The Essence Of Data Science For Accurate Credit Underwriting In The Digital Era
Data plays a vital role for financial organizations in accurately estimating a customer’s loan repayment ability. In the context of digital and alternative lending spaces where manual intervention is limited and decisions need to be taken quickly, data science can help assess the safety or probability of risks in any scenario.
Many alternative platforms including crowdfunding and peer to peer (P2P) lending have emerged those match borrowers and lenders online. These alternative spaces are driving financial inclusion by offering credit to underserved consumers – people with no or poor credit history, geographically marginalized borrowers etc.
Banks and the traditional credit scoring process
Banks use highly regulated and uncompromised criteria in their loan underwriting, evaluation of payment history and credit score. One parameter that most banks strictly rely on is the CIBIL score.
The CIBIL score is a three-digit number providing the summary of your credit history. It is calculated by using the credit history from the Credit Information Report (CIR), which is an individual’s credit payment history ranging across credit institutions and loan types over a period.
In short, the credit score or the CIBIL score is one of the most important criteria for loan approvals. While this is a useful metric to understand an individual’s creditworthiness, often individuals with no credit history or a delayed payment record might get precluded from availing another loan. For instance, a score of NA or NH (applications with no credit track record) prevents some traditional lenders’ credit policy from providing loans. Also, the credit score range of 1-5 indicates individuals with a credit history of less than 6 months and is often viewed as high risk.
On the other hand, P2P lenders believe that the credit score may not represent the complete story of an individual’s credit-worthiness. They use a more comprehensive set of parameters to judge the borrowers. For example, they assess factors like identity verification, risk profile, credit history, trading history, CIBIL score, PAN, bank statement among others. But that’s not all. P2P lending platforms also refer to unconventional means such as social media profiling to gauge a borrower’s creditworthiness. In fact, many lending platforms leverage Artificial Intelligence and generate thousands of data points for comprehensive profiling.
It is here that technologies like data analytics and social modeling come into the picture. Using the power of big data and AI, lenders can make more accurate underwriting decisions that can help against defaults and sometimes automate parts of the process.
Data Science for Loan Underwriting Accuracy
Machine Learning helps analyze and correlate vast amounts of customer data to find patterns that are otherwise difficult to find manually. These include information such as educational merits/certificates, employment records, internet browsing habits, and daily location patterns.
To the extent that it can determine, whether applicants are truthful about their income by validating their employment history and comparing data with similar applications. ML algorithms can also find hidden patterns that are favorable for the customers. Using this information, lending platforms create a borrower profile if the applicant is eligible for a loan and assign them a risk grade or score along with an interest rate based on the risk.
Each P2P platform or marketplace lender may have their own algorithm or credit scoring model, but this data-backed approach helps in reducing defaults and allows lenders to make informed investment decisions.
Accelerating lending with automation
When it comes to loan approval delays, the process of evaluation and assessment can take over weeks. Time is of an essence and here it is literally money. The algorithmic lending model provides key decisions in minutes and shortens the registration, approval and disbursal loop from weeks to days or even hours. Often lending platforms offer an auto-invest option where lenders key in their lending criteria and the tool chooses borrower profiles and investments based on this information, taking manual intervention and guesswork out of the process.
It is a profitable scenario for all concerned.
Benefits of data science for lending platforms and financial institutions:
Comprehensive evaluation and assessment of first-time borrowers
Better insights from data based on behaviors and patterns
Higher level of accuracy in assessing risks and defining interest rates
Reduction in defaults for lenders and better interest rates for borrowers
With the evolution of fintech and data science, financial services have become more inclusive and accessible. Artificial Intelligence and big data will continue to improve the science of credit underwriting and lending as we know it.