Machine learning in banking

Over recent years, there has been a significant increase in the application of machine learning to improve the efficiency and management of many banking infrastructures and provide new opportunities. There are many different ways machine learning can be applied to banking, such as trading, real-time transaction analysis, anti-money laundering, personalised recommendations, credit application, chatbots and investment research.

What is machine learning?

According to TechEmergence, machine learning is the science of getting computers to learn and act like humans do, improving their learning autonomously by feeding them data and information in the form of observations and real-world interactions.

Machine learning’s history and future

Machine learning is now crucial for companies trying to get an edge over the competition by predicting the trends and patterns most likely to yield profits. There have been dangers of using machine learning for trading purposes; algorithms led (partly) to the black Monday crash in 1987; however, as the technology improves, this has become less likely, and AI has become even more essential. The real-time tracking of transactions has been a problem for banks with a large amount of legacy IT infrastructure for a long time. Getting the data to a place where it is at low latency could be very beneficial for legacy banks. Using machine learning in this way initially would mean banks could offer more personalised products to customers later down the line as the AI learns about their spending habits over time.

Customer relations

Machine learning could also aid a customer-centric business strategy through personalised recommendations. The aim is to have an AI system that can help the bank create and recommend better banking products to customers on a more personalised basis.

Problem-solving and crime prevention

Machine learning could also be used in the banking world to help solve specific problems, such as money laundering and fraud, as well as help with the accuracy and efficiency of credit applications. By combing through large sets of data and looking for any unusual behaviour, banks can spot potential fraud before it happens, rather than having to deal with the aftermath of fraud, marking a large step forward in the realm of banking security. Similarly, analysing data using machine learning can help identify any patterns showing signs of nefarious activity (such as money laundering), upon which these patterns are shared with the relevant agencies to track down those responsible.

Chatbots

Chatbots are another way of applying machine learning in banking; chatbots can understand the context and are designed to answer questions posed by customers, mimicking human language as closely as possible, as well as performing simple banking tasks such as money transferring. If the chatbot can’t answer a user’s question, it will pass the customer to a staff member. Investment research is the process of building virtual agents that can perform investment research to near-human levels; they can go through SEC filings and market data, even doing company validation using the same inputs that a human analyst would receive.

We live in a constantly changing world and must adapt to it. Machine learning is one of the ways banks can improve innovation and stay ahead of emerging digitally-advanced start-ups.