Non-performing Assets (NPAs) are also known as defaults or bad loans. They result from lousy lending practices like lack of a thorough background check of borrowers for the repaying capacity, financial health, and intent to repay, etc. Sometimes, increased competition coerces banks to disburse unsecured loans.
Apart from falling ROIs, NPAs can tarnish the reputation of shareholders of the lending company. This can also adversely affect the liquidity at a lending firm.
Here Are Some Reasons Explaining Why NPAs Occur:
Improper Threat Assessment at the Lending Business
All successful lending businesses thrive on cardinal principles of safety, liquidity, and profitability. They always assess applicant’s applications for proof of good financial standing. They look for safe avenues for lending by checking if an applicant is in a position to pay back a loan, including both principal and interest.
However, repayment of a loan refund depends on factors like the borrowers, their capacity, and willingness to pay. Banks ascertain a loan applicant’s ability to repay a loan by looking at their tangible assets and financial viability.
Financial institutes also consider a candidate’s willingness to pay through their character, reputation, etc.
Inadequate Use of Technology to Estimate Loan Eligibility
Very often, outdated technology and management information solutions prevent lending businesses from reaching market-driven decisions. The time taken by financial institutions to estimate loan eligibility and evaluate loan applications can be taxing for an applicant. Manual processes to analyze heaps of paperwork to check if an applicant qualifies for a particular loan scheme or not can elongate the lending lifecycle.
Real-time decision making for loan disbursal is the norm today. With a lack of proper MIS and financial accounting systems, lending companies end up with poor credit collection, hence NPAs. There is an immediate need to upgrade to a multifaceted lending management solution to keep up with the times.
Human Error at the Lending Business
Most lending businesses still abide by manually managed processes. These paper-heavy and time-consuming processes are prone to widespread data breaches and security risks.
Most business owners are hesitant in adapting to newer technologies due to concerns like questionable effectiveness, change in management practices, and sensitivity of data, etc.
Even methods like data extraction from documents, especially KYC, is a challenge for lenders. This is because the papers are collected and submitted physically. There scanning, uploading, verification is all done manually, before storage in a database, which increases the scope for error.
Further, the growing credit demands in India increase the need for manual data extraction to maintain accuracy. Manual data extraction is inefficient, given the high demand. Execution failures like monotony that sets in with the repetition of manual effort ultimately lead to negligence.
Over Dependence on CIBIL and Other CICS for Credit Rating
Sometimes financial lending companies rely on three-digit credit scores generated by CICs like TransUnion CIBIL Ltd. CIC credit data is calculated based on the credit history of an individual over a fixed period of time-based on a combination of factors.
CIC data is calculated on the basis of indicators like credit utilization, amount and duration of credit facilities availed, month-on-month credit payment behaviour, delinquencies and defaults, written-off or settlements on credit facilities, and also the credit history on loans guaranteed or co-owned by the applicant. While these scores keep changing from time-to-time, they are not very reliable.
Very recently, TransUnion CIBIL Ltd reported issues with credit data. There were reports of update failures of CIBIL scores for many score holders.
How to Reduce NPAs for a Lending Company?
Leverage Alternate Data for Application Assessment
It is common knowledge that even minute inaccuracies in an individual’s credit score mess up their chances of availing loans, as most traditional lending companies count on credit ratings by CICs.
Letting off an eligible applicant without approving their application can translate into a loss of business for the lending firm.
That is why lending companies need to settle for a more concrete approach to access loan evaluation. Instead of just relying on credit scores by any single agency, they can leverage the use of alternative data for credit assessment.
The use of alternative data can help evaluate the ‘true’ creditworthiness of an applicant. The use of alternative credit scores along with credit scores from various CICs presents a more wholesome picture of the prospects of a loan applicant, thus reducing the chances of NPAs for the lending firm.
Use of AI and ML backed Platform for Efficient Loan Origination.
Lending businesses can use tools that are designed to ease the lending origination processes, to begin with. A useful loan origination tool allows lending teams to do more than just manually entering subjective data. Platforms backed by AI and ML technology allow effective document recognition and field detection.
Counting on a data structuring module enables lending companies to reduce the end to end time used to populate the required fields of a document down to 5-10 seconds. It also prevents any chances of human error in the process of loan origination, thus ensuring there are no NPAs in the long run.
Intelligent Loan Eligibility Estimator for Loan Evaluation
Standard practices often render the loan approval exercise lengthy. Further, the eligibility of an applicant for a loan depends on multiple factors like conditions of the lender, type of loan, and borrower profile.
Some lending companies faultily choose highly generic eligibility estimators that are not designed to cater to the needs of these financial lending companies.
While there is no standard way that loan eligibility can be calculated, lenders must integrate a loan eligibility calculator customized to their criteria into their loan origination systems to reduce their NPAs.
NPAs or Bad Loans are default cases that adversely affect the profitability of lending companies. NPAs also result in reduced capital assets and lending limits. A tech-based lending management platform can help banks financial institutes reduce inconsistencies. Businesses must note that leveraging data analytics and allied tech to identify red flags and early warning signals can improve the overall experience for borrowers and reduce NPAs for the lenders.
Finezza is a leading loan management platform that eases the process of loan disbursal on the whole. Thanks to Finezza, lending institutes benefit from enhanced speed and security. The tool helps lenders track the collection process in real-time with the help of features live dashboards, data analytics, and insights. Also, it closely monitors customer data for more granular customer segmentation.
Furthermore, Finezza uses sophisticated data science-backed measures like sentiment analysis to suggest a strategy for a field agent or caller. Overall, it can significantly reduce the rate of NPAs of different lenders through its superior algorithm that follows regulatory compliances and adapts to the needs of the firm.