The financial sector needs AI

How can artificial intelligence (AI) support banking services, and are banks looking at it favourably, are two of the questions that Insider Intelligence’s AI in Banking report sets out to answer. In response to the second of those questions, it appears that 80% of banks are highly aware of the potential benefits presented by AI.

The scope of possible uses for AI and machine learning in finance stretches across business functions and sectors. At present, the technology is being widely used in upgrading customer services, looking at new ways of segmenting clients in order to offer more bespoke products, as well as fraud prevention and loan assessment, and there are many more opportunities to expand it.

In customer services, banks now use AI-based chatbots in order to provide customer services and support on a 24/7 basis. The bots, as many of you have probably experienced, have been ‘taught’ to answer basic customer questions via an instant messenger interface. They are able to provide fast and relevant information and support to each user and drive tailored interactions, and the more sophisticated the chatbots become, it is anticipated that customer satisfaction will rise in tandem.

Client segmentation is an interesting one. It divides bank customers into groups based on common characteristics, such as demographics or behaviours. Here, AI can seek out patterns within client data quickly and on a huge scale, creating outputs that would otherwise be unachievable through manual means, or at least would take an exceedingly long time to process if humans were performing the task.

In loan assessment and fraud prevention, AI also uses its pattern recognition skills to search out irregular transactions that would otherwise go unnoticed by humans but may indicate the presence of fraud. In this respect, AI is a great tool for banks to assess loan risks, detect and prevent payments fraud and improve processes for anti-money laundering. For example, Mastercard’s Identity Check solution developed in Dublin, uses machine learning capabilities to verify more than 150 variables as part of the transaction process to help reduce fraud, thus giving merchants more confidence.

These examples are just the beginning of how AI can benefit finance, although it is important to consider that with increased use, it is imperative that controls on how AI is set up and applied are put in place to ensure systems are robust, fair and safe. For example, an AI tool that has not received the necessary guidance and proper training can output responses that lead to unknowingly biased decisions, with potentially damaging consequences.

It is essential that responsible governance of solutions plays an important role in the successful deployment of AI. It is only by keeping models tight to their tasks and free of bias and error that banks can be sure of the best results for all.

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