The banking sector in India has gone through little or no change at all. With the ambitious plan to digitize India, it is high time that the banking sector also undergoes a shift into digitization.
The use of Paytm and Google pay as a means of payment has increased tremendously. The shift to mobile banking is seen everywhere and the banking sector has slowly started to adopt the digitalization trend.
Analytics in Banking
The amount of bad debts in the banking sector is still very large in India. This has caused many banks to go through the financial crisis. The most recent example is YES bank. It was absorbed into the State Bank of India and was saved. But there are talks in the government that this practice is going to change very soon. Banks on which moratorium has been declared will be sold out and not saved. This brings the importance of adopting analytics and making banks more efficient in order to avoid bad debts.
How Can Predictive Analytics be used in banking?
Analytics as we know is the use of data to find meaning information that can be used to the advantage of the organization. In banking analytics ie predictive analytics can be used to the advantage of the organization. The CIBIL score that is used by banks while giving us a loan is one of the uses of predictive analytics. Even though our CIBIL score maybe green there might be the possibility for any customer to become loan defaulters. This is avoided by using predictive analytics. Also, the value of an asset can also be determined using analytics. By collecting the historical price of that particular asset and then comparing it with the current price, one can predict if the asset will increase in value or decrease in value. Such information can be used to the advantage of the bank and can be used to reduce the probability of NPA (Non-Performing Assets).C
By using predictive analytics banks can reduce the risk of Non-performing assets and loan defaulters. Thus helping banks avoid moratorium and provide a healthier and stronger banking industry.