Analytics in Banking

analytics in banking

Analytics in Banking:

Banks day to day generate huge data due to n number of transactions. To get insights out of this data banks have started using Analytics long time back. These are some of the following work in which bank is using Analytics widely:

  1. Fraud Detection and Money Laundering

Banks are using Analytics to detect fraudulent transactions and protect themselves and their clients from fraud and gain their confidence. Central Bank Such as RBI is using Analytics to identify Money Laundering.

  1. Credit Card Data Analysis

Credit card transactions generate huge amount of data. These transactions help back end analytics team to identify financial habits of a customer. Based on this promotional offers and other financial products can be pitched to the customer.

Gone are those days when credit cards were sold to any Tom Dick and Harry. Now before issuing any credit card lot of analysis on potential customer’s data is done. Initially based on their Salary credit limit is set but later based on every month credit card transactions credit limit is increased or decreased. It also helps credit card provider to manage risk of default payment.

  1. Trend Analysis of Customer usage for cross selling and up selling

Depending on customer’s transactions analytics team can identify customer’s buying behavior, paying capability, needs and requirements etc. Based on this different products can be proposed to sales team for cross selling or up selling to the customer. Campaign management, performance and evaluation of these campaigns are done based on revenue generated via customers after roll out of these campaigns.

  1. Managing customer retention

Banks are facing cut throat competition in Market and to retain an existing customer it has become very challenging as well as important. Analytics has helped banks to tackle such challenges by understanding which customers are likely to leave first in future. In such cases customer’s needs and requirements needs to be addressed on time with different offers and products and handle grievances if any.

  1. Managing Loan, NPA Management, Credit Scoring

World still cannot forget sub-prime crisis and housing bubble burst happened in US in 2007-08 followed by deep recession. The root cause of this financial crisis was giving loans by banks to borrowers with more risk of being defaulters (sub-prime borrowers). As banks were not able to recover their money, many banks were left with no cash which lead to filing of Bankruptcy. Lehman Brothers is such an example .Post these events need emerged for doing proper analysis of borrowers, credit scoring, making sure NPA (non-performing assets) are as low as possible. Now Banks are leveraging on Analytics to perform these tasks.

About the Author

Dyuti is an Analytics Enthusiast. She is an MBA in Finance and B.E in Computer Science. She has years of experience in the field of Analytics and is also the Co-founder, CEO, Nikhil Analytics. She has prior worked with companies like HCL Technologies, Deutsche Bank ,WNS, Reliance Capital etc.

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