• Sat. Jun 22nd, 2024

With the rise and exponential growth of AI models and tools, one would think the world would have become fully automated despite all the hype.

Well, not quite.

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Emerging technologies always have an allure that creates a mass effect.

AI models and tools have existed in one form or the other.

Most people didn’t see them at the time.

The world of finance holds the potential for AI to really take off.

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At least, our experts think so (and then some).

Here’s what they had to say.


Scott Harkey, EVP Financial Services and Payments at Endava

“AI and Machine Learning have been a part of financial services for nearly a decade primarily in the fraud detection, credit decisioning, and authorization space. What we now see is those use cases expanding greatly to include less-complex tasks and business processes. These new use cases range from creating marketing copy and testing customer outreach campaigns to language translation, customer support and knowledge management. Simply put, AI has moved from a technology focused on solving only the most complex data heavy use cases to being a more common tool featured all throughout financial services organizations.”



Eetu Laaksonen,  CTO at Valona Intellignce

“AI has been revolutionary in the finance industry, but companies have found accuracy to be lacking among many AI tools, which, when it comes to finance, can be more than a small problem,” said Eetu Laaksonen, CTO at M-Brain. “By concentrating on tools that provide clear business insights and accurate intelligence, financial entities can feed strategic decision making and prudent risk assessment, enabling their organization to have a resilient journey. This is imperative for the future credibility and success of the finance industry.”


Seth Diener, Private Wealth Manager at Diener Money Management LLC

“One of the most significant impacts of AI in finance is the enhancement of data analysis and predictive capabilities. AI-driven algorithms can swiftly process vast amounts of financial data, identifying patterns and trends that were previously difficult or time-consuming for human analysts to uncover. This has improved risk assessment, fraud detection, and investment strategies, leading to more informed decision-making and reduced financial risks.

Moreover, AI-powered tools have reshaped customer interactions and engagement within the finance sector. Chatbots and virtual assistants are increasingly being employed to provide personalized customer support, answer queries, and offer financial advice, available around the clock. This not only enhances customer experience but also frees up human agents to handle more complex tasks. Additionally, robo-advisors have gained prominence in the investment realm, offering algorithm-based portfolio management tailored to individual goals and risk tolerance. This democratization of investment advice has made financial planning and wealth management accessible to a wider range of investors.

AI’s impact extends to regulatory compliance and fraud prevention as well. Machine learning algorithms can quickly identify unusual patterns in financial transactions, helping banks and financial institutions combat money laundering and fraudulent activities. These tools can analyze historical data to detect anomalies and deviations from expected behavior, alerting authorities to potential issues before they escalate.”


Related: AI Tech Demand Exploded By 500% In 12 Months Boosted By ChatGPT Success


Richard Gardner, CEO at Modulus

“There are a number of ways that AI has, and will continue to, affect the finance industry. For years, we’ve been building algorithms, powered by artificial intelligence and machine learning, to analyze financial data, all in real time. These algorithmic trading strategies allow traders to identify trends which would have otherwise been missed. The same kinds of technologies enable quantitative analysis analysts and offer the ability to make reasoned market predictions.

In the realm of digital assets, there’s a strong use case for AI in fraud detection. In practice, AI can be useful in KYC and AML compliance; however, the implications go far beyond. We have systems which are able to prevent toxic order-flow by providing multiple configurable levels of pre-trade risk management. This includes monitoring for momentum ignition, hammering, spoofing, smoking, quote stuffing, wash trading, and a great deal more, all in real-time. Widespread use of these technologies could prevent market abuse, flash crashes, market manipulation and money laundering. This technology could be critical for the industry as a response to regulator concerns over suspicious activities in crypto.

Another area where AI can be useful is in customer service, particularly for exchanges. We have developed a conversational assistant built especially for digital asset exchanges, allowing users to ask for and receive instantaneous help with trades, their order status, and their account history, among other functionalities.”


Adam Funnell, CSO of CLC & Partners

“AI will transform most industries, but finance is one I find most interesting.

Financial Crime

Let’s begin by considering financial crime. The overall cost of compliance amounted to $213.9 billion in 2021. Artificial Intelligence has started to disrupt this industry, able to detect trends and irregularities within financial information. This enables AI to support financial institutions in preventing money laundering by validating transactions, reinforcing security measures, and responding to potential risks. Nevertheless, an ongoing concern is AI algorithms biases. This is when the tendency of algorithms reflect human biases.

Supporting and replacing analysts

I was talking recently to someone hiring a CFO, who had, had a bad experience with a CFO. Let’s imagine for a minute, a superhuman CFO. This is a term I have heard several when discussing AI for financial modelling. Supercharged simulations of various scenarios, identifying risks and resources to develop strategies, without error, bias or emotion to influence decision making. And, of course, it’s faster. So will we really see people at this level, replaced? Let’s not get too excited just yet, like many applications, it is unlikely to fully replace analysts any time soon, but will enable analysts to increase productivity.

Retail banking

What about the retail side of financial services? We are already seeing the increasing use of chatbots as part of customer support, facial recognition and voice command features, improving customer service 24/7. Many customers may not realise how much of their user experience is now managed by AI. As bricks and mortar branches close, AI will flatten the customer experience for online users and improve the web applications that have increasingly become the first point of call for customers.

Credit scoring via AI will continue to gain momentum and will help with more accuracy in accepting and rejecting customers for credit and minimise the credit losses faced by banks.”



Jonathan Chin, Co-Founder at Facteus

“In the world of Finance, AI tools like ChatGPT can significantly enhance the efficiency of investment analysis. For a single company, a human analyst needs to process and understand vast volumes of text from various sources such as company reports, industry news, financial reports, and investor transcripts. ChatGPT and these AI models can synthesize this information into cohesive insights and generate comprehensive summaries or draft sections of reports. This makes them powerful tools for augmenting human analysis, providing rapid, comprehensive insights while allowing analysts to focus on strategic decision-making.

-What are good examples of current success stories when using ChatGPT-like tools in finance?

Related to the first question. ChatGPT-like tools are being used to read, analyze, and summarize troves of financial news briefs for analysts today. This is especially useful for foreign investment firms based in countries where English is not the first language. The US equities markets are the largest in the world and the ChatGPT-like tools allow non-native English speakers to have an inexpensive highly skilled double checker that can help ensure nothing is getting “lost in translation.”

-What are some of the top challenges and best practices for addressing them?

The biggest challenge for ChatGPT-like tools in Finance continues to be compliance and trust. Often times rules and regulations lag behind technology. Investment analysts in finance have high standards for compliance and information auditing. ChatGPT-like tools are challenging because the information generated is not easily traced or audited. This is not just a compliance conundrum, but a trust hurdle that individuals and practitioners will need to overcome mentally as well. Can they trust the summary, information, data, or interpretation that ChatGPT-like tools provide?



Mac Steer Owner and Director at Simify

AI is a game-changer for the finance industry. The ability to take raw data and turn it into actionable insight is what has made AI such an important tool in the first place, but its impact on finance goes beyond simple data analysis: It has also changed how we think about risk management.

When we look at AI’s impact on the finance industry, it’s important to remember that AI doesn’t just mean “computer does everything.” Rather, it means that computers can do things that humans simply aren’t able to do—and this is true for finance as well as any other industry. Computers are able to analyze massive amounts of data much more quickly than humans and without error, which is why they’re so useful when it comes to financial modeling and risk management. But they’re not perfect; they’re not going to make decisions for you or replace human judgment anytime soon.

Instead of replacing us with machines, AI will help us become better versions of ourselves by augmenting our abilities in new ways that were previously impossible before now.”



Slaven Bilac, CEO & Co-Founder at Agent IQ

“AI holds tremendous promise to impact – and improve – the life of financial services customers, and with ChatGPT propelling itself to the frontpage of publications, it may seem preordained that our interactions of the future will be predominantly managed by chatbots and virtual assistance.

No more waiting times with support available 24/7 – sounds like a customer’s dream. But how many of us have experienced frustrating interactions with virtual assistants as they try to ascertain our intent and provide a canned response? At times, a simple answer is just what we need but more often than not, life is a little more complicated than that.

One use case includes Extraco Banks (a $2B bank in Waco, TX) launching their public-facing AI virtual assistant, Gabby. What is unique about Gabby is that she gives customers the ability to ‘bypass’ the simple answers and request help from a banker immediately. Applying an agile mindset and AI-powered conversational analytics helped the bank to address 75-80% of queries without involving a banker, thus allowing bankers to spend more time helping customers with more complex issues.”


Kerel Verwaede, Chief Marketing Officer – CMO at Cryptology.com

“Recent advances in the field of AI, notably with the likes of ChatGPT and what it meant for the working force, have seen a sudden spike in interest over the applications AI could have over a variety of fields.

When it comes to trading, AI has actually been one of the first applications to generate significant strides of its potential as a game changer and the possibility of generating vast amounts of revenues over short periods of time. As such, AI has already has had rapid advances over the last decades and will likely continue to do so, but one needs to understand that GPT and the likes use very different AI models than what is currently used for trading.”


Related: AI Might Unleash The Biggest Bubble Of All Time

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Kevin Moore - E-Crypto News Editor

Kevin Moore - E-Crypto News Editor

Kevin Moore is the main author and editor for E-Crypto News.