According to Ovik Mkrtchyan, corporate consultant to the Gor Investment Limited group of companies, the modern banking system not only operates with finances but also owns a huge amount of data about customers and their consumer behaviour. The processing of such an enormous amount of information demands the use of new technologies to handle it all, the most potentially productive of which is artificial intelligence.
Today, AI is already widely used for customer service improvement. Thanks to the technologies used in voice assistants and chatbots, it is possible to increase the speed of customer service and improve employee productivity. Artificial intelligence also helps to combat fraudulent activities through the introduction of biometric analysis, which identifies client users by voice, appearance and behaviour.
Sharing his own experience in AI technology, Ovik Mkrtchyan feels its wider use is a necessary step to optimise financial business processes. He comments: “The use of artificial intelligence as part of the bank’s digital ecosystem can bring strong dividends to organisations, increase profits and help them succeed over competitors by providing next-generation financial services.”
One of the most promising and indeed priority areas for AI algorithms being applied in the banking sector is the construction of reliable scoring models used to assess the creditworthiness and reliability of applicants. Monitoring customer data has previously been the basis of funds issue decisions but has failed to create a reliable and accurate picture due to a lack of metrics and technological capacity.
Ovik Mkrtchyan’s opinion
Ovik Mkrtchyan says: “The main problem of traditional scoring is that it only relies on credit history, which of course excludes the assessment of first-time borrowers, and the low predictive power of the model used. AI technology, on the other hand, allows not only to obtain detailed data on consumer habits but also to build long-term forecasts of customer behaviour.”
Thanks to AI, banks will be able to evaluate both global financial flows and daily spending, while taking into account the cost and quality of purchased goods. In addition, neural network models lock into alternative data sources – things like similar user behaviour, previous historical cycles, and other geographic and macro-economic indicators.
Adds the expert: “We are implementing modern nonlinear machine learning models that have already proved themselves in the banking systems of other countries. They provide a more accurate assessment of creditworthiness, together with additional predictive power by working quickly with a large amount of anonymised data whilst guaranteeing client security. The use of AI in the banking sector right now demonstrates lower levels of defaults and reduced operating costs. These taken together are opening up a great opportunity for lowering interest rates and ensuring revenue growth.”
Today, the use of AI platforms in the financial sector is a logical step towards improving the work of banking organisations, allowing them to better understand customer needs, improve the quality of service, provide next-generation personalised services and of course increase revenues.