The traditional methods to assess creditworthiness have fared no good. But, they have paved the way for robust policy frameworks and advanced credit scoring systems.
It goes without saying that funds are oxygen to businesses – the more the better. Although funds cannot guarantee survival, their deficiency has definitely stifled many startups. Though Micro, Small, and Medium Enterprises (MSMEs) can seek help from banks and NBFCs, getting the financial boost they need is not that easy. It depends upon several factors, but mostly, if the business entity is a viable investment for banks (or other financial institutions) to bank on. This is assessed based on a lot of data.
Traditional credit underwriting processes revolve around asset pricing and rely on basic information such as time in business, industry growth projections, personal credit score, and annual revenues. Although crucial, the data points are not sole indicators to gain a holistic view of creditworthiness for any business.
50 million MSMEs in India account for USD 2 trillion of business every year. These are entities with little to no digital footprint and minimal formalization. Due to the limited availability of data, credit is low-interest credit is inaccessible by these enterprises leading to a USD 1 trillion debt deficit.
Clearly, the traditional methods to assess creditworthiness have fared no good. But, they have paved the way for robust policy frameworks and advanced credit scoring systems. The Reserve Bank of India issued its Master Directions for compliance to public and private banks and Financial Institutions to deploy Early Warning Systems (EWS) for the entire life-cycle of a loan account. Besides, the pandemic has added another nail to the coffin. With a lack of revenue and an unpredictable market scenario, the scrutiny around digital data and creditworthiness has significantly tightened. The trend will persist for the foreseeable future.
How can businesses avail themselves credit?
A multidimensional credit scoring approach can empower businesses to gain digital trust in the long haul. This opens their doors to having better access to formal credit. So, a dynamic Trust Score works two ways in ensuring the interest of both parties. Robust creditworthiness empowers a thin-file business to get access to formal credit, whereas, it protects banks from making bad decisions. It is ascertained by getting a 360-degree view of the creditworthiness of an applicant’s partners, suppliers, and vendors in the supply chain.
The role of new-age technology
Sophisticated AI-powered TechFin solutions let businesses self-report on-time payments to their creditors, which helps in building their credit score. A strong Trust Score manifolds an entity’s prospects in getting the desired loan while creating superior brand credibility. Therefore, it becomes necessary to maintain a healthy credit score as it may be investigated for discrepancies, averting fraud incidence against collateral, and monitoring red flags for EWS. Business credit scores can impact the value of funding, repayment terms, interest rates, among other things vis-a-vis financial support that a business is seeking.
Banks and NBFCs can leverage the same technology but with different parameters (and purpose) to evaluate the risk that a potential borrower (business) may pose.
Here are a few components of advanced credit scoring:
AI-ML-powered all-inclusive credit scoring uses a mix of standalone image analysis, consent-based data, public data, and peer comparison to underwrite and price credit to these entities.
Image analytics dwell on advances in image processing techniques and ML to gain insights on MSMEs, which cannot be sufficiently analyzed using traditional methods. Here, an algorithm provides insights on entities by evaluating the pictures of the entity’s physical infrastructure.
The idea is to collate and correlate such picture-based insights with traditional (financial and non-financial) data points. The advent of a gamut of mechanisms available for capturing a high-quality image (smartphone camera), has made the entire process easy. Correlating different pieces of data together, predicts the economic footing of businesses, no matter how thin-file they are or falling very short of relevant data points in reaching credit decisions.
After gaining consent from the borrowing entity, a lender can extract valuable information and perform multiple checks during underwriting through GSTN filings, bank statements, and ITR filings. Cutting-edge credit intelligence and monitoring solutions are capable of performing automated checks. They analyze every document while simultaneously verifying information at distinct parameters to reveal inconsistencies and tallying it with the pictorial information.
Public Data Analysis
New-age credit scoring may consider data from various public sources like Regulatory Registrations, Sanctions Screening, Statutory Payments, Trade Information, Litigation checks, Media Monitoring, and Sentiment Scoring. They can correlate the data from disparate sources with the credit-seeking entity using a proprietary singularity model.
Credit intelligence platforms such as these can impact borrower segmentation, helping lending institutions profile the borrowers based on risk appetite. Artificial Intelligence-backed credit scoring models render a composite risk rating score (Consent Data Score, External Data Score, and Image Analysis) for each potential borrower.
A combination of the above insights and scoring can help financial institutions weigh the threat involved with each borrower and reach the right decision. With volatility in markets across all sectors and government orders treading cautiously against NPAs, a strong credit score will become an inevitable feature for both FIs and businesses in the future.
By, Meghna Suryakumar, Founder and CEO, Crediwatch