In the financial sector, AI has become one of the main players; 80% of banks are highly aware of the potential benefits presented by AI (Business Insider). It is used to detect consumer and manager fraud, in chatbots and Robo-advisors to help customers make more informed financial decisions, to predict trends in the stock market and loan repayments. It has become a cornerstone of the financial world and is predicted to have saved banks and financial institutions $447 billion by 2023 (Business Insider).

Ahead of the AI in Finance Summit in London on 25-26 April 2023, we caught up with AI experts within financial services to find out more about the most talked about subjects around AI are in the finance sector. Here are the key takeaways from speaking to Ronan Brennan, Luke Vilain, and Giorgios Samakovitis on the latest opportunities and challenges with AI in Finance.

The Black Box Issue and Explainable AI

Data scientists and AI experts are predominantly focused on creating new and better techniques that can perform even better and more complex algorithms and calculations. In finance, this can lead to a lot of issues, as many times the code used in these programs is not completely understood by the people who made it. The financial industry is understandably heavily moderated, and the decisions made by algorithms should be fully understood. For example, a person could receive a poor credit score and have their loan application declined. Such a person could then file a claim and request a detailed explanation of all the factors that led to this decision – if the bank or financial service cannot explain that decision, it can lead to the loss of customer trust.

The recent focus on regulation and policymaking around data and AI means that the need for a framework for understanding how this AI works and how it comes to these decisions has increased.

Explainable AI allows stakeholders and customers to increase trust in banking and insurance. Artificial intelligence that has been built to explain its goal, justification, and decision-making process to the typical person is known as explainable AI (XAI). Implementing Explainable AI into financial services is critical, with regulations increasing and customer trust decreasing because of fraud and cybersecurity issues.

How AI helps with Fraud Detection

Identity theft and fraud in the financial sector are major concerns for almost every business. With the rise of online consumer shopping, the number and types of online fraud have increased tenfold. According to McAfee, cybercrime and financial fraud are presently costing the global economy 600 billion dollars each year. Implementing efficient anti-fraud solutions in its processes has become an inevitable task for any business and with the exponential growth of digital customer transaction data, traditional rule-based fraud detection models are increasingly struggling to meet demand. AI can augment existing rule-based models and significantly help human fraud analysts, improving efficiency while reducing costs.

Financial services can use AI and their access to large amounts of customer data to predict patterns and look for irregularities in customers’ habits and can save the bank and the client thousands by detecting and recognising identity theft and other typical frauds used by criminals to compromise financial institutions.

Register for the AI in Finance Summit.

If you would like to learn more about these topics and other trends in AI, join us at the AI in Finance Summit in London, on the 25-26 April 2023. Bringing together an audience across the banking, financial services, and insurance sectors to explore the latest advancements in AI and machine learning, and how these can be applied successfully. At the Summit, you will hear from Ronan, Luke, Giorgios and many more experts so join them here. Download the brochure or  Get your ticket today!