Michael Imeson, Chartered MCSI

Michael Imeson, Chartered MCSI, is a senior content editor at Financial Times Live, a contributing editor at The Banker magazine and chair of the CISI Fintech Forum. 

Natural language processing, computer vision, machine learning and other types of artificial intelligence (AI) are increasingly being used by investment banks, asset managers and other participants in the capital markets. AI is transforming their business by improving operational efficiency, data analytics, customer service, risk management, regulatory compliance and more.

The nature and scale of its advantages across the financial sector were neatly summarised in the final report of the Artificial Intelligence Public-Private Forum published by the Bank of England and Financial Conduct Authority in February 2022. “AI is an increasingly important technology for UK financial services,” it says. “Firms already use AI across a wide range of business activities and the Covid-19 pandemic has accelerated the pace of adoption.”

Although the forum – whose members include capital markets practitioners from Credit Suisse, Acadian Asset Management and Royal London Asset Management – concentrated on the risks of AI and whether new regulations were needed, it is unequivocal about the benefits.

Similarly, the Organisation for Economic Cooperation and Development in its annual OECD Business and Finance Outlook 2021 says AI has the potential to “facilitate transactions, enhance market efficiency, reinforce financial stability, promote greater financial inclusion and improve customer experience”.

It adds that in 2020 alone, financial markets witnessed a global spend of over US$50bn in AI, accompanied by a boom in the number of AI research publications and in the supply of AI job skills. “Banks, traders, insurance firms and asset managers increasingly use AI to generate efficiencies by reducing friction costs and improving productivity levels.”

AI is no longer a technology of the future, it is a technology of today The UK’s Alan Turing Institute, in its 2021 AI in Financial Services report, says the technology “is already having transformative impacts on the delivery of financial services” and that its role will increase in the years to come. In capital markets, for example, “AI-based trading systems could contribute to a reduction of bid-ask spreads or otherwise enhance the efficiency of markets”. AI can also be used to improve models for calculating investment, credit or insurance risk, “models used in stress testing, and models used for the calculation of capital requirements”.

Another use identified by the Institute is in financial crime prevention. Detection systems may become more effective, due to the use of “machine learning, non-traditional data or automation” in spotting fraud or money laundering, “than systems based on explicitly programmed rules that do not adapt over time”.  

Capital markets at the leading edge of AI

Marshall Choy, senior vice president for products at SambaNova Systems – an AI software, hardware and solutions company – says AI is already widely used by capital markets participants, though some are at a more advanced state of adoption than others. This is due partly to a failure to recognise the benefits, and partly to a lack of in-house expertise.

“If you look at the main lines of business in a bank – whether that be capital markets, wealth management, commercial banking, retail banking or whatever – capital markets have traditionally been the leading-edge innovator,” he says. “If they can use technology to shave a few milliseconds from a transaction, or to gain the slightest competitive insight, that has a dramatic payback."

He explains that capital markets firms were early adopters of AI and "now they’re taking advantage of the latest developments ... For example, they are using deep learning and natural language processing to scour large volumes of text and unstructured data to gain insights into the markets and a competitive advantage in transactions.”

There is a move towards using larger foundational language models in preference to smaller models that specialise in specific tasks like text extraction and sentiment analysis. “We are seeing firms consolidate their efforts into larger language models which are more easily managed,” says Marshall. “They can use more parameters in these larger models to get higher levels of accuracy and quicker results.”

One obstacle to greater adoption of AI among investment banks, asset managers and other financial market firms is the lack of technical talent. “There’s a shortage of deep learning specialists,” he says.

AI in action

Societe Generale, one of France’s top five banks, positions itself as a 'data-driven bank' where data is a key asset and AI is the tool used to extract the full value of that data. The bank says all of its 25 business and service units across 61 countries are ramping up their digital transformation, in particular by adopting data processing and artificial intelligence technologies.

Claire Calmejane is in charge of Societe Generale’s ambitious digitalisation strategy. She is the bank’s chief innovation officer and president of SG Ventures, the bank’s corporate venture capital arm which invests in technology start-ups, many of which use AI.

“The bank has more than 330 AI and data use cases in production, of which nearly 170 are based on AI, in order to execute our strategy for a value of €230m at the end of 2021,” says Claire. “We are widely using the predictive power of machine learning to personalise customer value propositions and deliver a smoother and more responsive experience through advanced automation.”

Specific uses for AI across Societe Generale – in its corporate and investment banking division as well as in other parts of the bank – include facial and biometric recognition, fraud and money laundering detection, automated credit rating, analytical tools for monitoring market activities, and assessing applications from business start-ups looking for investment from Societe Generale ventures.

Societe Generale has developed an AI-based anti-money laundering solution that targets suspicious transactions by analysing movement of funds, and AI-based anti-fraud solutions that detect falsified cheques, fraudulent bank transfers and similar illegal activities, and fraud in the use of payment instruments by individual and corporate customers. The latter uses machine learning and behavioural analysis algorithms to identify abnormal or suspicious events and trigger warnings where appropriate. Fraud can be detected on an instant payment in less than half a second.

Societe Generale has also developed an AI-based multilingual monitoring and analysis service that reviews all written and audio communications by regulated staff in 16 countries.

And an AI-based relationship management tool that analyses an investor’s risk appetite, behaviour and other factors and then provides the investor with appropriate buy or sell recommendations.

Tomorrow’s technology today

AI is no longer a technology of the future, it is a technology of today. It is being widely used across financial services, including capital markets. Yes, some capital market companies are still at the “touch it and see” phase. Others are in the very early stages of adoption. Yet many have fully embraced AI and are putting it to good use. The question is no longer “should we adopt AI?” but “how fast can we adopt AI to transform our business?” AI is here.

Views expressed in this article are those of the authors alone and do not necessarily represent the views of the CISI.