Where can Machine Learning be Applied to Improve Banking Performance?
Machine Learning is an application of Artificial Intelligence that allows computers to learn without being explicitly programmed to do so. It’s the product of established statistical theory and more recent developments in computing power. Combined, the Machine Learning algorithms offer businesses the opportunity to transform their operations and the services they provide.
These algorithms are categorised as being supervised or unsupervised. They learn by analysing very large volumes of quantitative and qualitative historical data, looking for patterns and trends across hundreds of variables at the highest speeds – something a human could never achieve alone. In a supervised learning case, knowledge gained is applied to new data in order to make predictions and therefore better plan for the future. In the unsupervised case, it’s used to describe the data’s “hidden structure,” such as for customer segmentation or anomaly detection purposes.
Where and how can it be applied?
Given developments in Natural Language Processing (NLP) and voice recognition, Machine Learning can be used in back office operations as well as communicating with customers and clients. Below are some of the key areas banks could benefit from by applying Machine Learning:
Traditional fraud systems identify fraudulent transactions based on specified, non-personalised rules, such as if a customer spends money abroad. Machine Learning systems, on the other hand, analyse large amounts of each customer’s transactions to understand their personal spending patterns. This way, they can spot subtle anomalies that indicate potential fraud. Each transaction is automatically analysed in real-time, and is given a fraud score which represents the probability that it is fraudulent. If it is above a certain threshold, a rejection is triggered immediately. This would be extremely difficult without Machine Learning techniques– as a human could not review thousands of data points in milliseconds and make a decision.
The current credit risk workflow tends to be labour intensive, slow and riddled with judgement related human-errors.
