Financial institutions like banks have been using predictive customer analysis for a long time. But as the complexity of loans increased, so did the need for more complex and accurate analysis.
Most of the past loan frauds are correlated to the prediction models that were being used for decades, which were not effective to detect bad loan potentials. But, as the days passed by, the financial risk factors started to plummet and it became almost impossible to approve bad loans. Today, thanks to data analytics, the default and fraud probability of loans have decreased significantly.
Here are the benefits that data analytics offer in loan proceedings.
What Is Data Analytics
Data analytics is the analysis and visualization of raw data. The process starts with historical data about what happened before and ends with the possible solutions and execution.
As loan proceedings are complex and sometimes emotional bias jeopardizes the proceedings, having a manual prediction system doesn’t help much. Data analytics roles are being assigned every day through trustable platforms and are being filled out by top institutions to make the most sense of the available data and to come up with a sustainable loan proceeding.
Customer selection is the fundamental part of any loan proceeding. While the data verification methods are still present, it’s now possible to analytically predict the quality of applications with the help of data analytics. This kind of analysis is deemed better as it leaves no-to-minimal room for errors.
Data analytics take account of the credit card purchases, subscriptions, and loyalty cards then categorize them into financial profiles. These are used to approve loans that have a better probability of being repaid.
A customer selection model usually concludes if a customer will be inclined to pay the EMIs regularly and is safe to grant loans to. But, typically, the financial verifications are done by business experts with the help of data analytics to understand their financial behavior, spending pattern, and repayment credibilities for safer loan disbursement.
Designing Custom Offers
Most of the loan offers are customized to the needs of individuals. If the applicant’s credit score is poor, they might have to pay a higher interest rate with collateral. If they have faulty loan histories, they might not be able to secure a higher principal.
In contrast, customers, who were efficient at paying previous loans are entitled to higher principal and lower interest rates.
Although these elements were always present in loan processing, with data analytics and the availability of past data, the applicants can get to know the best offers within seconds. Immediate feedback helps customers make an informed decision quickly, in addition to ensuring the experts that no bad loans are being distributed.
Data analytics are also being used by financial institutions to customize the promotional offers that a specific demographic is likely to avail. Small interest rates, longer repayment terms, and no-cost EMIs targeted to reach certain customers are such examples.
It was almost impossible to predict the financial behavior of a lender with older prediction models. A customer, appearing to be the perfect candidate could pose as a bad loan with their erratic payments after the loan was approved.
As the problem grew to the extent of jeopardizing the business of the lenders, delinquency prediction models came into play. With extensive data of past loans, records of transactions, late payments, partial payments, and failed payments, the models are now able to predict the risky loans before they are approved.
Delinquency detection not only helps the banks but the borrowers too. It’s possible that an individual somehow missed payments and has shown unusual financial behavior in the past, but is now trying to rework the errors. With ample data available on them, they can check their financial scores – which are usually reversible – to understand what went wrong and decide how it can be improved.
Even after probability models are employed, some bad loans pass through the check. When that happens, the only way to recover the principal is by collection methods. In the past, customers were categorized by risk factors. And different contact strategies were used to extract the amount. Which resulted in failures more often than not.
But after data analytic models were introduced to the banking sector, even though bad loans still went through, the applications could be segmented into micro categories depending on demographics, financial activity, and risk ratings to employ more effective intervention techniques.
According to ScoreData, the customers are divided into three categories in collection analytics.
- The ones who have defaulted for the first time
- The lazy ones
- Self-cure customers
- Bad debt
- Those who are beyond any redemption
The first-time defaulters are often the ones who are the safest of the customers but can be flagged as frauds if the account is new. Hence the collection efforts are marked as low.
The lazy customers forget to pay the bills but are the safest bet among the lenders. They usually pay after the period is over with late fines and interest. The collection efforts are marked as low.
Self-cure customers are occasional defaulters and usually are safe bets. Collection efforts for these customers are medium
Bad debt and point of no return customers are the ones who have a comparatively higher spending rate than their earnings but usually pay the minimum fees. Collection Efforts are considered High or non-viable for the borrowers of this category.
Credit card frauds, loan frauds, and deliberate payment delays have always been an issue for lenders. With data analytics and better customer selection, the problem had been handled quite effectively. Let’s look at some of the ways data analytics help to detect fraudulent activities of a profile.
Data analysis models provide an all-around view of alarming actions and suspicious transactions of the customers. It isolates attributes and identifies hidden threats to notify the banks about them.
By calculating statistical parameters like standard deviation and moving averages, analytical frameworks identify fraud patterns in customers. Excessive high or low numbers are also taken into consideration while screening fraudulent activities.
Cross Channel Monitoring
As the financial sectors became more centralized, all the transaction data of customers are available to the models. Which data analytics programs can monitor and analyze to prevent loan fraud.
Easy Loan Processing
The data analytical models were not designed to keep good loans at bay. Moreover, the data analytics capabilities now make the loan proceedings easier than ever if you have a good credit score.
As the financial institutions run their business on loans and interests, the faster and safer proceeding time helps them get the most benefit out of the customers as well.
The Bottom Line
Bad loans will always be there. What would the lenders do if a business goes bankrupt even after showing promise? But, it’s now becoming possible to detect patterns of customers who are likely to fail loans and categorize them with the help of data analytics.
Delinquency detection, offer creation, fraud detection, and loan collection are also the benefits of data analytics in loan processing.
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