In 2004, HDFC Bank in India started thinking about investing in analytics with an aim to revolutionize the banking sector in India. They could use it to identify who was an ‘active’ customer, who was a ‘long-term’ customer, and, who was just having an account. The offers were sent out accordingly, so were the add-on benefits, and all these, in turn, helped them strengthen their framework and, more importantly, the trust the customers had in them. In June 2014, the NPA for HDFC bank stood at 1.1%, one of the lowest achieved in Indian banking. The investment in AI and, specifically, machine learning, had got them rich benefits. The results were there to be seen by everyone. Other banks have followed the practice, and absolutely none could complain that investing and implementing data science have not worked out well for them.
Let’s look at some of the areas in which data science has intruded and made massive positive changes:
Customer Lifetime Value
Customer Lifetime Value (CLV) helps the banks assess the customers they have. It gives predictive value to all the business the financial institutions will derive throughout the lifetime of a customer. It involves a rigorous process of data cleaning and manipulation. Then there’s often segmentation and profiling before the final model can be fit to generate the desired outcomes.
Customer Segmentation based on multiple criteria not only can help build models on the segmented data but can also in itself provide insights and trends regarding customer data, which often is used to target the right customers with the right plans and benefits.AI Methods like K-means clustering, random forest and decision trees can be used to get the meaningful insights.
You must have got those offers via phone banking? And, perhaps, you have even opted for that life-term insurance plan? Well, nothing to worry about. The bank has built a robust customer support system and, with the help of AI, has been able to monitor and explore all your data and investment patterns in a very efficient manner. Automation by means of macros and arrays have helped it to identify different plans and offers for each of its customers.
The banks and such financial institutions are wary about fraudulent activities that can affect them adversely by making them incur huge loses. Analytics has evolved to become a separate branch in itself and often involves robust regression model building and forecasting techniques.
Banks and other financial institutions have deemed risk modelling a mandatory and integral part of assessing their overall performance. Stress testing is being done in most of the major banks around the world and tools like SAS, R and Python are often used to carry out the check on the financial health of the institutions.