The Banking Industry has evolved with the digital revolution, due to which there has been tremendous growth in the dynamics of Analytics and AI. Analytics can be used for improving the customer base and for optimizing the costs.
There's plenty of big data in every industry, especially banking and financial services. Banks are obliged to collect, analyze, and store massive amounts of data. But rather than viewing this as just a compliance exercise, machine learning and data science tools can transform this into a possibility to learn more about their clients to drive new revenue opportunities.
Types of Banking Analytics:
The applications for data analytics in banking are endless. Data can be analyzed in real time and make business decisions accordingly. Broadly, they can be segmented as below:
Few applications of data analytics in banking discussed in detail:
1. Customer Segmentation
Based on a customer’s historical data regarding the customer spending patterns, banks can segment the customers according to the income, expenditure, the risk taken etc. Cross-selling can be personalized based on this segmentation.
It’s important to differentiate between the customers that make you money and the customers that lose you money. By understanding the profitability of certain groups of customers banks can also analyze each group and extract useful insights. To grow wallet share and create more loyal affluent customers, banks need to concentrate on selling the right product to the right customer.
2. Fraud management & prevention
Knowing the usual spending patterns of an individual helps raise a red flag if something outrageous happens. If there is a sudden increase in the expenditure of a cautious customer, this might mean the card was stolen and used by fraudsters. Analyzing these types of transactions in real time helps cut down the risk of fraudulent actions greatly.
The key steps to fraud detection include:
● Obtaining data samplings for model estimation and preliminary testing
● Model estimation
● Testing stage and deployment.
3. Risk modeling
Risk assessment is of high priority for banks, as it helps to regulate financial activities and in the pricing of financial investments. The financial health of a company can be assessed for corporate financing, facilitating in mergers and acquisitions and for investment purposes
Similarly screening a candidate for a loan by understanding the spending patterns and previous credit history, can help rapidly assess the risks of issuing a loan.
4. Identifying the main channels of transactions (ATM withdrawal, credit/debit card payments)
Banks can track the past usage patterns and the daily coordination between the in- and out-payments at their branches and ATM’s, hence predicting the future needs of their potential customers. This also leads to the optimal management of the liquid assets which can result in extra income and can help obtain an overview of future changes in investment and liquidity options.
5. Customer Lifetime Value (LTV)
Customer's lifetime value is how long the organizations are able to retain their customers. Identifying who the best customers are, making them better in different ways, and once you win them over, securing their loyalty, are a few areas that banks are focusing.
Predictive analytics helps:
● Know which customers should be the focus of new customer engagement efforts.
● Identify the previous factors that enhanced returns on customer engagements in the past.
● Use that knowledge to understand why customers responded to certain messages and promotions.
6. Feedback management
Feedback management is really important. Predictive analytics allows banks and financial firms to keep up their relationship with the customers by giving them the right services and products for their needs and matching individual preferences in the most sorted way.
To gain competitive advantage, banks must acknowledge the crucial importance of data science, integrate it in their decision-making process, and develop strategies based on the actionable insights from their client’s data.