A glimpse into the future of credit scoring
It goes without saying that funds are oxygen for business – the more the better. While funds cannot guarantee survival, their scarcity has definitely stifled many startups. Although micro, small and medium enterprises (MSMEs) can turn to banks and NBFCs for help, getting the financial boost they need is not that simple. It depends on several factors, but especially if the business entity is a viable investment that banks (or other financial institutions) can build on. This is assessed on the basis of a lot of data.
Traditional credit underwriting processes revolve around asset pricing and rely on basic information such as time spent in business, industry growth projections, personal credit rating, and annual income. While crucial, data points are not the only metrics for gaining a holistic view of any business’s creditworthiness.
50 million MSMEs in India represent 2,000 billion USD in turnover each year. They are entities with little or no digital footprint and minimal formalization. Due to the limited availability of data, credit is a low interest rate credit unattainable for these companies, resulting in a debt deficit of $ 1 trillion.
Obviously, traditional methods of assessing creditworthiness have not worked well. But, they have paved the way for strong policy frameworks and advanced credit rating systems. The Reserve Bank of India has issued its general compliance instructions to public and private banks and financial institutions to deploy Early Warning Systems (EWS) throughout the life cycle of a loan account. In addition, the pandemic has added another nail to the coffin. With a lack of revenue and an unpredictable market scenario, control over digital data and creditworthiness has tightened considerably. The trend will continue for the foreseeable future.
How can businesses access credit?
A multidimensional approach to credit scoring can help businesses gain digital trust in the long run. This opens the doors for them to better access to formal credit. So, a dynamic trust score works in two ways to ensure the interest of both parties. Robust creditworthiness allows a thin file company to access formal credit, while it protects banks against bad decisions. It is verified by obtaining a 360 degree view of the creditworthiness of a candidate’s partners, suppliers and vendors in the supply chain.
The role of new-age technology
Sophisticated Artificial Intelligence-powered TechFin solutions allow companies to self-report payments to their creditors on time, helping them build their credit score. A high trust score increases an entity’s chances of obtaining the desired loan while creating superior brand credibility. Hence, it becomes necessary to maintain a healthy credit rating, as it can be investigated for discrepancies, avoid collateral fraud, and monitor red flags for AMPs. Business credit scores can have an impact on the value of financing, repayment terms, interest rates, among other things in relation to the financial support sought by a business.
Banks and NBFCs can take advantage of the same technology but with different parameters (and goals) to assess the risk that a potential borrower (business) may pose.
Here are some elements of advanced credit scoring:
AI-ML-based all-inclusive credit scoring uses a blend of stand-alone image analysis, consent-based data, public data, and peer-to-peer comparison to underwrite and assess credit to these entities.
Image analysis focuses on advancements in image processing techniques and ML to obtain information on MSMEs, which cannot be sufficiently analyzed using traditional methods. Here, an algorithm provides information about the entities by evaluating the images of the physical infrastructure of the entity.
The idea is to bring together and correlate this image-based information with traditional data points (financial and non-financial). The advent of a range of mechanisms available for capturing a high quality image (smartphone camera), has made the whole process easy. Correlating different data together helps predict the economic base of businesses, regardless of their size or lack of relevant data points for making credit decisions.
After obtaining the consent of the borrowing entity, a lender can extract valuable information and perform several checks during underwriting through GSTN deposits, bank statements and RTI deposits. State-of-the-art credit monitoring and monitoring solutions are capable of performing automated checks. They analyze each document while simultaneously checking information at separate parameters to reveal inconsistencies and match them to the information shown.
Public data analysis
New-age credit scoring can take into account data from a variety of public sources such as regulatory registrations, sanction screening, statutory payments, business information, litigation checks, media monitoring, and credit scoring. feelings. They can correlate data from disparate sources with the credit research entity using a proprietary singularity model.
Credit reporting platforms like these can impact borrower segmentation, helping lending institutions profile borrowers based on their risk appetite. Artificial intelligence-based credit scoring models generate a composite risk score (consent data score, external data score, and image analysis) for each potential borrower.
A combination of the above information and rating can help financial institutions weigh the threat each borrower poses and make the right decision. With market volatility across all sectors and government orders being cautious about NPAs, a good credit rating will become an inevitable feature for FIs and businesses in the future.
By, Sandeep Anandampillai, Co-Founder and CPO, Crediwatch