How AI and ML can drive resiliency for banks and customers

For banks, artificial intelligence (AI) and machine learning (ML) should be more than just buzzwords. Correct application of these technologies to their back-end processes can help facilitate increased operational resiliency, in the process enabling customers to become more financially resilient in an era of widespread economic shocks and increased financial crime. But how can they do so effectively?

AI in its simplest form involves the use of computers to complete tasks, such as data analysis, which would take humans hours or even days to do. The aim of AI is to recognise patterns in data sets and decide next best steps. It can make sound judgments, like humans, but does this almost instantaneously, unlike humans. Further, ML can be considered a sub-section of AI, which focuses on using data and algorithms to mimic the way humans learn. This, in turn, improves the accuracy of ML and therefore AI.

Banks and other financial institutions implementing AI and ML is not a new phenomenon. According to research from the Bank of England, the number of UK financial service firms using ML has continued on an upward trajectory throughout 2022 with 72 percent of those polled either currently using ML, or developing ML applications. The influence of ML is also predicted to more than triple over the next three years where financial applications is concerned, including in banking.  

ML and AI to improve operational resiliency

Banks should not be put off with the prospect of deploying and implementing AI and ML into their backend systems, even if they have legacy systems in place. By developing and implementing the appropriate AI-based technologies in conjunction with automation efforts, financial institutions can easily connect both together to drive enhanced, up-to-date financial applications. These functions can then collaborate with each other in harmony, rather than it being a fight of one over the other.

One of the biggest worries for financial institutions and their customers is financial crime and fraud. AI and ML can help here by facilitating enhanced risk detection and management through connecting case management tools with current fraud screening methods already in play. As part of an intelligent automation approach, AI and ML tools can also help banks more efficiently screen transactions for anomalies to improve detection and management of financial crime. This ensures security and protection of banks due to an encouragement of resilient operational processes, creating a strong backbone for their backend systems. 

There are also financial benefits to deploying AI and ML. Banks can take advantage of AI powered enhanced backend system automation, which overcomes the repetitive, but necessary manual work employees would face without these processes in place. By automating these tasks, employees can focus on the more complex needs which may require a human touch or empathy. This can be done by implementing a best-of-breed AI system, which marries legacy systems with investments in newer technologies. This allows banks to effectively cut operational costs as well as increasing workflow efficiency. Some banks have achieved impressive straight through processing of between 50 to 85 percent for complex detection and resolution processes. They have also been able critically to get the balance right between what to automate and what not to.

However, when banks implement AI and ML, they must be wary about managing ethical issues related to these technologies. In order to overcome bias risks, they must establish a strong ethical code to promote non-discriminatory practices for their customers. This can be achieved by training models with diverse data, and monitoring outputs on an ongoing basis to ensure the AI operates as intended. It can also be achieved through working with solutions that have AI that is clearly explainable.

Promoting financial resiliency for customers using AI and ML

AI and ML have become some of the most valuable assets for banks when servicing their customers. As handling customer data is improved through automation, customers are served faster and more accurately. By deploying AI, banks are able to recommend not just the products and services each customer needs at the right time, but the right next best interaction that customer needs. This can help lead to improved financial resiliency for customers as they will receive the help they need from their bank when they need it the most. 

Combined AI and ML powered automation technologies will drive the future of financial services innovation. Acting as the ‘brain’ of any financial institution, they will increasingly provide banks with the ability to harness the power of advanced data analytics to combat challenges and drive a better standard of customer service, which is key to ensure operational resiliency for themselves. Subsequently, banks can pass these benefits on to their customers to drive financial resiliency through more personalised products and services. The future is not just approaching quickly, it’s already here, with easy-to-use solutions to apply in this space – time to get on board.