I was facing a cash crunch in this month so decided to promote my saving henceforth started making deposits in the bank. With excitement and enthusiasm finally started with my 1st deposit of Rs 4000 and keep on depositing the same amount for 2 months than in 3rd Month some penny was less so deposited only Rs 3000 then the same continued for 4 months than in 7th month I wanted to ignore and thought who will go and deposit it but still made my mind hard and deposited Rs 2000 but after a year completely ignored it and no deposits at all.
Same happened with my friend in case of loan taken by him. He repaid first 6 installments but later he did not repay so that became NPA for the bank.
In this scenario, banks could have made some predictions that why deposits are not coming regularly and loan turned into NPA. Why banks were not able to retain customer?? Was customer relationship management not proper or fraud detection not made?? What was the actual reason?
Is predictive analysis a right answer to solve the above problem…? Let’s see!!!
Predictive analytics is use by many organizations as a competitive advantage over others. It is an area of statistical analysis that deals with extracting information from data and using it to predict future trends and behavior patterns. The core of predictive analytics depends upon capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting it to predict future outcomes.
What is the PROCESS for “predictive analysis”?
1. Collection of Current Data:
Define the project outcomes, the scope of work done, business goal and identify the data sets which will be used then keeping these things in mind collect the data from numerous sources. It will show how keenly customers are integrated with the data.
2. Analysis and Statistics:
Analysis is the process of inspecting, modifying, modeling data with the object of getting new and useful information and deriving end results. Whereas statistics deal with validating assumptions, hypothesis and test them using statistic analyzing tools and practice.
3. Modeling of Data:
Predictive modeling provides automatically create predictive models about future and one can choose the best option among various alternative with multi-model evaluation.
4. Deployment of Data:
To deploy and monitor the analytical results in day to day decision thereby reducing complexity and get more viable results and report by automating the conclusions based on modeling.
It’s “now or never” for banking to work on predictive analytical function. The banking sector is active in performing the analytics on various parameters.
All these parameters can be categorized under 3 major head-
- Customer Analytics
- Marketing strategy
- Risk and credit management
Analytics is a never ending process so more fields need to be discovered very soon as one competitor can take over the other in no defined time frame.
- Understanding customer behavior
- Debt collection and recovery models for profitability
- Understanding risk profile of customer
- Cross sell of various financial products
- Understand the customer sentiments
The growth of bank depends on its customer. The bank is rich only when its customer acquisition, retention and customer service satisfaction is rich. Various banks have started using predictive analytics to take care their business grow and stay in the market. Let’s check the same.
ICICI Bank improved collection efficiency by developing a centralized debtors’ allocation model to apportion erroneous cases to the right channels till the last mile.
KOTAK MAHINDRA BANK
Kotak Mahindra Bank’s analytics platform extracts data from the core banking system and the relationship management system then collects all the data and puts in place algorithms which are working on certain thresholds. Every time there is a breach of that threshold it generates an event for them.
Bank also used advanced analytics to classify dormant accounts into different groups and focus reviving efforts on customers who are more likely to reactivate their accounts
- Measure marketing strategies
- Campaign Analytics
- Building relationship with customers
- Customer Segmentation
- Next best offer
- Product Pricing Analytics
- Creating loyalty programs
Having in the middle of the battlefield but all weapons are blunt then what one will do on that ground? Banks need to sharpen their weapon tools and actively play in the field. Marketing tool needs to be polished and a result of which can be seen by the move in marketing analytics by some banks.
Singapore Citibank offers client discounts at retailers and restaurants based on the customer transactional patterns. By offering this service, Singapore Citibank has a significant increase in its card usage loyalty, retention and overall enhancement of customer satisfaction
Westpac,a bank operating in Australia and New Zealand with 812,000 customers, are successfully making use of next best offer to drive their cross sales. Using the next best offer methodology, Westpac measures each customers product propensity across their range of products and services. From this information, Westpac is able to subscribe extra banking products to 37% of its customers through its branch staff and 60% to this customer who communicates through its call center staff.
Fifth Third Bank uses analytics-based product pricing engine to help obtain new customers. Using data analytics the bank can track scenarios on how various price points will influence its customer acquisition and deposit levels. For example, the bank can make price predictions when interest rates will go up in the future and make scenarios where it wants to be with rates in the market to be aggressive in attracting customers.
Analytics enabled by HDFC Bank to arrange straight-through processing of personal loans without human intervention and with regards to this, HDFC Bank’s personal loans business has been growing at 25-30%.
Risk and Credit Analytics:
- Analyzing behavioral & credit/risk scorecards optimize cost and capital allocation and employ stress testing methods.
- Manage the operations profitability.
- Managing portfolio to monitor the performance
- Fraud analysis
- Generate alerts which need validation.
AXIS ACL implementation has identified important revenue leakages and over 50 percent of the detected shortfalls have already been recovered. With a focused, ongoing ACL data analysis strategy, AXIS is effectively addressing important operational risks and providing solutions that increase confidence and regulatory compliance
STATE BANK OF INDIA
Using data analytics in Non-Performing Assets (NPA) management. On the basis of the symptoms, from the accounts, which went bad already, Non Performing Assets that will turn bad and the ones that can be recovered – geographically, demographically can be recognized.
NPA has been a problem. “We have reduced our NPA (Non Performing Assets) for the last few quarters,” says the chief general manager of SBI, Mr. Ghose.
There is a very famous quote “BRAND IS BUILT OVER YEARS, but managed over quarters”. Prediction is a never ending process and it goes on and on. Well, what can lending platforms do with this predictive analytics?
To know more about Boost in Banking Operations with Assistance of FinTech Players read this article.