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 as well as to 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 over time in an autonomous fashion, 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 which are 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.
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 world of banking to help solve specific problems, such as money laundering and fraud, as well as helping with the accuracy and efficiency of credit application. By combing through large sets of data 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 to identify any patterns which show signs of nefarious activity (such as money laundering), upon which these patterns are shared with the relevant agencies in order to track down those responsible.
Chatbots are another way of applying machine learning in banking, chatbots have the ability to understand 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 member of staff. Investment research is the process of building virtual agents that can perform investment research to near-human levels, they can go through the likes of SEC filings and market data, even doing company validation using the same inputs that a human analyst would receive.
We are living in a constantly changing world and we must adapt to it. Machine learning is one of the ways banks can improve innovation and stay ahead of the emerging digitally-advanced start-ups.