The Adoption of Artificial Intelligence and Machine Learning in Fintech
There is hardly any aspect of business today that is untouched by technology. And when it comes to technology, the whole world is in raptures about Artificial Intelligence, using it in diverse ways, ranging from error minimization, pipeline generation, customer service, backend operations and so many more. For companies that provide computer programs and other applications for banks and financial institutions, Artificial Intelligence has opened up several avenues to sharpen their operations and offerings. There are several aspects of banking operations that can be optimized by AI applications, but let’s limit ourselves to three important ones and examine how artificial intelligence is impacting each of them – we will focus on Investment Advisory, Risk Management and Customer Service.
The first application of Machine Learning is in undertaking recurring transactions. Let’s take the example of a trading firm which buys and sells stocks for itself or on behalf of its clients. Its systems can be set up to place a buy order or a sell order when a particular stock reaches a predetermined price. This is a fairly straightforward transaction, but when Machine Learning components are introduced, what the system does is to plough through millions of such transactional data points to come up with a predictive algorithm. This would take into account the history of a particular stock and also the general response of stocks to certain external indicators like political or corporate events. As the system keeps crunching more and more data and continues to learn from data, it would possibly become easy for it to predict stock or fund movements in advance.
Risk Management and Fraud Prevention
The biggest advantage of applying Artificial Intelligence and Machine Learning to the prevention of fraud and the management of risk is that no human judgement or discretion is involved. This usually dilutes risk management and fraud detection. Let us look at fraud prevention first. Often frauds are perpetrated by a group of people working from multiple locations and carrying out multiple transactions on the same day, or even within a couple of hours. For a particular office or branch, the entire chain of transactions might be impossible to take cognizance of and make out a potential fraud build-up. But Machine Learning can detect such patterns based on the basis of a study of past frauds and flag off those transactions in time. Even if a big fraud is not being planned, regular vetting of transactions can also be done efficiently to free up employees for more productive tasks. Risk management for credit appraisal is a complex set of calculations from financial reports and other static data. Artificial Intelligence can add more depth to that assessment by factoring in real-time dynamic data.
Customer Service as a function started with customers having to walk into their branch to have their queries resolved. Then we moved on to phone-based query resolution setups, but many customers found it comfortable to speak to a voice rather than a human. The introduction of machine learning has meant that the phone service can become more effective and the customer doesn’t have to wait till a human comes on to the line. Patterns of earlier queries, access to large amounts of data, and the potential to carry out calculations and searches much faster have meant that Artificial Intelligence can actually provide useful answers most of the times, rather than a sterile standardized answer. This has been augmented by the introduction of digital assistants who are able to mimic a human customer service employee and come up with answers that are relevant and useful. We are fast moving towards the day when fintech will utilize artificial intelligence almost completely to handle customer service.