Over recent years there has been a significant increase in investment in banks towards the application of machine learning in order to improve the efficiency and management of many banking infrastructures as well as in the hope to provide new opportunities. There are many different applications of banking in regards to machine learning such as; trading, real-time transaction analysis, anti-money laundering, personalised recommendations, credit application, Chatbots and investment research.
According to TechEmergence:
Machine Learning is the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-world interactions.
Machine learning is now crucial in trying to get an edge over the competition by combing through vast amounts of data in order to predict trends and patterns which are most likely to yield profits. There have been dangers to this form of trading, algorithms led (partly) to the black Monday crash in 1987, however as the technology improves this has become less likely and the 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 and the ability to get the data into a place where they can be at low latency could be very beneficial. Using machine learning to do this set the ground work in order to apply further AI and deep learning which would mean the banks could offer more personalized products to customers as the AI learns about their spending habits over time.
Regarding personal relationships with customers this is something machine learning could further improve through personal recommendations. The basis of this would be to tailor specific algorithms to the individual, rather than on the function of selling general products. Headed by those within the bank responsibly for inventing and maintaining products and services for customers the ultimate aim is to have an AI system that can help the bank create and recommend better banking products to customers on a more personalized basis. An example of this application would be companies like Trussle using machine learning to find customers the best mortgage product.
Machine learning could also be used in the world of banking to help solve specific problems, specifically money laundering and fraud, but 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 situation after fraud has already occurred marking a large step forward in the realm of banking security. Similarly analysing this data using machine learning can identify any patterns which show signs of nefarious activity (for money laundering), upon which these patterns are shared with the relevant agencies in order to track down those responsible. Regarding credit application, by looking at a client’s age, credit score, postcode and more an AI can make a decision on whether an applicant is acceptable for credit with the belief that by being completely impartial the AI is likely to provide the most accurate results.
Other applications of machine learning in banking are the likes of Chatbots which 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 upon which it can return text similar but not yet identical to human language.
To sum up
We are living in a constantly changing world and we must adapt to it. Machine learning is one of the ways banks can go with the flow of innovation and there are numbers of reasons why they should.