In the fast-paced world of finance, the rise of artificial intelligence (AI) has been nothing short of revolutionary.
As this cutting-edge technology continues to infiltrate every corner of the industry, from trading floors to customer service departments, we want to hear more about how AI is revolutionising the industry.
To shed light on this topic, we asked the experts..
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Our Experts
- Matt Mckenna, Head of Communications and Research at finder.com
- Rachael Greaves, CEO/CSO and Founder at Castlepoint Systems
- Tim Hood, VP EMEA & APAC at Hyland
- Ananth Gundabattula, Co-Founder at Darwinium
- Kate Leaman, Chief Market Analyst at AvaTrade
- Christian Trummer, Co-Founder and Chief Scientist at Bitpanda
- Emanuele Tomeo, Chief Technology Officer at Mosaic Smart Data
- Kimberly Dillon, VP of Brand at Cleo
- Christen Kirchner, Senior Solutions Expert, Fraud & AML at SAS Northern Europe
- Alister Sneddon, Head of Product at CMC Invest
- Lacey Hunter, CEO and Co-Founder at TechAid
- Nikhita Hyett, European Managing Director at BlueSnap
- Ryan Sorby, Regional Head of Debt Finance at Growth Lending
Matt Mckenna, Head of Communications and Research at finder.com
“Elements of AI have been used behind the scenes by large investment firms for a while but it is the recent launch of platforms like Chat GPT that could prove to be most disruptive. These tools have caught the public’s attention for a variety of reasons, and while they are still error-prone or using old data in the case of Chat GPT, they have demonstrated their potential to change the status quo of various industries.
“In the case of investing, AI’s ability to find and synthesise large amounts of technical data could empower more consumers to make their own decisions and reduce reliance on financial advisers and even fund managers.
“To demonstrate the potential of AI, we decided to create a (fictional) investment fund by persuading Chat GPT to pick 30+ stocks based on a range of criteria we took from the UK’s most popular funds. We then pitted them against each other, and after 3 months the Chat GPT fund is up over 7% compared to the top 10 funds, which collectively are at 0%. This was just a thought experiment and consumers should absolutely not be using AI tools for financial advice in their current form, but it does hint at what could be possible in the future.”
Rachael Greaves, CEO/CSO and Founder at Castlepoint Systems
“Generative AI has been steadily gaining traction with corporates for several years but has recently burst into the public consciousness as well. Risk managers use generative AI in the same way many of us do – to speed up their processes.
“Generative AI is language-based, and most of risk management is actually based on language. While there are quantitative models for assessing information risk, such as FAIR (Factor Analysis of Information Risk), most organisations manage risk from a qualitative lens. Risk managers read, research and review (usually unstructured) content to understand risk context, threats, and potential impacts. They produce concise, clear recommendations, and then communicate those recommendations effectively as part of risk treatment.
“Risk is a constantly evolving domain. The threat environment changes day to day, and the sensitivity of the internal data and processes we manage can change minute to minute. The cycle of risk identification, analysis, evaluation, treatment, and monitoring is (or should be) dynamic. The requirement to do significant research (and writing) at every step of the process necessarily limits the cadence of risk assessment.
AI-driven risk assessment technologies will initially focus on efficiency. ChatGPT is not the answer for this, as its information is outdated and likely unreliable when considering specific or complex risks. But pointing AI internally, at the whole corpus of legacy and current content in the network, delivers a very strong use case. Using AI to read every document, email, and database in the environment, parse it, and explain it back to us in terms of its risk and value, is a capability we already have that is being used by many governments, corporations, and regulators on their own networks right now.”
Tim Hood, VP EMEA & APAC at Hyland
“The increased push for modernisation in the financial and insurance sector is driven by a need to future-proof. While artificial intelligence and machine learning are often deemed buzzwords in the industry, companies should be looking at identifying the applications of those technologies that deliver the best value for their customers.
“The first phase of this, is implementing an architecture and a data structure that enables such a process. The second phase is working with customers to understand the particular use cases and key drivers for them. The potential uses of AI can be anything from lowering risk to making information accessible more quickly or even allowing for the identification of trends.
“The use of such technology will be an invaluable tool to identify trends and behaviours, allowing these companies to react more quickly to the intense pressures that these institutions are under to modernise and compete in an economically unstable landscape.”
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Ananth Gundabattula, Co-Founder at Darwinium
“AI is at the epicentre of driving the online fraud problem we’re seeing today. According to one estimate, Brits lost as much as £4 billion to scammers in 2022. Although the sheer pace of technology innovation we see today benefits our society and economy immeasurably, criminals are using these advancements to scale their operations to new levels.
We’ve entered a time where machines are fighting machines. So, AI is both the problem, but is also the solution too. The next wave of innovation in fraud will come from cloud-powered AI – or more correctly, machine learning (ML). Leveraging the power of the cloud, new malign ML models offer cybercriminals the prospect of automating tasks that only humans could perform a few years ago. That’s bad news for us all and fraudsters often have the advantage. They have the element of surprise and the financial motivation to succeed.
That’s not all. AI can be trained by the bad guys to mimic human behaviour more realistically. For example, creating fake but compelling enough image data of a user’s face which might allow a transaction to proceed, as the checking computer assumes it to be a photo of a new user. Or it could be trained with video or audio data in the public domain (e.g. clips posted to social media) to impersonate legitimate customers in authentication checks. Even sophisticated ML models toda are being probed and attacked for weaknesses by malicious AI.”
Kate Leaman, Chief Market Analyst at AvaTrade
“Artificial Intelligence (AI) is transforming the finance and trading industry, by driving efficiency, accuracy and innovation. It is powering automated trading systems with machine learning algorithms and data analysis capabilities to maximise returns. AI is also improving risk management processes and fraud detection, as well as customer service through AI-powered chatbots and virtual assistants. Additionally, it is being used for portfolio management and asset allocation, using historical data and market trends to construct diversified portfolios.
“Despite the numerous advantages of AI in finance and trading, there are significant challenges too. These include data privacy, which is a major concern when it comes to AI in finance, as algorithms are becoming increasingly invasive. Other challenges include algorithmic bias, which can produce inaccurate performance assessments and undermine trust and finally the challenge of regulatory compliance in an ever changing regulatory landscape.”
Christian Trummer, Co-Founder and Chief Scientist at Bitpanda
“Historically, the ability to grow wealth through investing was something that was only available to the richest in society, but as technology has improved so have the opportunities that every day people have to control their own finances. The internet, blockchain technology, and digital assets have already reshaped finance, we see AI as the key to unlocking the next big evolution.
“Working with a financial advisor, even in the unlikely event that you find one that is good and really cares about your finances, is something that is simply too expensive, or indeed intimidating, for most people. By using AI that has been trained on hundreds of thousands of market data points, we can put a personalised wealth manager in everyone’s pocket, and allow a new generation of investors to access the benefit of customised investment advice and support – because no one cares as much about your money as you do. Bitpanda has already committed $10 million towards establishing a dedicated AI department within the business to ensure that we are capitalising on what this technology can provide for our customers. This is just the start.
“We believe that AI will be the tool that unlocks financial freedom for millions of people and will move us closer to our goal of unlocking wealth creation for everyone. This technology has the power to help people with many aspects of their lives.”
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Emanuele Tomeo, Chief Technology Officer at Mosaic Smart Data
“AI is transforming capital markets – but the technology this industry requires is very different to large language models (LLMs) such as ChatGPT in its current form. Forbes this year, listed ten use cases for ChatGPT in the banking industry. But if we look under the hood, the technology currently has a number of issues that prevent it from delivering true value to banks that are looking to gain a comprehensive view of their data and extract actionable insights from it:
Data is a bank’s most valuable asset, and as such they guard it fiercely. Any risk of a data compromise that could reveal sensitive information to their competitors will see them running for the hills.
“When it comes to niche capital markets such as FICC, a careful combination of AI technology and human expertise is required to ensure the output is of use to salespeople and traders.
“Consistency of information is very important in financial markets – banks need to know they are providing credible, accurate and consistent recommendations and advice to their clients.
Model explainability is increasingly important in the field of data analytics. Banks want to know exactly why they are being given certain intelligence, and how the models underlying AI technology are working.
“There’s no doubt that this is an exciting time for AI and its capabilities. LLMs have surprised even their creators with their unexpected talents. But for it to be truly useful, this technology must be tailored to the nuances of capital markets and combined with specialised human knowledge.”
Kimberly Dillon, VP of Brand at Cleo
“AI is transforming the finance industry by facilitating more intuitive and personalized user experiences. With recent advances in generative AI, the next industry-wide shift will be towards conversational user interfaces. We’ve been leading that charge over the past 6 years with our AI assistant, Cleo, that helps our users manage their money with just a quick text.
“AI’s predictive and personalization capabilities are changing the way our users manage their finances. By analysing transaction history and personal priorities, AI can predict future financial difficulties and suggest proactive measures to take. Smart budgeting features enabled by AI can track bills and income, aiding users in creating personalized budgets based on individual spending habits. E.g. “Have I spent more on clothes this month than last?”
“By turning financial advice into a conversation, AI can help users confront their spending habits in an approachable way. At Cleo, we have trained our AI with our unique tone of voice that can both roast and hype you, leading to a more positive and proactive approach to personal finance.
In the future, we believe there will be an AI assistant for every domain of our lives, with Cleo as the AI assistant for personal finance.”
Christen Kirchner, Senior Solutions Expert, Fraud & AML at SAS Northern Europe
“Earlier this year, Fortune predicted that AI and machine learning were set to have a breakout moment in finance – so far, this is proving true. We’re seeing banks in particular benefit from developments in AI technology, from enhancing the customer experience to preventing fraud.
“In a competitive market, embedded AI tools have allowed banks to create a seamless journey for customers across channels, joining up data from various touch points – whether it be online banking apps, SMS messages, or even social media. As such, many banks are now relying on AI to enhance customer engagement.
“We’re also seeing AI help prevent fraudsters’ efforts. Given that UK consumers lost a combined £1.2 billion to fraud last year, using technology in this way could not be more important. Unlike manual methods or lesser technologies, AI can identify fraudulent transactions in real time, spotting changes in customer behaviour that allow banks to stop fraudsters in their tracks.
“Fraudsters may be able to adapt to changing contexts quickly, but AI is quicker, meaning we can expect to see AI continue to have a significant, positive impact on the finance industry well into the future.”
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Alister Sneddon, Head of Product at CMC Invest
“You may have seen the news of a fund chosen by ChatGPT outperforming the UK’s top 10 most popular funds previously in the year. Though this is an eye-grabbing headline, this isn’t how I see AI being used in financial services beyond the initial buzz. Stock picking is a deeply personal experience and expression of your values and beliefs. We have seen models attempting to outperform the market for years. Famously Warrant Buffet claimed active stock pickers on the long term wouldn’t outperform the passive index, a bet he is set to lose, ironic as he is an active manager himself!
“However, AI can complement that human element of the financial services. I see its power in using data to inform and improve the consumer experience. Take a person’s monthly expenditure as an example: knowing when standard payments come out of their account isn’t the true power of AI. But, being alerted to lunch spending gradually increasing month-on-month is. In the case of investments, if AI flags that there are higher dividends and income than expected across a person’s investments, these insights can help them decide their next steps and take action.”
Lacey Hunter, CEO and Co-Founder at TechAid
“AI’s impact on the finance industry and the aid sector is significant, offering opportunities for increased efficiency and a more inclusive society. While the use cases for AI in finance may seem unrelated to the aid sector, the core issues of identifying, grouping, and matching the ‘best fit’ remain the same. In finance, AI streamlines processes by algorithmically matching criteria like financial statement audits and evaluating capital expenditures, leading to reduced errors and enhanced credit evaluation. These advancements are particularly crucial for regulated lenders who must conduct rigorous evaluations for every borrower. An AI model trained on historical data and industry-specific specifications could greatly enhance credit evaluation and approval.
“In the aid sector, AI plays a vital role in effective response coordination during crises. By programmatically assessing the needs of affected individuals and providing communication tools, AI enables coordination among public and private sector actors, preventing overlaps and gaps in aid distribution. Leveraging predictive analytics and responsible AI solutions allows for a quick and coordinated response to urgent needs.
“Matching supply with demand more effectively, AI contributes to combating food shortages, climate-driven disasters, conflicts, and inflation. By embracing innovative and responsible AI solutions, we can leverage technology to address the challenges faced by both the finance industry and the aid sector, ensuring a more equitable and efficient response to crises.”
Nikhita Hyett, European Managing Director at BlueSnap
“With the banking crisis causing many merchants to rethink who they bank with, AI is becoming increasingly instrumental in their decision of how they want to process payments.
“The recent failures of Credit Suisse and First Republic Bank have brought the importance of having a contingency plan into sharp focus. Merchants can’t afford to lose access to revenue and a positive cash flow due to a bank failure causing an outage to their payment processing.
“When it comes to payment processing, this is where payment orchestration that leverages AI comes in. True Intelligent Payment Routing uses AI to analyse a global network of banks in real-time and re-route customers’ transactions to ensure the highest conversion rates. In this process, the AI matches currencies and local cards with local acquirers, card issuers with card acquirers, and optimises merchant category codes. In addition, if one bank is down, AI automatically routes the transaction to another bank.
“A contingency plan like this protects merchants as it leads to high authorisation rates, lower costs and built-in redundancy and failover. As more businesses are partnering with payment orchestration platforms to gain access to this effective backup plan, it’s safe to say AI is catapulting the payments industry.”
Ryan Sorby, Regional Head of Debt Finance at Growth Lending
“From the perspective of businesses trying to secure funding, while valuations for tech businesses have faltered during the past 12 months, businesses with a strong AI proposition are one of the very few remaining sub sectors of the tech industry that are showing continued growth. This makes them an attractive proposition for specialist lenders.
“For lenders themselves, AI can significantly improve the operating efficiency of back-office finance processes. For example, for portfolio management, where manual data input and data analysis is high, AI can be utilised for tasks such as data entry, reconciliations, and trend analysis. This reduces the need for manual intervention, improving accuracy and speeding up processes.
“Likewise, AI enables the rapid collation of many data points, such as financial information, market trends and competitor performance, which form part of the initial analysis required for underwriting. Again, this helps to speed up processes and can allow for key risks to be identified earlier.
For some specific lending products, where there are high volumes and low risk features, AI can deliver even more of the underwriting process, as it’s much more formulaic. In the future, it’s likely that this process will become fully automated using machine learning.
“However, complex debt structures will always need human input to consider gut feel and comfort relating to risks and mitigants when underwriting, which AI is unable to do.
“It’s also important to note that there is very little regulation around AI yet so whilst there is great potential for it to be impactful for the finance industry, there are also associated risks.”
For any questions, comments or features, please contact us directly.
