04 Ways banks should leverage Data Science

As we go deeper into the fourth industrial revolution ,we see fintech and other digital solutions taking over traditional offline solutions. With all these transactions passing though the banks,there is a laot of data that banks already have that are currently under utilised.Many banks still collect and exploit data strictly to sataisfy regulatory requirements and produce reports requiredby the Central Bank or the government only.

There is a lot of data science applications relevant to the banking industry.Below is a list of the top 4 opportunities banks can seize while leveraging data science, even without upgrading their current data infrastructure.

1.Market Segmentation

With the data banks currently have about customers, a good segmentation of the customer based can be done easily. Based on the age, sex, expenditure pattern, risk preference, etc clear segments with specific needs could be identified. This intend will help in developing relevant products which add value per segment, and as a result there will be an increase in average spending per user. Another important benefit is when these segments see that the bank “understands” their needs and seeks to satisfy them and offer them and attractive value for money, the brand affinity and loyalty will also be greatly enhanced.

Also cross selling and upselling will be facilitated when a clear segmentation of the customer base has been effected. Data science tools such as K-means clustering and H-chustering will come in handy with this segmentation.

Banks need to sell the right product to the right customer.

2. Fraud Prevention

Banks are very attractive for hackers and fraudsters as that is where money is stored.Each day banks are fighting to prevent fraudsters from coming in and causing customers to loose confidence in the bank’s security system.

 Data science helps establish the normal pattern of a customer after “observing” the customer’s behaviour over a period of time. This is usually a real time model which sends a signal if for example,the model notices a sudden high expenditure from the account of a customer who usually does not spend that much on average per transaction.

The article explains more detailing how data scientists will use fraud detection models in a bank.


3. Credit Risk Management

The major relationship between the bank and the Regulation is for the bank’s risk to be assessed regularly and a certain cash reserve ratio is determined and kept with the Central bank,for example. The higher the risk calculated for the bank,the more the cash reserve required by regulation.

The bank is interested in reducing this cash reserve so the bank can have more available cash to lend out with interests and make more money.So managing risk wo as to minimise this cash reserve is very critical for a bank.

Secondly, banks lose a lot of money when they grant loans to customers who are not credit worthy.

With the use of data science, models can be built to calculate customer credit worthiness and hence predict the probability that the loan applicant will pay the loan in future.That way the bank can reduce the credit risk and also the overall bank risk.

The article explains in more detail how data scientists can predict customer credit wordiness to help manage credit risk in a bank.


4.Customer Lifetime Value Management

Customer lifetime value is a key CRM statistics to measure customer retention.

It is 10 times more expensive to get a new customer than to retain an existing customer.The longer the Customer lifetime the better for the bank who will earn more revenue cumulated throughout the lifetime of the customer.

Data analytics has many tools which will help identify who the best customers are, how to increase their value ( or average spending per user) and retain them so they do not see any need to go to the competition.

With the use of predictive analytics, the bank will be able to determine the most effective method of engaging new customers and shape future campaigns and product development to satisfy the optimum acquisition and retention objectives.

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