banking

Contributed by: Prashanth A
LinkedIn Profile: https://www.linkedin.com/in/prashanth-a-bb122425/

The use of data analytics in the banking industry started after the global financial crisis of 2008, where the industry is data-intensive with typically massive graveyards of unused and unappreciated ATM and credit processing data. The banks understood the need for risk management thereby leading to the usage of all these data to identify customer predictions, fraud detections, and financial advisories to identify and predict market trends. With the help of advanced data science techniques banks became better equipped to manage market uncertainty, minimize fraud, and control exposure risk. 

But to discover the set of critical success factors that will help banks reach their strategic goals, they need to move beyond standard business reporting and sales forecasting. By applying data mining and predictive analytics to extract actionable intelligent insights and quantifiable predictions, banks can gain insights that encompass all types of customer behavior, including channel transactions, account opening, and closing, default, fraud and customer departure.

Insights about these behaviors can be uncovered through multivariate descriptive analytics, as well as through predictive analytics, such as the assignment of credit score. Banking analytics, or applications of data mining in the industry, can help improve how banks segment, target, acquire, and retain customers.

Application of Data Science in Banking

Fraud Detection

Fraud detection is now becoming a critical activity to safeguard the interest of the customers and employees. Since this is a highly regulated industry, there are several external compliance requirements that banks must adhere to combat fraudulent and criminal activity.

Some of the techniques that banks undertake to check on fraudulent activities are:

  1. Keep a tab of inflows and outflows of all transactions by adopting statistical methods like variance calculations, deviations in transactional value, or number of transactions.
  2. Check on duplicate entries if any in an account or as the bank on the whole.
  3. Cross validating entries of account numbers and names with criminal records.

Customer Segmentation Analytics

There is always a risk of losing customers because of bad services. To reduce this risk, banks have adopted data science techniques to group them into different sectors, based on their business and transactional records. A popular method that is used here is the K-means algorithm. Based on these segments, banks can adopt different marketing strategies and services like providing better loan rates, waiver of annual fees, providing locker facilities at subsidized rates, providing prioritized treatments, zero account balance options, and many more.

Also Read: Design Thinking in the banking industry

Prediction of Customer Lifetime Value (CLV)

Banks need to predict future revenues based on inputs from the past. This is best done using predictive data analytics to calculate the future values of each customer. This helps in segregating customers, identifying the ones with high future value, and investing more resources on them in terms of customer service, offers, and discounted pricing. The primary data science tools used for this purpose are Generalized Linear Models (GLM) and Classification and Regression Trees (CART).

Risk Modelling 

Risk prediction has become a primary concern for banks with risky credit products as well as investment banks. 

1. Credit risk modeling 

This allows banks to predict how their loans are going to be repaid and to foresee a defaulter based on history and credit report. The risk modeling calculates a risk value for each case and the Credits Team only sanctions loans based on this Score.  

2. Investment Risk modeling 

Risk modeling is also used in investment banking, wherein risk-rewards ratios are calculated for risky investments. This helps in giving investment advice to customers as well as making the right decision in internal investment to generate profits for a fund.

Recommendation Systems for promotional products

Based on the historical data, transaction information, and personal data, a bank provides real-time recommendations of entertainment options to customers by providing promotional offers or discounts. Such information is fed into data science algorithms to predict the customers’ interest and relevant promotional options are provided.

The industry has also ventured into advanced Artificial Intelligence services by providing AI bot services for personalized banking at branches for people to meet their basic needs. HDFC bank was a pioneer in providing these services. 

In the case of digital services in this industry, virtual AI options are provided to customers for query handling and immediate resolution of any banking requirement. Data science is a growing option in the banking industry with more data being collected to cater to different needs of the customers as well as to increase the market value of banks.

If you found this interesting and wish to learn more about Data Science, you can upskill with Great Learning’s PGP- Data Science and Engineering Course or M.Tech in Data Science and Machine Learning today!

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