficiency of the AI development process, contributing to the growth of the AI market.

Furthermore, ChatGPT’s capacity to create human-like writing, which can be utilised for various content creation and optimisation activities, has the potential to propel the AI market forward. ChatGPT, for example, may produce product descriptions, marketing text, and even news pieces, reducing the time and effort necessary for content generation while enhancing output quality. 

Below is a simple example of how it can write a product description for Coca-Cola within seconds. 

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The Benefits of ChatGPT

One of the primary benefits of ChatGPT is its ability to help users improve their writing and language skills. ChatGPT can help individuals become more effective communicators by providing real-time feedback and suggestions, whether they are writing emails, composing reports, or creating content for social media. 

For example, sales and marketing professionals can use ChatGPT to improve their email writing, helping them to better engage with prospects and customers. Additionally, educators can use technology to help students improve their writing and critical thinking skills without needing human grading and feedback.

Another critical benefit of ChatGPT is its ability to support knowledge management and collaboration. By using the technology to automate repetitive tasks, such as summarising reports or answering frequently asked questions, organisations can free up time and resources for more strategic initiatives. 

This can help companies become more efficient, increase productivity, and enhance customer service. For example, customer service teams can use ChatGPT to respond quickly to customer inquiries and resolve issues, reducing wait times and improving the customer experience.

The example below shows how a customer might be able to resolve a query about their home insurance without speaking to a human.

How ChatGPT Augments Roles

ChatGPT can significantly augment the functions of different departments in an organisation, including Data, IT, Marketing, Development, Finance, and Compliance.

Data 

For Data teams, it can assist in processing large amounts of data to provide insights and support decision-making. It can benefit data teams in their coding endeavours, particularly when it comes to writing code in SQL or Python. 

ChatGPT’s ability to provide suggestions for completing code snippets, identify syntax errors and suggest corrections, and generate complete code snippets based on specific requirements, can save data teams valuable time and effort. Furthermore, it can serve as a repository of coding knowledge that can be easily shared among team members. 

For example, if a data team member is working on a SQL query and encounters a roadblock, they can ask ChatGPT for advice on how to proceed. It can then provide suggestions for optimising the query or offer alternative solutions based on its vast knowledge of SQL coding best practices. By utilising its coding capabilities, data teams can improve their coding efficiency and accuracy, freeing them up to focus on more complex tasks.

IT

IT teams can use ChatGPT to automate various IT operations tasks and build a knowledge management system. It may also be incorporated with IT systems to give users rapid and accurate replies to technical assistance enquiries, decreasing the IT team’s burden.

Furthermore, ChatGPT can create a knowledge management system to store and retrieve information about IT systems and procedures, increasing the team’s productivity. IT teams may also use its natural language processing skills to examine massive quantities of log data and give insights into system performance and potential faults.

Marketing

Marketing teams can use ChatGPT to generate high-quality content and build conversational AI chatbots for customer service and sales. You can watch a video below on how ChatGPT built an entire marketing campaign in minutes. 

https://www.youtube.com/watch?v=gI1UL1cHHjk
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Marketing teams still need to ask the right questions, but ChatGPT saves time and efficiency. 

Finance

For Finance teams, it can be integrated into financial systems to assist with data analysis and decision-making. It may assist finance teams in making more informed decisions and improving financial planning and forecasting. 

ChatGPT may also help finance teams automate operations, including calculating financial ratios, creating reports, and tracking spending. Furthermore, ChatGPT’s natural language processing skills may be utilised to analyse financial data and discover trends, allowing finance teams to recognise opportunities and possible hazards quickly.

Compliance

Compliance teams can use ChatGPT to ensure compliance with regulations and standards by automating various compliance tasks. 

It may also aid in the categorisation and classification of enormous volumes of data, as well as the investigation of complicated legislation and laws. Furthermore, it may give real-time responses to staff inquiries, decreasing the time spent on manual research and enhancing the compliance team’s productivity. The capacity of the language model to interpret and create human-like writing makes it a powerful tool for firms wanting to strengthen their compliance procedures.

By augmenting the roles of different departments, ChatGPT can help organisations increase productivity and improve the quality of their work. Some entrepreneurs are using the technology to brainstorm business ideas. It’s like having a friend to bounce your thoughts between. 

Risks of ChatGPT

Despite these benefits, there are also some risks associated with ChatGPT that must be considered. 

One of the primary risks is the potential for the technology to promote cheating and plagiarism. For example, students may use technology to generate homework assignments, or employees may use it to create reports and presentations without doing the necessary research and analysis. 

To mitigate this risk, it is essential for organisations to communicate the acceptable use of the technology clearly and to have clear policies and procedures in place to monitor and enforce compliance.

Another risk is the potential for the technology to perpetuate bias and harmful stereotypes. As the model has been trained on a large corpus of text, it may generate offensive or inappropriate language or reinforce negative stereotypes. It is vital for organisations to use the technology responsibly and ethically and to regularly review and update the training data to ensure that it is inclusive and free from bias.

AI for People

Despite these risks, companies are already using ChatGPT in innovative and impactful ways. For example, OpenAI partnered with the non-profit organisation ‘AI for People’ to develop a tool that uses ChatGPT to support mental health and well-being. 

The tool uses natural language processing and machine learning to provide users with personalised feedback and support, helping them manage stress, anxiety, and depression. OpenAI has also worked with news organisations and journalists to develop an AI-powered writing assistant that can help writers quickly generate high-quality, accurate news articles.

Copy.ai

Another example of a company positively using ChatGPT is Accenture, a leading global professional services firm. Accenture has developed a tool called ‘Copy.ai’ that uses ChatGPT to help businesses quickly generate high-quality marketing and advertising content. 

By using the technology to automate routine tasks, such as writing product descriptions and creating social media posts, Accenture is helping its clients become more efficient and effective in their marketing efforts.

Closing Thoughts

ChatGPT is a powerful tool that has the potential to help individuals and organisations across different roles to adapt and develop new skills. While some risks are associated with the technology, companies are already using it innovatively to drive positive outcomes. The key is to use it responsibly. 

Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice – it is provided for educational purposes only. By using this, you agree that the information does not constitute any investment or financial instructions. Do conduct your own research and reach out to financial advisors before making any investment decisions.

The author of this text, Jean Chalopin, is a global business leader with a background encompassing banking, biotech, and entertainment.  Mr. Chalopin is Chairman of Deltec International Group, www.deltec.io. 

The co-author of this text, Robin Trehan, has a bachelor’s degree in economics, a master’s in international business and finance, and an MBA in electronic business.  Mr. Trehan is a Senior VP at Deltec International Group, www.deltec.io. 

The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees.

How Generative Chat AI Operate

Posted on by Jean Chalopin

Generative chat AI is an exciting technology that has been making waves in recent years. It refers to computer programs designed to interact with humans using natural language processing and can generate responses that seem to be coming from a real person. 

These AI systems are capable of analysing and understanding the context of a conversation and can create responses that are not only relevant but also coherent.

According to PitchBook data, generative AI investment rose by 425% between 2020 and December 2022, totalling $2.1 billion last year. This is an especially astounding performance, given a general decline in tech investment in 2022.

This article will dive into technical details that make generative chat AI possible. We’ll explore natural language processing, deep learning, and neural networks and how they are used to train these AI systems. We’ll also touch on some challenges developers face when creating generative chat AI and how they work to overcome them.

What Is Generative Chat AI?

Generative chat AI refers to computer programs that use natural language processing (NLP) to generate human-like responses to a user’s input. These AI systems are designed to interact with humans in a way that feels natural, as if you were chatting with another person. Unlike rule-based chatbots that rely on pre-written responses, generative chat AI is capable of generating new responses on the fly based on the context of the conversation.

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At the heart of generative chat AI is a technology called deep learning, a type of machine learning that involves training neural networks on large amounts of data. By feeding these neural networks with vast amounts of text data, such as chat logs or social media posts, they can learn to generate human-like responses.

The training process involves teaching the neural network to recognise patterns in the data, such as common sentence structures, idioms, and other linguistic features. Once the network has learned these patterns, it can generate new responses that fit within the context of the conversation. The more data the neural network is trained on, the better it becomes at generating natural-sounding responses.

How Does Generative Chat AI Work?

Generative chat AI works by using a combination of natural language processing (NLP) and deep learning, specifically through the use of neural networks. Neural networks are a machine learning algorithm that can recognize patterns in data and learn to make predictions based on that data.

In the case of generative chat AI, the neural network is trained on large amounts of text data, such as chat logs or social media posts. This training process is called deep learning because the neural network has multiple layers of interconnected nodes that allow it to recognize increasingly complex patterns in the data.

During training, the neural network learns to identify linguistic patterns and relationships between words and phrases. For example, it might know that certain words tend to be used together in specific contexts or that certain terms are more likely to occur in response to particular prompts. This training process enables the neural network to generate new responses relevant to the conversation’s context.

Once the neural network has been trained, it can generate responses to user input in real time. When a user inputs a message, the generative chat AI system uses NLP techniques to analyse the text and determine the context of the conversation. Based on this context, the system then uses the trained neural network to generate a relevant and coherent response.

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The success of generative chat AI depends mainly on the training data quality and the neural network’s complexity. Developers must ensure that the training data is diverse and representative of the conversations the system will likely encounter in real-world situations. Additionally, they must design neural networks capable of handling the complexity of natural language and generating accurate and engaging responses.

Use Cases for Generative Chat AI

The future holds many potential use cases for generative chat AI, but there are already a few ways that businesses are making the most of the opportunity. 

Coding

Generative AI can understand user coding requirements in countless languages such as Python, SQL, and Excel formulas. You can ask it to write or debug your code, and the AI returns step-by-step instructions on implementing it. 

Below is a snippet of what the popular Chat GPT platform can provide using a simple question. The more specific the user is with a question, the better the output. 

Copywriting

Users can provide generative chat AI with a topic overview, context and tone. The output is a loose summary that can speed up the copywriting process, allowing humans to focus on the more creative parts. 

Currently, the results are imperfect; see our section on augmenting roles rather than replacing them below. Still, they can make marketing teams far more efficient by giving them a solid starting point. 

Before posting anything written by AI, it is vital to check the accuracy of the information. Outputs are based on the data AI reads, which can be filled with bias and fake content. 

Customer Service

Sales teams can use generative chat AI to sort through all previous customer interactions across all channels (such as web conferences, phone calls, emails, and instant messages) and then direct it to create the next answer.

Consider yourself a salesperson who must react to a client’s query. Imagine how AI could assist you in coming up with the ideal response based on understanding the account history. An article in Wall Street Journal (membership required) talks about some businesses already adopting AI for this purpose. 

Augmenting Roles, Not Replacing Them

One of the key benefits of generative AI is that it can automate routine tasks, allowing humans to focus on more complex and creative work. For example, a chatbot can handle basic customer inquiries, freeing human customer service agents to handle more complex issues requiring empathy and critical thinking.

However, it’s essential to recognize that generative AI cannot replicate human creativity, empathy, and intuition. There will always be tasks and situations requiring a human touch, such as complex problem-solving, creative work, and emotional intelligence.

Moreover, the widespread adoption of generative AI could potentially lead to job displacement and a loss of human jobs. To mitigate this risk, companies should take a responsible approach to AI adoption, ensuring they are using it to augment human capabilities rather than replace them entirely.

In practice, this means that companies should carefully consider how generative AI can be used to complement human work rather than replace it. This might involve retraining employees to work alongside AI, redesigning job roles to take advantage of AI capabilities, or providing opportunities for employees to learn new skills that will be in demand as AI becomes more prevalent.

The Challenges of Generative Chat AI

Generative chat AI faces several challenges. 

The first major challenge is obtaining high-quality training data. Generative chat AI models require large amounts of diverse and representative training data to learn how to generate appropriate responses to various user inputs. However, obtaining such data can be difficult, especially for specialised or niche domains or languages with limited digital content.

Another challenge is ensuring that the AI model does not produce biassed outputs. AI models are trained on data, which may include inherent biases in language use or representation of certain groups or perspectives. If the training data is biassed, the AI model may learn to produce outputs that reinforce or amplify those biases, potentially leading to harmful or discriminatory user interactions.

And Possible Solutions

To address these challenges, it’s important to carefully curate and evaluate the training data used to train the generative chat AI model. This may involve sourcing data from diverse and representative sources, applying quality control measures to filter out biassed or irrelevant data, and using techniques like adversarial training to ensure that the model can handle a variety of inputs and outputs.

Another approach is to evaluate the outputs of the AI model and implement techniques like debiasing or reweighting to mitigate any potential biases. This can involve human oversight and intervention and ongoing monitoring and adjustment to ensure that the model remains fair.

A further challenge is the consistency of generative AI. Users expect a natural and engaging dialogue where responses flow smoothly from one to another and build upon previous messages. However, generative chat AI models may struggle to maintain coherence and consistency, especially when dealing with complex or unpredictable user inputs. 

For example, the model may generate off-topic or irrelevant responses or contradict previous statements made in the conversation. To address this challenge, AI models may require additional training or techniques like attention mechanisms, which can help the model focus on relevant parts of the conversation and generate more coherent responses.

Closing Thoughts

The future of generative chat AI is promising as advancements in natural language processing and machine learning pushes the boundaries of what’s possible. In the coming years, we can expect to see more sophisticated and context-aware AI models capable of engaging in rich and natural conversations with users. 

These models may incorporate advanced techniques like sentiment analysis, emotion detection, and personality modelling, allowing them to tailor their responses to individual users and create more personalised experiences. However, as with any technology, some potential risks and challenges must be addressed, such as maintaining an ethical and responsible use of AI, ensuring transparency and accountability, and addressing potential biases in the data used to train these systems.

Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice – it is provided for educational purposes only. By using this, you agree that the information does not constitute any investment or financial instructions. Do conduct your own research and reach out to financial advisors before making any investment decisions.

The author of this text, Jean Chalopin, is a global business leader with a background encompassing banking, biotech, and entertainment. Mr. Chalopin is Chairman of Deltec International Group, www.deltec.io. 

The co-author of this text, Robin Trehan, has a bachelor’s degree in economics, a master’s in international business and finance, and an MBA in electronic business. Mr. Trehan is a Senior VP at Deltec International Group, www.deltec.io. 

The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees.

Ethereum Is a Base Layer of Computing

Posted on by Jean Chalopin

Ethereum has maintained a strong position as the second-largest cryptocurrency by market capitalisation after Bitcoin. However, it has gained widespread recognition for its unique technology, smart contracts, and the decentralized applications (dApps).

Ethereum goes far beyond the general abilities of first-generation cryptocurrencies. Since its launch in 2015, Ethereum has grown to become much more than just a digital currency. It has positioned itself as a base layer of computing, serving as the foundation for a variety of applications that rely on its unique abilities combined with its robust and secure infrastructure.

Ethereum’s Benefit, Smart Contracts, and the EVM

At its core, Ethereum, like its big brother Bitcoin, is a decentralised, open-source blockchain platform. However, Ethereum also enables developers to build decentralised applications. 

dApps are possible with Ethereum, because it offers a programmable platform that allows developers to create smart contracts, which are self-executing contracts with the terms of the agreement between two or more parties (usually a buyer and seller) being directly written into lines of code. These smart contracts are executed on the Ethereum Virtual Machine (EVM), a decentralised runtime environment that can execute code on the blockchain.

Smart Contract Basics

Ethereum’s smart contracts are built on its blockchain technology, which is a decentralised, secure, and transparent ledger that records all transactions and interactions on the network.  Smart contracts are designed to enable, verify, and enforce the negotiation or performance of a contract without the need for intermediaries such as banks, lawyers, or notaries, nor their escrow accounts. While there are similar blockchains, Ethereum is the most popular blockchain for smart contracts.  

A smart contract’s encoded terms, stored on a blockchain, are then executed by the blockchain when certain conditions, known as ‘triggering events’, are met. Most of this code is a combination of simple ‘if-then’ statements. For example, in a simple, smart contract for a vending machine, the triggering event is the insertion of the coin: ‘if a coin is inserted’, ‘then’ the trigger releases the treat.

One benefit of blockchain-based smart contracts is that they are immutable, meaning the contract cannot be changed once deployed on the blockchain. Immutability ensures that the terms of the agreement are always executed as written, without the risk of tampering or fraud.  It also means that a smart contract must be carefully crafted before it is deployed.

The Ethereum Virtual Machine

The Ethereum Virtual Machine (EVM) is a key component of the Ethereum blockchain. It is a virtual machine responsible for executing Ethereum’s smart contracts and recording transactions on the blockchain.  

The EVM is a software environment that allows developers to write smart contracts in high-level programming languages, the most common is Solidity (Ethereum’s native smart contract programming language), and then compile them into bytecode that can be executed on the Ethereum network. The EVM is designed to be platform-independent, meaning that smart contracts can be executed on any device that is running an Ethereum node.

The EVM’s primary benefit is that it allows for the creation of dApps that can run on the Ethereum blockchain. These dApps can provide a wide range of services, such as digital identity, voting systems, supply chain management, and decentralised finance, discussed further below.  

Because the EVM is a decentralised platform, these services can be provided without the need for intermediaries or centralised authorities, helping reduce costs and increase the transparency of these functions.

The Ethereum Virtual Machine Architecture and Execution Context Courtesy of github

Another benefit of the EVM is that it provides a high level of security for smart contracts.  Smart contracts are executed on the EVM in a sandboxed environment, which means that they are isolated from the rest of the network and cannot access any external resources without explicit permission. This can help to prevent hacks and other security breaches that can occur in traditional software environments.

Ethereum’s Power

While built from simple triggering events, smart contracts can be used for a wide range of applications. This versatility has made Ethereum a popular choice among developers looking to build decentralised applications. Let’s look at a few of the ways these dApps are being utilised.

Digital Identity and Voting Systems

Ethereum is being used for digital identity and voting systems by providing a secure and transparent way to verify and authenticate users. In a digital identity system, an Ethereum smart contract can be used to store and manage user identities, including their personal information and verification documents.  

This system can help to prevent identity theft and fraud by ensuring that only authorised users can access the system. In a voting system, smart contracts can be used first to confirm a voter’s identity and then to ensure that votes are recorded and counted accurately while maintaining the anonymity of the voters.  

This two-tiered system can help increase the voting process’s transparency and integrity while reducing the risk of tampering or fraud. Overall, using smart contracts for digital identity and voting systems can increase security, transparency, and trust in these critical areas.

Supply Chain Management

Ethereum’s blockchain technology has also found its way into the world of supply chain management. By using smart contracts, businesses can create a decentralised, tamper-proof ledger that records every step of the supply chain by enabling the creation of decentralised supply chain solutions that utilise blockchain technology and smart contracts.  

These solutions, known as blockchain supply chain solutions, can provide a high level of transparency and security in the supply chain, by allowing all parties involved (businesses and consumers) to track and verify the movement of goods and information in real-time from the origin to the final destination. 

Smart contracts can be used to automate many of the processes in the supply chain, such as payment processing, quality control, and inventory management, which can reduce the risk of errors and fraud. Additionally, blockchain technology can help reduce the risk of counterfeiting and ensure the authenticity of products, which is particularly important in industries such as pharmaceuticals and luxury goods. 

Overall, Ethereum’s impact on supply chain management has the potential to increase efficiency, reduce costs, and improve the overall transparency and security of the supply chain.

Gaming

Another area where Ethereum is making significant inroads is in the field of gaming. 

Using smart contracts, game developers can create provably fair decentralised games that use blockchain technology, offering transparent and auditable gameplay. These blockchain games, are built on the Ethereum blockchain and allow players to own, trade, and sell in-game items and currency as digital assets.  

This system creates a new level of ownership and control for players, allowing for intermediary-less decentralised transactions, and creating a new form of digital economy. Blockchain games also have the potential to reduce the risk of fraud and hacking, as all transactions are recorded on the blockchain and cannot be altered.  

Additionally, blockchain games can provide players with a new level of transparency and fairness, as using smart contracts can ensure that the game rules and rewards are enforced without central authorities.  

Overall, Ethereum’s impact on gaming has opened up new possibilities for player ownership, security, and fairness, and has the potential to revolutionise the gaming industry as we know it.

Decentralised Finance

Ethereum’s impact on Decentralised Finance (DeFi) has been immense, as it has become the primary blockchain used for DeFi applications. DeFi refers to a new generation of financial services that operate on a decentralised, blockchain-based platform. 

These services include lending, borrowing, trading, and investing and are built using smart contracts that automate many of the processes involved in traditional finance. By eliminating intermediaries and providing a more transparent and secure platform, DeFi has the potential to democratise access to financial services and provide more opportunities for people to participate in the global financial system without relying on traditional financial institutions.  

Ethereum’s programmable blockchain has enabled the creation of a wide range of DeFi applications, such as decentralised exchanges (DEXs), stablecoins, lending platforms, prediction markets, and yield farming platforms. The use of Ethereum’s native token, Ether (ETH), has also become a key component of the DeFi ecosystem, as it is used as collateral for loans and as a means of exchange on many DeFi platforms.  

Overall, Ethereum’s impact on DeFi has opened up new opportunities for financial innovation and has the potential to disrupt traditional finance in a significant way.

DAOs

In recent years, Ethereum has also been used to create decentralised autonomous organisations (DAOs), which are organisations that are run by code rather than a central authority.  

DAOs are governed by a set of rules encoded in smart contracts, and decisions are made through a decentralised voting system. This creates a new form of organisational structure that is transparent, efficient, and free from centralised control.  

The options that DAOs bring are far-reaching and make the democratisation of many organisations and groups that were once impossible, possible.

Closing Thoughts

Ethereum has become a base layer of computing, serving as the foundation for a variety of applications that rely on its robust and secure infrastructure. 

Its ability to support a vast array of use cases, from DeFi and gaming to supply chain management and DAOs, has made it a popular choice among developers looking to build decentralised applications. As blockchain technology continues to mature and become more widely adopted, Ethereum is well-positioned to play a leading role in the decentralisation of various industries.

With the switch to a Proof of Stake (POS) consensus mechanism, the entire Ethereum blockchain system is becoming faster and more energy efficient, allowing more users to participate in the infrastructure. This combination makes Ethereum more competitive and enables it to retain its status as a computing base layer. 

Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice – it is provided for educational purposes only. By using this, you agree that the information does not constitute any investment or financial instructions. Do conduct your own research and reach out to financial advisors before making any investment decisions.

The author of this text, Jean Chalopin, is a global business leader with a background encompassing banking, biotech, and entertainment.  Mr. Chalopin is Chairman of Deltec International Group, www.deltec.io.

The co-author of this text, Robin Trehan, has a bachelor’s degree in economics, a master’s in international business and finance, and an MBA in electronic business.  Mr. Trehan is a Senior VP at Deltec International Group, www.deltec.io.

The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees.

Blockchain and AI

Posted on by Jean Chalopin

According to a report by Allied Market Research, the global blockchain technology market was valued at $3 billion in 2020 and is expected to grow to $39.7 billion by 2025. Similarly, the AI market is projected to grow to $190 billion by 2025, according to a report by MarketsandMarkets. 

With the increasing demand for both blockchain and AI, combining these technologies can revolutionise many industries and transform the way we do business.

What Is Blockchain?

Blockchain technology is a decentralised, distributed ledger that allows for secure and transparent transactions without intermediaries. It was first introduced in 2008 by an unknown individual or group of individuals under the pseudonym Satoshi Nakamoto to facilitate Bitcoin transactions. 

The technology works by recording transactions in blocks linked together to form a chain, hence the name ‘blockchain’. Each block contains a cryptographic hash of the previous block, ensuring the chain’s integrity.

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The benefits of blockchain technology include increased security, transparency, and efficiency. By eliminating the need for intermediaries, such as banks, transactions can be completed faster and at a lower cost. The technology’s decentralised nature also makes it more resistant to fraud and hacking. Blockchain is used in various industries, including finance, healthcare, and supply chain management.

What Is AI?

AI, or artificial intelligence, refers to the ability of machines to perform tasks that would typically require human intelligence, such as learning, reasoning, and problem-solving. The history of AI traces back to the 1950s when researchers first began developing algorithms for machine learning. Since then, AI has evolved to include many technologies, including neural networks, natural language processing, and computer vision.

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AI has rapidly transformed the finance industry by providing faster, more accurate decision-making capabilities and improving operational efficiency. Some examples of how AI is being used in finance include:

  • Fraud detection: AI-powered fraud detection systems use machine learning algorithms to identify unusual behaviour patterns and detect fraudulent activities. 
  • Trading and investment: AI-powered trading algorithms use natural language processing (NLP) to analyse news articles, social media, and other data sources to identify patterns and predict market movements. 
  • Customer service: Financial institutions use chatbots and virtual assistants to provide customer service and support. 

Financial firms worldwide are increasingly turning to artificial intelligence (AI) technologies to improve their efficiency, automate their processes, and provide better customer service. Three examples of financial firms that have successfully adopted AI are Capital One, Citigroup, and Ping An.

Capital One, a US-based financial institution, has implemented natural language processing (NLP) to enhance customer service. Its virtual assistant, Eno, can understand and respond to customer inquiries in natural language, available via the company’s mobile app, website, and text messages. The system has helped Capital One reduce wait times and enhance customer satisfaction. The company has also used machine learning to detect and prevent fraudulent activity.

Citigroup, a multinational investment bank, has been utilising computer vision to analyse financial data. Its research team has developed an AI-powered platform to analyse financial statements and other data to identify patterns and trends. 

The platform can also provide predictive insights, assisting investors in making well-informed decisions. The system has improved Citigroup’s research capabilities and enabled the company to provide superior investment advice to its clients.

Ping An, a Chinese insurance and financial services company, has been using machine learning to improve its risk management. Its risk management platform, OneConnect, can analyse large amounts of data to identify potential risks and provide real-time insights. 

The system can also offer tailored risk assessments for different types of businesses. OneConnect has assisted Ping An in reducing its risk and enhancing its operational efficiency.

Financial firms are increasingly adopting AI technologies to remain competitive and enhance customer service. By leveraging NLP, computer vision, and machine learning, financial institutions can streamline operations, improve customer service, and make informed decisions. Firms that fail to embrace these technologies may risk falling behind their competitors.

Why AI and Blockchain Must Work Together

AI and blockchain are two of the financial services industry’s most innovative and disruptive technologies. While they are often seen as separate technologies, AI and blockchain are becoming increasingly interdependent for several reasons. 

One of the most significant advantages of blockchain is its ability to provide secure, transparent, and tamper-proof transactions. However, blockchain cannot detect fraud, which is where AI comes in. 

By integrating AI and blockchain, financial firms can build more secure and transparent systems that leverage AI’s fraud detection capabilities to enhance the trustworthiness of blockchain. This combination can offer improved security and transparency in transactions, which is crucial in financial services. 

Another advantage of integrating AI and blockchain is the improved accuracy and efficiency of financial services. Smart contracts built on blockchain can automate financial transactions and self-execute when predefined conditions are met. By integrating AI, smart contracts can also be made more intelligent and capable of automatically adjusting to changing conditions. This integration can lead to the creation of more efficient and accurate financial systems.

Integrating AI into the blockchain can also help financial firms to detect and mitigate risks more quickly and effectively. AI can analyse vast amounts of data in real-time, making it an ideal tool for risk management. For example, AI can identify anomalies in financial transactions and flag them for review or rejection, making detecting fraud and other risks easier. This benefit can lead to better risk management, an essential component of financial services.

The integration of AI and blockchain can also help financial firms to comply with regulations more effectively. Financial rules are complex and ever evolving, making compliance a significant challenge for financial firms. By combining AI and blockchain, financial firms can improve their ability to comply with regulations and reduce the costs and risks associated with non-compliance. For example, blockchain can provide an immutable record of transactions, while AI can be used to analyse the data and ensure that it complies with regulations.

AI Creates New Business Models

Finally, integrating AI and blockchain opens up new business models and opportunities for financial firms. Decentralised finance (DeFi) applications are leveraging AI and blockchain to create new financial products and services that are more efficient, accessible, and affordable than traditional financial services. The combination of AI and blockchain technology creates new opportunities for financial firms, leading to the development of new financial products and services that were not possible before. 

In practice, many examples of financial firms are already successfully leveraging AI and blockchain to enhance their services. For instance, Ripple, a blockchain-based payments solution, has integrated AI to improve its fraud detection and risk management capabilities. JPMorgan Chase is using blockchain to develop a decentralised platform for tokenising gold, and AI is being used to analyse the data generated by the platform. Visa also leverages blockchain and AI to enhance its fraud detection and prevention capabilities.

AI and blockchain can transform financial services, enhancing security, transparency, accuracy, efficiency, risk management, compliance, and new business models. By working together, AI and blockchain can create synergies that make them greater than the sum of their parts. Financial firms embracing AI and blockchain are likely better positioned to succeed in an increasingly competitive and complex financial services landscape.

Closing Thoughts

The future of AI-enabled blockchain in financial services is promising, with significant advancements expected in the next decade. Here are some potential developments:

  • Financial firms will continue integrating AI and blockchain to improve their operations, increase efficiency, and reduce costs. 
  • By combining AI’s ability to analyse data with blockchain’s secure and transparent ledger, financial firms can develop systems that provide more secure and private transactions.
  • Decentralised finance (DeFi) applications are already leveraging AI and blockchain to create new financial products and services. 
  • As AI and blockchain become more integrated into financial services, regulatory oversight will increase. 
  • Integrating AI and blockchain will likely create new business models and revenue streams for financial firms. 

Overall, the future of AI-enabled blockchain in financial services looks bright, with continued growth and development expected in the next decade. As financial firms increasingly adopt and integrate these technologies, we can expect to see significant advancements in efficiency and security as new business opportunities emerge. 

Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice – it is provided for educational purposes only. By using this, you agree that the information does not constitute any investment or financial instructions. Do conduct your own research and reach out to financial advisors before making any investment decisions.

The author of this text, Jean Chalopin, is a global business leader with a background encompassing banking, biotech, and entertainment. Mr. Chalopin is Chairman of Deltec International Group, www.deltec.io. 

The co-author of this text, Robin Trehan, has a bachelor’s degree in economics, a master’s in international business and finance, and an MBA in electronic business. Mr. Trehan is a Senior VP at Deltec International Group, www.deltec.io. 

The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees.

What Is Generative AI?

Posted on by Jean Chalopin

Generative AI is a rapidly developing field of artificial intelligence that has been making waves in recent years. Using advanced algorithms, generative AI can create original and often impressive content, such as images, music, and even text, without direct human input. 

This article will delve deeper into generative AI, exploring what it is, how it works, and its potential uses.

Understanding Generative AI

Unlike other types of AI designed to complete specific tasks, such as image recognition or language translation, generative AI is programmed to learn from existing data and generate new content based on that information. 

The key to this process is the use of deep neural networks, designed to simulate how the human brain works, allowing the AI system to learn from patterns and generate new content.

Source

One of the most impressive aspects of generative AI is its ability to create content that is often difficult to distinguish from something a human would produce. For example, generative AI can be used to create realistic images of people who don’t exist or to generate music that sounds like it was composed by a human musician. The image below is AI-generated and not of a real person.

Source

This has exciting implications for various industries, from art and entertainment to marketing and advertising.

Against Other Forms of AI

Generative AI is distinct from other forms because it is designed to create something new rather than simply perform a specific task. This contrasts with different types of AI, such as supervised learning or reinforcement learning, which are focused on solving a particular problem.

For example, supervised learning algorithms are commonly used in image recognition software to identify and classify objects within a given image. In contrast, generative AI can be used to create original ideas, such as realistic portraits of people who don’t exist or entirely new landscapes that have never been seen before.

Another example of a different type of AI is natural language processing (NLP), which is used to analyse and understand human language. While NLP can generate text, it is typically focused on tasks such as language translation or sentiment analysis. In contrast, generative AI can be used to create entirely new pieces of text, such as short stories, poetry, or even news articles.

Most of the AI we see today is still based on machine learning, which involves training a model on a large dataset to identify patterns and make predictions. This is done by feeding the machine learning algorithm a set of labelled data, allowing the system to learn from the data and identify patterns that can be used to make predictions on new, unseen data. 

While machine learning has already had a significant impact on many industries, from healthcare to finance to transportation, the ability to create entirely new content has the potential to revolutionise these fields completely.

Ultimately, the critical difference between generative AI and other types of AI is the focus on creativity and originality. 

The Benefits of Generative AI

Generative AI is a rapidly developing field with numerous potential benefits.

One industry that could improve significantly from generative AI is fashion. With the ability to generate unique designs and patterns, it has the potential to transform the fashion industry. Designers can use it to create new designs, allowing them to produce unique and eye-catching pieces that stand out from the competition. By using it, designers can also save time and resources, allowing them to focus on other aspects of the creative process.

A second industry that stands to gain is gaming. With the ability to generate unique characters, landscapes, and environments, it has the potential to revolutionise the gaming industry. Game designers can use it to create original game elements that are unique and engaging for players. It enables game designers to save time and resources, allowing them to focus on other aspects of the game development process.

Finally, generative AI has the potential to shift the healthcare industry. Using it, researchers can create new drugs and treatments, allowing them to treat diseases and illnesses. It can also be used to analyse medical images and data, allowing doctors and researchers to diagnose and treat patients more accurately. With its ability to create new content and analyse large amounts of data, generative AI can potentially transform how we approach healthcare.

Successful Case Studies

Several companies are already using generative AI to great effect in their applications. Here are a few examples:

Adobe is using generative AI to develop new tools and features for its Creative Cloud suite of products. For example, Adobe’s Sensei platform uses generative AI to analyse images and suggest improvements. The company has also used it to develop new fonts and predict which colours will be popular in the coming year.

OpenAI is a research organisation focused on advancing AI safely and responsibly. The company has developed several generative AI models, including GPT-3, a language model that can generate text that is often difficult to distinguish from something a human would write. GPT-3 has many potential applications, from natural language processing to chatbots. The revolutionary Chat GPT platform is based on these models.

IBM uses generative AI to develop new solutions for various industries, including healthcare and finance. For example, the company has developed a system to analyse medical images and provide more accurate diagnoses. It has also used it to create new financial risk models.

Nvidia is a leading provider of graphics processing units (GPUs) that are used in various applications, including gaming, scientific research, and machine learning. The company is also investing heavily in generative AI and has developed several models that can generate realistic images and even entire virtual environments.

These companies are just a few examples of how generative AI is already being used to create new opportunities and drive innovation in several industries. As the technology develops, it will be interesting to see how it is integrated into even more applications and use cases.

The Risks

While generative AI has enormous potential, several risks are also associated with the technology. One of the most significant risks is its potential to be used for malicious purposes. 

For example, it can be used to create realistic-looking fake images, videos, and audio, which can be used for deception or propaganda. In the wrong hands, these tools could be used to manipulate public opinion, create fake news, or even commit fraud. 

Another risk of generative AI is its potential to perpetuate biases and inequalities. Its models are only as good as the data they are trained on, and if the data is biassed, then the model will be biassed as well. 

For example, a generative AI model trained on predominantly white and male data may be more likely to generate images and text biassed against women and people of colour. This can perpetuate existing inequalities and reinforce harmful stereotypes.

In one study published in 2018, researchers found that several leading facial recognition algorithms were significantly less accurate at identifying the faces of people with darker skin tones, particularly women. This bias was pervasive across multiple algorithms from different companies. The researchers attributed it to the fact that the training datasets used to develop the algorithms were overwhelmingly white and male.

A third risk of generative AI is its potential for cyberattack use. For example, generative AI can generate realistic-looking phishing emails, which can trick people into giving up sensitive information or clicking on links that download malware onto their devices. Additionally, generative AI can generate realistic-looking social media profiles, which can be used for impersonation or other online attacks.

Overall, while it has enormous potential for positive applications, it is vital to be aware of the risks associated with the technology. As the technology continues to develop, it will be necessary for developers and users of generative AI to take steps to mitigate these risks and ensure that the technology is being used responsibly and ethically. This will require ongoing research, development, collaboration, and coordination among stakeholders in various industries.

Closing Thoughts

Generative AI has made tremendous progress in recent years, and there is no doubt that the technology will continue to evolve and improve in the coming decade. One of the most promising areas of development for generative AI is in the realm of creative applications. For example, generative AI is already being used to generate music, art, and even entire literature. As technology advances, we can expect to see more creative works generated by AI and even collaborations between human and machine artists.

Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice – it is provided for educational purposes only. By using this, you agree that the information does not constitute any investment or financial instructions. Do conduct your own research and reach out to financial advisors before making any investment decisions.

The author of this text, Jean Chalopin, is a global business leader with a background encompassing banking, biotech, and entertainment. Mr. Chalopin is Chairman of Deltec International Group, www.deltec.io. 

The co-author of this text, Robin Trehan, has a bachelor’s degree in economics, a master’s in international business and finance, and an MBA in electronic business. Mr. Trehan is a Senior VP at Deltec International Group, www.deltec.io. 

The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees.

The 60-40 Portfolio: What Went Wrong

Posted on by admin

For decades called the ‘balanced’ portfolio, the 60-40 portfolio allocation of 60% to stocks and 40% to bonds served as the quintessential go-to for stable growth, income, diversification, and inflation protection. It worked wonders in the decades leading to the dot-com bubble, known as one of the worst financial events for now-seasoned investors. 

Yet the 60-40 portfolio persists even after the Global Financial Crisis despite a 2022 negative return of -18%, reminiscent of the -22% crash of 1937. What’s next for 2023? 

This article delves into the pitfalls of the hallmark 60-40 portfolio, what went wrong, and what we can do moving forward into the post-pandemic era of high rates and high inflation. 

What Is a 60-40 Portfolio? 

Again, the 60-40 portfolio is an industry-standard investment strategy that allocates 60% of the portfolio to stocks and 40% to bonds. This asset allocation is based on the idea that stocks have the potential to generate higher returns over time but also carry higher risk and volatility relative to bonds. 

Stocks, in general, remain closely tied to the overall health of the economy. Periods of low rates elevated consumer sentiments, and increasing supply orders in a smoothly functioning supply chain–suggesting expected demand–tend to bode well for stocks. 

Stocks, therefore, represent the ‘growth’ we want in a portfolio and can perform well if timed well with an increasing trend or theme. For example, electric vehicles and climate change represent two persistent investment themes despite the Covid-19 pandemic.

Bonds represent the base ‘income’ necessary for a more conservative investor, such as an individual saving for retirement or for a child’s college plan. In theory, it all works well on paper so long as the two asset classes are uncorrelated. 

Why Correlation Matters

Should their correlation turn positive, which was the case for 2022, then bonds present an inordinate amount of risk for an insufficient amount of return. In other words, the risk-reward ratio initially used to create your portfolio is now out of balance. 

Bonds follow the negative trajectory of equities typically when rates are rising on the back of high inflation, supply chain disruptions, or an exogenous event, such as a global pandemic. The usual ‘flight’ to bonds that would, in theory, alleviate the damage done by a 60% allocation to stocks does not occur as institutional investors quickly foresee the incoming damage owing to duration. 

How Duration Is Harmful

Bond duration showcases any one bond’s sensitivity to changes in interest rates, often the Fed’s policy rate. In technical terms, it represents the weighted average time until the bond’s cash flows (inclusive of coupon payments and principal) are received by the investor. 

When interest rates rise, the value of a bond falls. The opposite remains true. Think of it this way: If we can receive a better deal from a more recent bond, then surely the old bond is worth less–and so it is. Bond duration demonstrates this price change. 

For example, a bond duration of 10 years means that this bond’s price will change by 10% for every 1% change in interest rates. If rates rise by 1%, then we can expect to see a 10% price fall. The effect dampens the higher the bond’s yield, but it’s an established rule of thumb we cannot ignore. 

How Active Monetary Policy Ruins ‘60-40’

January 1980 began with a Fed funds rate of 14%. You read that correctly. In order to combat inflation of also 14%, the Federal Reserve manufactured a recession on its own accord. It threw an ice bucket of water onto an overheating economy. 

This set the precedence for constant market manipulation for the following decades to come up to today. It’s the belief in a ‘soft landing’ towards recession or a ‘quick recovery’ following a disaster that keeps central banks worldwide motivated. 

And they have reason to believe–it does work to some extent. For example, many financial executives cite 1994’s soft landing by Alan Greenspan. However, there is the valid argument that it all came down to luck. 

What central bank activity certainly does is inject volatility into an asset class chosen to buffer against the very same thing–volatility–by creating expected cash flows. Again, the concept works fine in theory as long as the sources of volatility are uncorrelated between stocks and bonds. 

When they derive from the same source, such as a central bank, then asset classes themselves become correlated–leading to a type of 100% either-way portfolio. 

Return to Supply and Demand

The global pandemic introduced rampant inflation in 2022 as supply chains buckled under the weight of austere government policies, afraid of what it might be like to repeat 1918’s Spanish flu in a hyperconnected world. Dry bulk shipping prices skyrocketed as the white collar world came to add a new word to our growing dictionary, ‘hybrid working’. 

In late 2021, the lag between event and aftermath led to the transitory inflation debacle in which the economic severity of a global pandemic was woefully under-expected and under-stated. 

When inflation hit due to rising supply costs passed onto consumers, the Fed following 1994’s example, decided to raise rates as rumblings of recession began in late 2022. Consumer spending fell as duration ensured a dark path ahead for bonds. 

If the demand for the products of large companies remained robust, then the 60-40 portfolio would have fared better. However, that demand fell in 2022, such as with Apple’s annual iPhone release. 

Long-term investors then, knowing that a 60-40 portfolio depends on a healthy ‘bull market’, look to a source of healthy demand despite rising inflation and rising rates. 

Alternative Portfolios

Yale’s endowment fund serves as a great model for investing in alternative assets and bucking the 60-40 portfolio. In its year ending June 2022, it earned a return of 1%, well exceeding S&P 500’s -16% loss. In the year ending June 2021, it earned a return of 40%. 

Yale publicly documents its gradual veering away from traditional asset classes and how it favours the alternative. Leveraged buyouts, venture capital investments, and absolute return strategies form its three greatest allocations. Domestic equity, on the other hand, remains minuscule. 

And Yale is not alone. Kansas State and the University of Michigan also represent top performers in one of the worst years for retail investors. It boils down to how endowment portfolios think differently to the standard 60-40. 

They operate truly in the long term and seek to diversify through alternative strategies, such as commodities, hedge funds, and private real estate, often approaching a portfolio like a long-term legacy plan.

Wealth Planning 

As opposed to wealth management, wealth planning pays specific attention to long-term goals, incorporating a holistic view of an investor’s entire estate and how they might be able to invest through alternative methods. 

2023 marks the year of wealth planning. Inflation and correlated volatility highlight the weaknesses of the 60-40 portfolio. Endowment funds and a true long-term perspective showcase the inflation-stopping and jaw-dropping power of what it means to diversify amongst different sources of demand growth. 

Disclaimer: The author of this text, Paul Winder, has a career that spans over 30 years in the financial services sector with emphasis on creating products and services in the international tax treaty and estate planning arena. Paul is Head of Fiduciary Products & Markets at Deltec Bank & Trust and CEO of Deltec Fund Services, www.deltec.io.

The co-author of this text, Conor Scott, CFA, has been active in the wealth management industry since 2011. Mr. Scott is a Writer for Deltec International Group, www.deltec.io.

The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees. This information should not be interpreted as an endorsement of cryptocurrency or any specific provider, service, or offering. It is not a recommendation to trade. 

The Financial Planning Process

Posted on by admin

Financial planning or wealth planning is the process of creating an achievable roadmap for your life’s goals. The financial planning process involves several intensive steps, such as: 

  1. Defining your financial goals
  2. Assessing your current financial situation
  3. Developing a realisable plan of action
  4. Implementing that plan
  5. Monitoring and adjusting 

Whether saving for retirement, paying off debt, or planning for a major purchase or life event, tailored financial planning means the difference between success and failure. The financial planning process itself must be well-structured, thorough, and comprehensive. 

After painting a complete picture of your finances, the capable advisor defines the primary realisable goals relevant to you and how to achieve them. Of course, the ultimate goal remains long-term financial stability and success through the well-ordered mind. 

This article explores the five essential steps that are the foundation for a well-ordered financial planning process. In the greater scope of wealth management, financial planning continues to be a complex speciality requiring both experience and compassion. 

Source: finexplained

Defining Your Financial Goals 

As the critical first step of the financial planning process, it helps you clarify what you want, what’s achievable, and what you want to achieve in the long term. Further, it provides a path forward. There are five considerations to keep in mind. 

Short- vs long-term. We must identify whether your goals are short-term (less than one year), medium-term (one to five years), or long-term (more than five years). This aids goal prioritisation and the relevance level of available strategies. 

Measurable. After time durations are determined, we examine the specific numbers to which we must hold ourselves accountable. This lets us see where we could have met specific objectives. 

Realistic. Ambitious goals come with high risk, while realisable goals enable us to moderate that risk, especially over longer durations. Considering the current income stream, we can identify any weak points and define strategies for remedying them. 

Prioritised. An essential question inside any financial planning process: what can we do without, and what is imperative? Pre-paying school fees for possible tax benefits is a high-priority item, while an additional car is not. 

Value-aligned. Your values and priorities dictate how you spend your disposable wealth. Otherwise, why hire a financial planner? Your passions and beliefs should enter many of your financial and life goals.

Assessing Your Current Financial Situation

The second step of the financial planning process, it provides an essential baseline for evaluating your forward progress and the necessary plan of action. 

Income. We must evaluate the varied sources of income and their levels of consistency. For example, salary payments versus sporadic rental income. Then we factor in taxes, deductions, and all matters relevant to your legal jurisdictions. 

Assets. We then need to review the total value of your assets, including savings accounts, retirement accounts, investment portfolios, private funds, real estate, and other sources. Not only does accurately understanding your net worth open up new doors, but it guides the timeline for realising more significant goals. 

Liabilities. In short, we must ensure that all unnecessary liabilities are handled with the utmost care and urgency. While more time may be needed for property or loan balances, removing minor matters immediately improves your financial momentum and well-being. 

Cash flow. In the final but essential portion, we need to determine the current cash flow picture and how it can be adjusted to meet your financial goals. This is one area where experience and financial acumen becomes critical. 

Developing a Realisable Plan of Action

The third step of the financial planning process, developing a realisable action plan, entails producing a concrete strategy for achieving your financial goals. 

Set targets. After setting your financial goals through to bequests and the next generations of your family, set your smaller, achievable targets. The overall goal is to know how one achievement feeds into the next. 

Identify obstacles. We’re all familiar with the timeless maxim: life happens. So what are the expected and possibly unexpected obstacles you might face in your journey? Your advisor must account for these and structure finances accordingly. 

Choose the right strategies. Yes, easier said than done, but this is the substance of any worthwhile financial plan. What are the vital commitments? What are the appropriate structures? How many generations are in the family? Dozens of questions comprise this point. 

Monitor your progress. Some ideas feel good in the mind or work until the market or the Fed takes a turn for the worse. Your advisor must always be reachable in the event changes are needed. 

Implementing Your Financial Plan

The final step of the initial financial planning process, implementing your plan, must be done carefully and guided by experience. Just as timing investments significantly impacts returns, time also impacts long-term financial plans. 

Automate your savings. As an essential “Rich Dad Poor Dad” technique, define your monthly portfolio contribution before spending your regular income. This not only brings mental well-being and confidence, but ensures that your financial plan keeps to your desired goals. 

Stay disciplined. By defining your significant purchases for the next five years with a financial advisor, you can avoid unnecessary expenditures or liabilities while limiting debt exposure. In addition, a worthy financial planner gently reminds you of your long-term ambitions whenever appropriate. 

Remain ready to re-evaluate. This can be negative or positive. If the real estate or cryptocurrency markets take an upswing, then the immediate cash boon should be included if favourable. If events turn unfavourable, then it’s best to prioritise and move forward.

Closing Thoughts

Define, assess, develop, implement, and then monitor. These five steps comprise a great financial planning process. We say: don’t settle for anything less. This is the baseboard, the bare minimum you should expect. 

Financial planning differs from private banking or traditional wealth management because it focuses more on the individual and the long term. It is far more idiosyncratic, considering hopes, fears, desires, and flaws. As personal dreams make the best north star, compassion and an experienced ear make the best financial plan. 

Disclaimer: The author of this text, Paul Winder, has a career that spans over 30 years in the financial services sector with emphasis on creating products and services in the international tax treaty and estate planning arena. Paul is Head of Fiduciary Products & Markets at Deltec Bank & Trust and CEO of Deltec Fund Services, www.deltec.io.

The co-author of this text, Conor Scott, CFA, has been active in the wealth management industry since 2011. Mr. Scott is a Writer for Deltec International Group, www.deltec.io.

The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees. This information should not be interpreted as an endorsement of cryptocurrency or any specific provider, service, or offering. It is not a recommendation to trade. 

What Is a Global Citizen? 

Posted on by Jean Chalopin

The term ‘Global Citizen’ has earned increasing attention in recent years as people worldwide become more aware of our interconnectedness and our actions’ impact on one another.

At its core, being a Global Citizen means recognising that we are all part of a larger global community and that we are responsible for taking actions that promote the well-being of people and the planet, both locally and globally. This includes valuing diversity, respecting human rights, promoting peace and justice, and working to address global challenges such as poverty, inequality, climate change, and other issues that affect people across borders.

Global Citizenship is not limited to nationality, religion, or culture. It’s a mindset and a way of living that emphasises our common humanity and our shared responsibility for the world we live in. It involves actively engaging with others, learning about different perspectives and cultures, and taking action to address social, economic, and environmental issues in our own communities and beyond.

A Global Citizen has the following key traits: 

  1. Awareness: Having a global perspective and understanding of how our actions impact others in different parts of the world.
  2. Empathy: Showing empathy for those who are different from us and being willing to learn from their experiences.
  3. Action: Taking action to address issues that affect people and the planet, such as volunteering, donating to charities, and supporting policies that promote social and environmental justice.
  4. Collaboration: Working with others across different backgrounds, cultures, and sectors to find solutions to complex global challenges.

Being a Global Citizen is about recognising that our actions have consequences that extend past our immediate surroundings. It’s to take responsibility for promoting a more just, equitable, and sustainable world for all people, regardless of where they were born, where they live, and how they identify.

This article delves into these traits shared by Global Citizens worldwide, what they care about today, and examples of what they’ve done with their success. 

A Global Citizen Is Aware

They maintain constant awareness of the interconnected world–that there are expanded ripple effects to our actions. They understand that there are social, economic and environmental issues that affect people in different parts of the world and that it’s essential to be informed about these issues.

For example, a Global Citizen makes a point to learn different cultures and languages and seeks opportunities in line with their passions. They recognise not only the potential to make a difference but to benefit themselves and add to their life while doing so. 

Although renowned late Beatle singer George Harrison passed away two decades ago, he lived ahead of his time and set the definition for a Global Citizen. In 1971, he organised the first benefit concert, The Concert for Bangladesh, which strove to raise awareness and help save the lives of 10 million East Pakistani refugees suffering from disease and starvation. 

Five decades on, this concert remains one of Mr. Harrison’s hallmark achievements and added to the permanent legacy of world-changing music. 

A Global Citizen Is Empathetic

They waste no time in showing empathy for others, especially those who are different from themselves. They recognise that there are many factors that influence a person’s experiences, such as their culture, gender, sexuality, religion, economic status, and race. A Global Citizen seeks to understand these factors and remain open to learning from others.

For example, a Global Citizen might participate in cultural exchange programs, learn a new language, or volunteer to support people in marginalised communities. They are aware that their own experiences are limited and that they can benefit from understanding the life stories of others. 

Wawira Njiru earned the headline’ novice cook to international icon’ for her dedication to transforming Kenyan schoolchildren’s lives through access to food. She launched the group Food for Education which targets explicitly the cycle of poverty and showcases how it can end with a simple, good meal. She has now served over nine million meals. 

A Global Citizen Understands Action

They don’t hesitate when the time comes to stop planning, and start doing. A Global Citizen is someone who takes action to address global challenges, both locally and globally. They recognise that there are many issues facing our world, such as poverty, inequality, climate change, and other environmental issues, and they’re willing to take action to address these challenges. 

For example, a Global Citizen might volunteer for a local charity, donate to a global relief organisation, or advocate for policies that promote social and environmental justice. They recognise that their own actions can make a difference and that collective action is necessary to create positive change.

Hamdi Ulukaya, founder and CEO of Chobani, champions fully-paid parental leave, local food banks, and trustworthy food programs for local schools. Further, he founded the Tent Partnership for Refugees, which seeks to provide hiring, training, and mentorship for refugees. Most are aware of the world’s crises, but it comes down to extending a helping hand. 

A Global Citizen Collaborates

They don’t live on an island, alone, king of a kingdom-of-one. A Global Citizen is someone who works with others to find solutions to complex global challenges. They recognise that no one person, organisation, or nation can solve these challenges alone, and that collaboration across different backgrounds, cultures, and sectors is necessary.

For example, a Global Citizen might participate in community forums, join a global network of activists, or collaborate with organisations to address issues such as poverty, climate change, or social injustice. They recognise that different perspectives and experiences can enrich the solutions we develop, and that working together is necessary to create meaningful change.

Richard Curtis has worked with numerous organisations and projects, such as Comic Relief, Red Nose Day, Projet Everyone, and Make Poverty History. His collaborations have led to the fundraising of over a billion US dollars for the benefit of children and vulnerable people worldwide. 

Closing Thoughts

Awareness, empathy, action, and collaboration–in that order. Global Citizens understand they’re on Earth to contribute, to do something, and help make something great. They utilise their unique talent or profession, be that music, business management, or cooking, and make it the bedrock of an enterprise far greater than themselves. 

Ironically and because of it, they become much more; memorable individuals we read about in magazines or hear of in podcasts. And it doesn’t take $10,000–not even $1,000. Wawira started with a single Kenyan meal, and later became one of the world’s most remarkable success stories under 30 years old. 

By combining a singular passion with a greater focus, entrepreneurs transition from the realm of ‘good’ to ‘great’. It’s this reason that prompted Deltec to launch the Deltec Initiatives Foundation, empowering young Bahamians, and Deltec Cares, a global disaster relief effort. 

Disclaimer: The author of this text, Jean Chalopin, is a global business leader with a background encompassing banking, biotech, and entertainment. Mr. Chalopin is Chairman of Deltec International Group, www.deltec.io.

The co-author of this text, Conor Scott, CFA, has been active in the wealth management industry since 2011. Mr. Scott is a Writer for Deltec International Group, www.deltec.io.

The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees. This information should not be interpreted as an endorsement of cryptocurrency or any specific provider, service, or offering. It is not a recommendation to trade. 

The Uses of Chatbots Like ChatGPT

Posted on by Jean Chalopin

When we used to hear the word “chatbots,” pain often comes to mind. Frustration with the novel was the norm. With chatbots that were mostly able to receive a question and reply, “I am sorry, I can’t answer that. However, I will contact someone that can help you with it. You should receive a reply in 24 hours.” 

Yet, chatbots have come a long way, and the next-generation bots, like the new Chat GPT and those under development by Google, are excellent. They will become a vital part of the customer experience and take the burden of repetitive tasks, simple tasks, and questions away from agents while improving satisfaction results by quickly providing the info clients need.  

Chatbots in Brief

Chatbots had evolved since their inception when programmers wanted to surpass the Turing Test and create artificial intelligence. For example, in 1966, the ELZA program fooled users into thinking they were talking to a human.  

A chatbot is a computer program often using scripts that can interact with humans in a real-time conversation. The chatbot can respond with canned answers, handle different levels of requests (called second and third-tier issues), and can direct users to live agents for specific tasks.  

Chatbots are being used in a wide variety of tasks in several industries. Mainly in customer service applications, routing calls, and gathering information. But other business areas are starting to use them to qualify leads and focus large sales pipelines. 

The first chatbots of over 50 years ago were intended to show the possibilities of AI. In 1988 Rollo Carpenter’s Jaberwacky was designed more for entertainment but could learn new responses instead of relying on canned dialog only. As they progressed, chatbots surpassed “pattern matching” and were learning in real-time with evolutionary algorithms. Facebook’s Messenger chatbot of 2016 had new capabilities and corporate use cases.  

The general format of a Chatbot system takes inputs looking for yes-no answers or keywords to produce a response. But chatbots are evolving to do more comprehensive processes, including natural language processing, neural networks, and other machine learning skills. These chatbots result in increased functionality, enhanced user experiences, and a more human-like conversation that improves customer engagement and satisfaction.  

Benefits of Chatbots

Improved customer service. Clients want rapid and easy resolutions. HubSpot found that 90% of customers want an immediate response to customer service issues. 

This is seen with the increase in live chat, email, phone, and social media interactions. Chatbots can provide service to users 24/7, handling onboarding, support, and other services. Even robo-advisors can use chatbots as a first line of contact. 

More advanced systems can pull from FAQs and other data sources that contain unstructured data like old conversations and documents. Chat GPT uses a massive supply of information up to its 2021 cutoff point.

Improved sales. Chatbots can qualify leads and guide buyers to information and products that fit their needs, producing a personalized experience that builds conversions. For example, they can suggest promotions and discount codes to boost purchase likelihood. They can also be a checkout page aid to reduce cart abandonment. 

Money savings. The goal of chatbot deployment for service and sales support is often to reduce casts. Chatbots can service simple and repetitive tasks allowing human agents to focus on complex issues. 

For example, if a small HR team is slowed with holiday and benefits questions, a chatbot can answer 90% of these, lessening the HR team’s load. An Oracle survey found that chatbots could produce savings of more than half of a business’s upfront costs. While the upfront costs of chatbot implementation are high, the long-term cost savings in staff equipment, wages, and training will outweigh the initial spending.  

Chatbot Implementation Mistakes

While chatbots cannot do everything yet, and it will be a long time before they can do many tasks, they have a skill set that can be used. They can help humans, allowing them to work on more human-required tasks.

No human option. This is a mistake many companies make. Chatbots cannot solve all problems, and the client should have a way to escalate their interaction to a human who can solve it.  

Lacking customer research. A bot needs to know what to look for and what to address. If an implementation starts with the most common and time-consuming questions and decides if a chatbot can solve these, it will prove its value many times over. 

Neglecting tool integration. A well-built chatbot will be part of the contact center platform, aiding agents and supervisors. Able to pull info from multiple sources and escalate to a live agent with useful contextual information allowing the agent to quickly take over from where the chatbot ended.

Use Cases of Chatbots

How can businesses use chatbots? Here are a few examples of great implementations improving customer service and outcomes.  

Retail Banking

Banks or online brokers will generally field simple questions from depositors and borrowers. However, many may come at times of vulnerability. The rising cost of living means a closer focus on finances. Clients may have pending transactions, payments, fraud, or other issues; technology could allow them to monitor these in real time. 

If there is only a call center to address these issues, they will have added pressure. But these can be addressed across multiple channels. A banking chatbot with sentiment analysis can handle text-based digital channels (web chatbot, social media, SMS messaging). 

Launched on the website, mobile app, and social media, this virtual assistant can handle first and second-tier queries (credit card payments, checking account balances). The implementation of sentiment analysis can detect upset customers, quickly getting them to a natural person. 

Chatbots can also aid with the creation of balance alerts, alter other settings, and set up payment reminders, ensuring that both the present issue is solved and the likelihood of a future issue is reduced. 

Property Management

As a commercial and residential real estate business grows, more calls are coming in from customers covering a wide range of issues (rent, maintenance, renovations, and potential customers). As a result, they are taking up the contact center’s resources. For example, a chatbot could answer routine renters’ questions, guide them to self-service solutions, or submit a service ticket. 

Chatbots can also collect info that will allow the direction of their query to relevant categorical information or help from the related agent. This reduces high call volume and becomes a source to produce tickets 24/7, not just when the office is open, providing notifications to the clients when their submissions are updated. Chatbots can also be set for rent reminders via text and provide online payment options to improve on-time payments—a win-win for the user and the company’s bottom line.  

Logistics

Logistics customers want to know where their items are and in real-time. Accurate tracking info is more widely available, but with logistics, there are many variables to contend with on the global level. In addition, high volumes of location requests can overpower a company; even if they are simple requests, they stretch a company’s resources. 

A chatbot can deflect many calls from the call center to automated phone response or web services that have a text chat service, providing callers with a way to track their packages and lowering the strain on the service staff, allowing them to focus on complicated issues.  

Direct-to-Consumer Retail

Online retailers have a lot of spinning plates. Supply chain, warehousing, couriers, drop shippers, and other order fulfillment, and running an E-com site. When one piece fails, there are unhappy customers. If a manufacturer has assembly issues for a hot new product, the company may experience high call volume and service requests, resulting in many refunds and returns. 

An AI-powered chatbot like ChatGPT can be a lifesaver, guiding customers to troubleshooting and instructional media such as video tutorials or the webpage’s knowledge base. It can also take customer feedback and use this information to improve service outcomes, further optimizing flow. 

It can also be helpful in the returns process, streamlining the system, resolving returns without the need for a human team member. In addition, by deflecting most inbound calls to self-service, the call center’s volume is decreased, reducing wait times and producing cost savings. The chatbot could also generate viable leads helping consumers find the right products for their needs while upselling products and services through personalized recommendations.  

Closing Thoughts

All of the use cases for chatbots provided above are currently being employed and are solutions that use chatbots that are less sophisticated than ChatGPT. However, chatbots can provide higher levels of service that can instantaneously scale with a business while doing so at an attractive ROI. 

There are thousands of chatbot implementations possible for today’s businesses, allowing customers to get the real-time service they need with more personalization and specificity than before; this will only continue to improve and expand, allowing more to be provided to consumers.

As chatbots improve their capabilities, their use will likely broaden in scope and volume. Many things humans did in the past, or do now, will be replaced by the faculties of ever-advancing chatbots. These humans will need to be trained to do other work or higher-level service tasks so that we don’t have a glut of out-of-work service personnel. 

On the other hand, this training will result in more satisfying work for employees, which in the long run can improve their lives. Balance is needed to gain further acceptance of chatbots by employees and the populace as a whole.

Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice – it is provided for educational purposes only. By using this, you agree that the information does not constitute any investment or financial instructions. Do conduct your own research and reach out to financial advisors before making any investment decisions.

The author of this text, Jean Chalopin, is a global business leader with a background encompassing banking, biotech, and entertainment. Mr. Chalopin is Chairman of Deltec International Group, www.deltecbank.com.

The co-author of this text, Robin Trehan, has a bachelor’s degree in economics, a master’s in international business and finance, and an MBA in electronic business. Mr. Trehan is a Senior VP at Deltec International Group, www.deltecbank.com.

The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees.

Brain-Computer Interfaces

Posted on by Jean Chalopin

Brain-computer interfaces are devices that allow people to control machines with their thoughts. This technology has been the stuff of science fiction and even children’s games for years. 

Mindflex game by Mattell

On the more advanced level, brain-computer technology remains highly experimental but has vast possibilities. First to mind (no pun intended), would be to aid those with paralysis in creating electrical impulses that would let them regain control of their limbs. Second, the military would like to see its service members operating drones or missiles hands-free on the battlefield.  

There are also concerns raised when a direct connection is made between a machine and the brain. For example, such a connection could give users an unfair advantage, enhancing their physical or cognitive abilities. It also means hackers could steal data related to the user’s brain signals.  

With this article, we explore several opportunities and issues that are related to brain-computer interfaces.  

Why Do Brain-Computer Interfaces Matter?

Brain-computer interfaces allow their users to control machines with their thoughts. Such interfaces can aid people with disabilities, and they can enhance the interactions we have with computers. The current iterations of brain-computer interfaces are primarily experimental, but commercial applications are just beginning to appear. Questions about ethics, security, and equity remain to be addressed. 

What Are Brain-Computer Interfaces? 

A brain-computer interface enables the user to control an external device by way of their brain signals.  A current use of a BCI that has been under development is one that would allow patients with paralysis to spell words on a computer screen. 

Additional use cases include: a spinal cord injury patient regaining control of their upper body limbs, a BCI-controlled wheelchair, or a noninvasive BCI that would control robotic limbs and provide haptic feedback with touch sensations. All of this would allow patients to regain autonomy and independence.

Courtesy of Atom Touch

Beyond the use of BCIs for the disabled, the possibilities for BCIs that augment typical human capabilities are abundant. 

Neurable has taken a different route and has created headphones that can make you more focused, not requiring a user’s touch to control, but can work with a wink or nod and will be combined with VR for a better experience.

Courtesy of Neurable

How do BCIs Work?

Training

Generally, a new BCI user will go through an iterative training process. The user learns how to produce signals that the BCI will recognize, and then the BCI will take those signals and translate them for use by way of a machine learning algorithm. Machine learning is useful for correctly interpreting the user’s signals, as it can also be trained to provide better results for that user over time. 

Connection

BCIs will generally connect to the brain in two ways: through wearable or implanted devices. 

Implanted BCIs are often surgically attached directly to brain tissue, but Synchron has developed a catheter-delivered implant that taps into blood vessels in the chest to capture brain signals. The implants are more suitable for those with severe neuromuscular disorders and physical injuries where the cost-benefit is more favorable. 

A person with paralysis could regain precise control of a limb by using an implanted BCI device attached to specific neurons; any increase in function would be beneficial, but the more accurate, the better.  Implanted BCIs can measure signals directly from the brain, reducing interference from other body tissues. However, most implants will pose other risks, primarily surgical-related like infection and rejection. Some implanted devices can reduce these risks by placing the electrodes on the brain’s surface using a method called electrocorticography or ECoG.  

Courtesy of the Journal of Neurosurgery

Wearable BCIs, on the other hand, generally require a cap containing conductors which measure brain activity detectible on the scalp. The current generation of wearable BCIs is more limited, such as only for augmented and virtual reality, gaming, or controlling an industrial robot. 

Most wearable BCIs are using electroencephalography (EEG) with electrodes contacting the scalp to measure the brain’s electrical activity. A more recent and emerging wearable method incorporates functional near-infrared spectroscopy (fNIRS), where near-infrared light is shined through the skull to measure blood flow which, when interpreted, can indicate information like the user’s intentions. 

OBELAB’s NIRSIT fNIRS system

To enhance their usefulness, researchers are developing BCIs that utilize portable methods for data collection, including wireless EEGs. These advancements allow users to move freely. 

The History of BCIs

Most BCIs are still considered experimental. Researchers began testing wearable BCI tech in the early 1970s, and the first human-implanted BCI was Dobelle’s first prototype, implanted into “Jerry,” a man blinded in adulthood, in 1978. A BCI with 68 electrodes was implanted into Jerry’s visual cortex. The device succeeded in producing phosphenes, the sensation of “seeing” light.  

In the 21st century, BCI research increased significantly, with the publication of thousands of research papers. Among that was Tetraplegic Matt Nagle, who became the first person to control an artificial hand using a BCI in 2005. Nagle was part of Cyberkinetics Neurotechnology’s first nine-month human trial of their BrainGate chipimplant.  

Even with the advances, it is estimated that fewer than 40 people worldwide have implanted BCIs, and all of them are considered experimental. The market is still limited, and projections are that the total market will only reach $5.5 million by 2030. Two significant obstacles to BCI development are that each user generates their own brain signals and those signals are difficult to measure.  

The majority of BCI research has historically focused on biomedical applications, helping those with disabilities from injury, neurological disorder, or stroke. The first BCI device to receive Food and Drug Administration authorization was granted in April 2021. The device (IpsiHand) uses a wireless EEG headset to help stroke patients regain arm and hand control.  

Courtesy of Neurolutions IpsiHand

Concerns With BCI

Legal and security implications of BCIs are the most common concerns held by BCI researchers. Because of the prevalence of cyberattacks already, there is an understandable concern of hacking or malware that could be used to intercept or alter brain signal data stored on a device like a smartphone.

The US Department of Commerce (DoC) is reviewing the security implications of exporting BCI technology. The concern is that foreign adversaries could gain an intelligence or military advantage. The DoC’s decision will affect how BCI technology is used and shared abroad.

Social and Ethical Concerns

Those in the field have also considered BCI’s social and ethical implications. The costs for wearable BCIs can range from hundreds even up to thousands of dollars, and this price would likely mean unequal access. 

Implanted BCIs cost much more. The training process for some types of BCIs is significant and could be a burden on users. It has been suggested that if the translations of BCI signals for speech are inaccurate, then great harm could result. 

The Opportunities of BCIs

The main opportunities that BCIs will initially provide are to help those paralyzed by injury or disorders to regain control of their bodies and communicate. This is already seen in the current research, but in the long term, this is only a steppingstone. 

The augmentation of human capability, be it on the battlefield, in aerospace, or in day-to-day life, is the longer-term goal. BCI robots could also aid humans with hazardous tasks or hazardous environments, such as radioactive materials, underground mining, or explosives removal.  

Finally, the field of brain research can be enhanced with a greater number of BCIs in use. Understanding the brain will be easier with more data, and researchers have even used a BCI to detect the emotions of people in minimally conscious or vegetative states.  

Closing Thoughts

BCIs will provide many who need them a new sense of autonomy and freedom they lack, but several questions remain as the technology progresses. Who will have access, and who will pay for these devices? Is there a need to regulate these devices as they begin to augment human capability, and who will do so? What applications would be considered unethical or controversial?  What steps are needed to mitigate information, privacy, security, and military threats?  

These questions have yet to be definitively answered—and they should be answered before the technology matures. The next step of BCIs will be information transfer in the opposite direction, like with Dobelle’s original light sensing “seeing” BCI of the 1970s, or computers telling humans what they see, think, and feel. This step will bring a whole new set of questions to answer.

Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice – it is provided for educational purposes only. By using this, you agree that the information does not constitute any investment or financial instructions. Do conduct your own research and reach out to financial advisors before making any investment decisions.

The author of this text, Jean Chalopin, is a global business leader with a background encompassing banking, biotech, and entertainment.  Mr. Chalopin is Chairman of Deltec International Group, www.deltecbank.com.

The co-author of this text, Robin Trehan, has a bachelor’s degree in economics, a master’s in international business and finance, and an MBA in electronic business.  Mr. Trehan is a Senior VP at Deltec International Group, www.deltecbank.com.

The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees.

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