Imagine you take out several loans from different sources. A good data scientist will be able to flag you as a good target for a credit consolidation offer. Reversely, this might also work against you, highlighting your high credit risk.
Nowadays, using Data Science in Banking is a necessity, not a luxury. Either a bank will benefit from the data it has to move forward, or it will outperform other banks and stay behind them.
5 main advantages of Data Science in Banking:
- Fraud detection:
Security is a service every bank should provide for its customers, especially that they are trusting it on their money, hence detecting and preventing frauds, banks worst nightmare.
Unusual high transactions put on hold until calling and receiving the client’s confirmation is as good for their client and the safety of its account/money, as for the bank and its reputation.
False positives may be an issue in this case, but it still worth locking an account by mistake and apologizing to the customer afterward than not blocking a fraud.
Interacting with an ITM, avoiding the line and using the ATM to deposit cash and cheques, being assisted by a chatbot at night when no branches are working, etc. are the best innovations a bank may have done.
Customer support and satisfaction:
Talking to strange people is not always fun. But when having full information and profile data about a client while assisting him in his request/complaint, he will be no more a stranger to the customer service team member supporting. The supporter may even come up with new suggestions. The employee is at ease, and the customer is satisfied.
Financial risk modeling:
In addition to measuring unfavorable and unforeseen events affecting the bank, stress testing is now being applied in most major banks, and tools like R and Python are being used to check on the financial health.
Predicting whether a customer is risky or not before agreeing on the loan is a must to avoid losses.
Knowing the customer:
Like any other business, collecting and analyzing customers data is an added value, especially for the bank having millions of information about each customer. Internet banking, social media, and mobile phone usage for banking have opened the floodgates of data flowing into the banking system.
Analyzing the behavior and interactions of the client to suggest new services and offers based on demand, predicting what the customers will want in the future and acting accordingly, assessing data from a new product or service to predict which demographic will use it, and personalized marketing and specific offers for specific clients based on their need after analyzing their data (behavioral, demographic, and historical purchase) are real-life scenarios a bank can do to both enhance its services and reputation, and increase its profit.
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