How will AI Change the Financial Industry? (Roundtable Interview)
There is little or no trust when it comes to people and their money. This assertion existed before banks got digitized. Now, there are automated solutions everywhere. In the finance industry, AI-driven solutions will change the game. That much is true. However, there is more to the situation than meets the eye.Now that AI has arrived, everything in the industry will change. On one hand, we have personalizations, improved accuracy, and speed. On the other hand, an overreliance on AI has security implications. Our panel of experts has a lot to say about the issue.
C’est la vie…
Arsalan Vossough, Co-Founder & CTO at BetterAI
“AI is fundamentally transforming the financial industry in 2024, playing a pivotal role in several key areas. Its most notable impact is in operational efficiency, where AI automates manual processes, enhances data analysis, and informs investment decisions. This not only makes financial operations more streamlined, but also significantly improves risk management by identifying suspicious activities and anomalous transaction patterns. AI’s role in elevating customer service is equally transformative. Financial institutions are adopting AI-driven chatbots, virtual assistants, and personalized recommendation systems, aiming to deliver a more engaging and individualized experience. This technological shift is crucial in enhancing responsiveness and tailoring services to specific customer needs.
In the field of fraud detection and cybersecurity, AI’s capabilities are proving to be game-changers. Advanced algorithms have been developed to detect fraudulent behaviors more efficiently, securing transactions and preserving customer trust in an increasingly digital financial landscape. In cybersecurity, AI shines in real-time monitoring of data access and user behavior, providing a robust defense against evolving cyber threats. This includes identifying harmful content and protecting sensitive information from cyberattacks, a critical aspect in maintaining the integrity of financial systems.
Furthermore, AI is redefining traditional practices like credit assessment and algorithmic trading. The focus on mitigating biases and ensuring ethical AI practices is critical, particularly in sensitive areas such as credit scoring. This involves transparent training of AI models, adherence to regulatory compliance, and accountability.
In trading, AI-driven algorithms are gaining popularity, balancing innovation with regulation to ensure market stability and retain investor confidence. While AI introduces challenges, its transformative power is enhanced through collaborative efforts between industry players and regulators. Rather than replacing human jobs, AI is enabling employees to focus on creative and strategic tasks. As these trends continue, AI is not just a tool but a revolutionary force, shaping a new generation of efficiency and security in the financial industry.”
Michael Hentschel, Co-Founder at IntualityAI
“Proposition: AI is poised to revolutionize financial and other decision sectors, depending on human collaboration and willingness to delegate:
AI-autonomous financial decisions are expected to become more prevalent.
AI’s ability to analyze vast amounts of data quickly and accurately allows it to make predictions and decisions that can aid in budgeting, investment strategies, and risk management, However, there is a critical need to create a culture that embraces and trusts AI, which for various human psychological reasons may not develop smoothly or as expected.
Human desire and trust to delegate is THE KEY as to whether autonomic AI will succeed. In some cases, human desire to delegate has non-optimal motivations, such as laziness, non-comprehension of details, human limitations, human inattention, excessive or insufficient trust, etc.”
AI is increasingly being used in collaboration with human decision-makers. But this is still in its infancy.
This approach, sometimes known as Human-AI collaboration (HAIC), aims to create synergistic teaming between human decision-makers and AI systems. Do machines suggest and wait for instructions, or do they decide AND execute?
Traders want to trade, but black-box trading systems (in the broad sense of ANY transactions, involving buy/sell decisions for whatever reason) are generally designed to optimize without mixing machine rationality (however programmed) and human rationality (snap intuitive decisions). Neither machine nor human decisions necessarily involve traceable documentation of why probabilistic decisions are made.
While AI can process and analyze data at incredible speeds, it lacks the nuanced understanding and instinct, and judgment that humans bring to the table. Therefore, human-centric prediction remains a key component of effective financial planning and trading.
AI’s prediction accuracy claims to be improving on the quality of human-centric financial projections.
AI algorithms can process vast amounts of data quickly, identify patterns, and make predictions that can aid in budgeting, investment strategies, and risk management.
However, the accuracy and dependability of AI-driven predictions are contingent upon two crucial factors: the quality of the data used and the efficacy of the AI model employed, as well as machine understanding of inherent risks and probabilities.
Human prediction has weaknesses, and machine prediction has weaknesses. Do two wrongs combine to make more-right decisions?
The combination of AI’s super-rationality with human intuition will create higher financial profits
Higher profits will create more competitive financial services in qualitative and quantitative terms. For sustained competitiveness, combining AI’s computational capabilities with human judgment is key, which is to say human involvement must be maintained, preserved, and programmed in.
AI systems, while highly advanced, may still fall short in the exercise of good judgment or common sense. Unlike machines, humans are capable of bringing their intuition and judgment to the table when making decisions. How to program this will be one of the main challenges ahead.
Is this ultimately a matter of Will? Who in the long run will make the financial-sector decisions? Where machines can do better than humans, will they insist that their “will” will become standard, or will humans simply defer all such cases willingly?”
Shawn Carpenter, Chairman & CEO of Stock Alarm
“The Power of AI: A New Era in Banking and Finance by Shawn Carpenter, Chairman, and CEO, at StockAlarm
How AI is Changing the Game in Finance
Hey there! You’re right if you’ve noticed that banking and finance are evolving at warp speed! Artificial intelligence (AI) has become the game-changer, shaking up the world of money and all related things. In this article, we’ll explore the extraordinary impact of AI, the hot new trends, and what this means for the future of finance.
Automation: Making Life Easier
Let’s kick things off with the automation revolution. AI is like your trusty personal assistant, handling tons of financial tasks with lightning speed. It handles data entry, verifies transactions, and even sniffs out sneaky fraud. The result? Fewer mistakes, faster processing, and more time for humans to focus on finance’s fun, creative parts. And trust us, AI is just getting started!
Personalized Service: It’s All About You
Have you ever felt like your bank understands your needs? Well, thank AI for that! AI-driven chatbots and virtual assistants are your new BFFs in the banking world. They’re available 24/7 to answer your questions, recommend the perfect financial products, and make your banking experience feel like it was tailor-made just for you. It’s like having a financial guru in your pocket!
Fighting the Bad Guys: Keeping You Safe
AI is not just good at making your life easier; it’s also a superhero in fighting financial crime. AI can spot suspicious transactions in real time by analyzing tons of data. This means your hard-earned money is safer, and you can sleep soundly knowing that AI is looking for any shady business.
Smart Investing: AI is Your New Financial Wizard
When it comes to investing, AI is the wizard behind the curtain. It crunches numbers, analyzes market trends, and considers news and sentiment. The result? AI-powered trading systems can make split-second decisions that could lead to better returns. And if you need to get into picking stocks, AI can help manage your portfolio and suggest intelligent investment strategies.
Staying Legal: AI Keeps Banks in Check
Finance is a world of rules and regulations, and AI is the perfect wingman for staying on the right side of the law. It helps with monitoring transactions to catch any rule-breaking and ensuring banks play by the book. In short, AI helps banks avoid costly fines and penalties.
What’s Next in AI and Finance?
So, what’s in store for the future? Here are some exciting things to look out for:
Explainable AI (XAI): AI is getting smarter, but it’s also essential that it’s transparent. XAI is all about making AI decisions understandable, which is vital, especially in lending where fairness is crucial.
Quantum Computing is like taking AI to the next level. It can crunch numbers at warp speed, revolutionizing complex financial calculations and even how we price things like derivatives.
Blockchain and Cryptocurrencies: AI is getting cozy with blockchain, making it easier to detect crypto fraud and automate trading in the world of digital currencies.
Robo-Advisors: These AI-driven financial advisors are gaining popularity, especially for individual investors. They create and manage investment portfolios based on your goals and risk tolerance.
The Future of AI in Finance
AI’s future in finance looks bright. It’ll be a key player in risk management, helping predict and prevent financial crises. Customer service will continue to improve, becoming even more personalized. With AI’s help, regulations will stay in check. Plus, AI will work towards making financial services more accessible to everyone. As AI grows in finance, ethics will also play a crucial role, ensuring AI is fair, unbiased, and transparent.
The Final Word
In a nutshell, AI has already transformed finance, and it’s just getting started. Those in the financial industry who embrace these changes will thrive in the exciting and ever-evolving world of finance. Get ready for a future where your financial life is smarter, safer, and more personalized than ever before, thanks to AI!”
Giuseppe Sette, Co-founder and President at TOGGLE-AI
“Generative AI is all the talk now, but Toggle AI has been using Generative AI for years to provide hedge-fund-style analytics that helps predict price movements in 40,000 securities including stocks, commodities, fixed income, currencies, and ETFs. Now the company is working directly with many of ChatGPT’s investors and creators to create ChatGPT for investing.
It used to take traders days to analyze data for complex trades, but now it can be done even quicker with Toggle’s generative AI. What’s next? Toggle AI is taking it one step further teaching ChatGPT to invest allowing for a two-way conversation for more in-depth analysis in mere seconds.
Toggle is already used by the largest hedge funds in NYC, as well as institutional traders and foreign banks. Backed by VCs and famed hedge fund trader Stan Druckenmiller of Duquesne Capital Toggle AI is led by former CIO and hedge fund trader Jan Szilagyi along with President and co-founder Guiseppe Sette.
What can you ask ChatGPT for investing? Imagine asking these questions based on your portfolio and getting an answer in seconds:
• What are the best stocks to buy in a recession?
• What stocks are likely to rally next week?
• If oil goes down 10% what stocks in my portfolio are most vulnerable.
The impact of LLMs (large language models) :
• “This is the AI moment in finance – it will transform our ability to respond to fast changing markets
• Chat GPT is one of many Large language models that will transform how we communicate with machines. LLM is an acronym investors will want to keep an eye out for. Suddenly, everyone will be able to speak – and be understood by – a machine.
• This will enable TWO-WAY communication for the first time ever. If RoboAdvisors were speaking AT YOU, the next generation will be analytical engines that do your bidding.
• The biggest challenge will be reliability: what any investing Chat will be able to convey is only as good as the analytics and information it’s trained on – and ChatGPT is quite a poor markets analyst in this regards
• Across all industries, R&D teams are paying really close attention to AI, but particularly LLMs right now
• At TOGGLE we are teaching these AI models to invest and endow them with analytical abilities that can really break down the barrier between users and analysis
• They won’t replace humans but they will dramatically improve our ability to respond to changing market conditions by crunching an extraordinary amount of data relly, really fast to get you the answers
• It is not a question of if, but how fast, other financial firms fully embrace this type of technology.
• A big problem at the big banks is that a huge amount of human capital – written research, data tables etc. – is fragmented and spread across the institution, often hidden away in document repositories where they are only accessible through keyword search.
• You don’t know what to search for when answering questions like “what happens when inflation peaks” – do you look at core CPI, CPI, core PCE, … GPT-4 coupled with an analytical tool like Toggle can solve this issue
• At Toggle, we are addressing this problem of “IKEA Of Financial information” – the data pieces are all there but need to be assembled to be useful.
• We are training GPT Models to help banks effectively revitalise their existing store of research and data, and enable any user across the institution to access relevant data OR analysis in seconds
• This is an important difference: GPT-4 on its own can find and articulate facts but coupled with analytical tools like what we are doing can turn it into a true analyst for any advisor
• That will be the next frontier beyond merely searching for existing data: enabling advisors to perform custom analysis and generating personalized insights on that basis
• Traditional flow would be either giving a very generic answer (which leaves clients thinking they are paying for very non-personalized service) or spending a significant amount of time hunting down relevant reports or analysts across the bank
• Accuracy is also a big factor: we are training GPT models only on vetted financial data, research, and information which dramatically limits hallucinations.”
Stefan Rust, CEO of Truflation
“The main advantage of artificial intelligence is the increased efficiency and productivity gains it will inject into global industries and sectors, including finance. When it comes to cryptocurrency, AI has the power to automate time-consuming menial processes, making the entire space more user-friendly. Imagine a DeFi protocol that offers the same seamless experience as a traditional investment app? AI can work in the background to make this possible.
In fact, AI is set to transform the entire financial advice market, offering a cheap alternative to costly wealth management solutions, and thereby democratizing access to financial services. AI can do anything from simple portfolio construction to taking over simple transactions to free up valuable time for important investment decisions. For example, in the DeFi space, AI could facilitate automatic staking, unstaking, and transaction approvals, based on a pre-set investment strategy or a predetermined goal.
AI will also have huge implications for data collection. For example, AI bots can trawl through vast swathes of blockchain data to pick out useful information far more quickly than a human could do it, or even the legacy automated processes. Through machine learning, these algorithms can adapt to the varied needs of each industry, and even each individual user, revolutionizing the way we consume and utilize data and helping to ensure that this data is accurate and up-to-date. This, in turn, can facilitate better financial decision making, more accurate monetary policy decisions, and open up access to limitless markets that have previously been available to only a small number of financial institutions.”
Jeff Owens, Co-Founder of Haven1
“While AI will undoubtedly bring many benefits to the world of finance, it also brings with it a greater risk of hacks as it can potentially add an extra layer of sophistication and thus exacerbate fraud and theft attempts. This is particularly the case with crypto fraud, as there are no recourse mechanisms in place in the case of theft, making crypto users far more susceptible than traditional investors. But in both areas of finance, AI could exacerbate the risks.
For example, machine learning algorithms could be used to analyze patterns in users’ behavior, identify vulnerabilities in security systems, and automate the execution of attacks so they take mere seconds. AI-powered tools can even mimic human-like interactions, allowing them to get around humanity checks and making it harder to identify malicious activities.
As such, the advent of AI means that there is an even more dire need for robust security solutions to protect against these increasingly sophisticated attacks, especially in the crypto space. Whether it is enhanced security measures that are designed to withstand AI-powered attacks or traditional security measures, such as identity verification, crypto, and the entire financial ecosystem will require an upgrade to protect against the increasingly sophisticated threats posed by AI.”
Jennifer Arnold, Co-founder and CEO at Minerva
“While AI applications like chatbots and decision-making algorithms are now commonplace in finance, the new frontier lies in two key domains: leveraging AI for more inclusive banking and enabling effective and efficient anti-money laundering (AML) strategies to protect vulnerable populations from exploitation and financial crime.
As an AI-driven AML Compliance platform, Minerva is at the forefront of these changes, predicting significant shifts in how financial inclusion and AML Compliance will evolve and the important synergies between them.
By adopting novel AI model training methods to mitigate bias, AI can break down the barriers to inclusivity, reaching vulnerable populations, such as seniors, newcomers, and human trafficking survivors. We are learning more about these niche populations through our current AI applications, which will allow us to tailor financial services to their unique needs and challenges, accelerate their integration into mainstream banking and enable access to savings and credit, giving these groups greater financial strength and purchasing influence.
As AI is rapidly transforming the financial services sector, our approach to addressing Anti-Money Laundering (AML) compliance and financial crime prevention and detection through AI is also transforming.
AML Compliance is undergoing a revolution with AI’s predictive analytics. Enhanced know your customer (KYC)/Risk Assessment methods leverage advanced neural networks and knowledge graphing techniques for more accurate and faster entity resolutions. This development heralds a more efficient, practical, and scalable risk-based approach in AML Compliance – a key enabler to ensuring that vulnerable populations, who’ve struggled with identification barriers and fair risk assessment, can enter into both traditional and neo-banking eco-systems seamlessly and without unnecessary friction.
In Blockchain/Web 3.0, AI will play a pivotal role in enhancing KYC processes. The introduction of self-sovereign identities and self-managed identity tokens is a significant development. These innovations will allow individuals to control their identity data, offering a secure and efficient way to manage identity verification in digital financial transactions and could act as a deterrent by making financial control and exploitation nearly impossible. This approach is particularly relevant as we will see more collaborations like PayPal with Paxos or Visa with Circle, where secure and compliant transactions are paramount and traditional payments infrastructure converges with web3.
For these AI-driven advancements to be effective and far-reaching, access to diverse data sets and a balance between innovation and ethical practices are crucial. Clear regulatory guidelines must address data access, model bias, and model safety, ensuring responsible AI deployment.
AI’s integration into financial services and AML Compliance is not just an emerging trend, but a paradigm shift that will redefine these sectors. With its potential to improve efficiency, inclusivity, and security, AI is a transformative force in the financial world.”
Adam Garcia, Owner of The Stock Dork
“AI’s role in reshaping the financial industry cannot be understated. The technology is likely to revolutionize the way firms manage money, from automating traditional practices to creating entirely new solutions.
In terms of roles, AI-driven solutions are being rapidly adopted for various purposes, which include, but are not limited to, financial risk assessment, algorithmic trading, and fraud detection. For instance, AI’s ability to learn and adapt from vast quantities of transaction data allows for the rapid detection of anomalies that could indicate fraudulent activity. This is a far more efficient system than any manpower-based one, as it reduces false-positives and enables quick action.
Other new emerging trends include robo-advisors and AI-driven wealth management platforms. These perform tasks such as analyzing market conditions, predicting stock performance, and offering personalized investment advice, all in real time. The days are not far when these AI-powered platforms will be trusted to handle large portfolios autonomously.
Customer interactions are another key area where AI will make a significant difference. Chatbots and AI-powered customer service tools are anticipated to assist with customer queries swiftly and accurately 24/7 thereby enhancing customer experience drastically.
Looking into the future, finance could well be an industry where AI implementation will be most profound. The primary factors for such a massive shift are the sector’s inherent complexity, its reliance on speedy decision-making, and the vast amounts of data it generates. Financial firms that effectively implement AI strategies can significantly enhance risk modeling, predict market anomalies, and streamline operations, leading to better revenue and profit realization.
However, AI in the finance industry comes with its own set of challenges. Key among them will be ensuring data security and privacy. Financial firms must endeavor to balance the benefits of AI with ethics and customer trust. To accomplish this, the proper legal framework and protocol for data usage and privacy should be in place.”
Dennis Gada, EVP and Global Head of Banking & Financial Services at Infosys
“AI is set to be a transformative force in banking and finance. AI can analyze massive amounts of data, identify patterns and insights, and automate complex processes. This presents potential to enhance operations, risk management, and customer experiences across the financial sector.
An area where AI can have significant impact is in detecting and preventing fraud. AI algorithms can identify abnormal transactions in real-time and spot potential criminal activity faster and more accurately than traditional systems. By combining customer profiles, transaction histories, and network patterns analysis, it’s promising that AI will greatly strengthen fraud detection and prevention capabilities.
Together, classical AI and generative AI are exponentially advancing the software development life cycle from product reimagination to development, testing, and operations. As banking and financial customers are embracing agile by incorporating “AI in the flow” the capability to reuse and create a faster “go-to-market” product gets accelerated.
AI also enables more personalized customer and advisory services. With natural language processing and sentiment analysis, AI chatbots can understand customer questions and needs.
And by accessing a customer’s financial data and modeling different scenarios, AI robo-advisors can provide tailored recommendations on financial products and strategies. Rather than a one-size-fits-all approach, AI allows banks to deliver hyper-customized experiences.
AI needs strong governance across several lenses, model risk management, legal, compliance, cybersecurity, first and second line of defense, and so on. Enterprises need to have a working committee across all these groups giving it transparency to provide patterns-based approvals to avoid blockers.
The transformative potential of AI for banking and finance cannot be overstated. Leaders in the sector should be proactive in piloting and integrating AI capabilities. Institutions that strategically leverage AI stand to reduce risks, improve efficiency, and exceed customer expectations. With thoughtful implementation, AI can shape the future of financial services.”
About Dennis:
“Dennis is the Global Head for Financial Services at Infosys and has P&L responsibility for a multi-billion-dollar growth business across North America, Europe, and APAC. He leads the Global Financial Services Executive Leadership team and is passionate about leveraging the power of technology to address challenges facing the financial services industry. Infosys is a global leader in next-generation digital services and consulting.
With his expertise across various business competencies, including sales, strategy, consulting, marketing, and general management, he has helped organizations drive large-scale transformation and innovation at the intersection of business and technology.
He is also focused on bringing cutting-edge technology solutions to established financial institutions through collaborations across his professional network, including a wide ecosystem of experts, startups, and established industry players.”
Paolo Malinverno, Head of Strategy and Innovation at Sensedia
“My answers to your questions are focused on the short to medium-term, 1-3-year outlook for AI in fintech and the financial industry. Most of the AI I’m talking about will be Generative AI, even if AI is much broader than that. Managing the risk of Generative AI hallucinating is an extensive topic. I’m not ignoring it in what I write here; I am just focusing on the most common solutions we will see in the marketplace in the coming years.
Most players in the financial industry are seeking ways to offer better customer experiences, increase revenue, and become more operationally efficient. Keeping current customers is a critical objective for financial institutions, and several analyst firms are predicting that paying attention to customer retention will be an even higher priority in the short term.
Better customer experiences often equate to effectively personalized interactions with the financial institution, and Generative AI has a lot to offer in this area. For example, conversational user interfaces can increase revenue by managing simple interactions, providing explanations, or even recommendations, whether they are used by a (human) financial advisor or are in direct touch with the customer. Internal bank and insurance staff can benefit from those, too.
Generative AI in financial services marketing has multiple uses, generally around conversational interfaces (not just chatbots), which can operate in several languages. They can be made more sophisticated by collecting data from multiple sources, i.e., on the success of specific financial products, and can suggest pricing/repayment options, especially for mortgages and life insurance. The tech can help financial institutions increase their customer base.
The concept of the digital twin of an organization will also be increasingly popular among financial institutions. Digital twins are based on constructing a sophisticated model of a real thing to understand its behavior better when external or internal conditions change. The concept was very popular in manufacturing (digital twins of jet engines are widely used), but it has recently been extended to modeling actual companies: for example, how is the pattern of calls in the call center going to be affected if a Bank happens to sell hundreds of more mortgages? What is the impact on internal staff if we sell fewer pensions in this time of uncertainty, and under what conditions should we stop that and sell more? Data ingestion from multiple sources and frequently synthetic data (generated by AI) will be used to run these what-if scenarios and minimize operational risks.
As you can see from the usages above, a lot of AI will be used by humans to enhance their work, at least initially. In time (months, on average), the models can be trained and given a higher level of autonomy according to the financial institution’s risk tolerance.
“As AI evolves within the financial industry, Generative AI applied to personalized customer experiences has the potential to reshape the industry’s landscape, fostering deeper engagements and driving revenue growth.”
“Financial institutions will benefit from navigating the shift towards AI-driven customer retention strategies, Generative AI emerges as the catalyst for tailored interactions, amplifying marketing efforts and expanding market reach to increase revenue.”
“Embracing the concept of digital twins, financial institutions will harness the power of AI to anticipate and mitigate operational risks, paving the way for more data-driven decision-making in a continuously changing landscape.”