The SME sector in India is vital for inclusive development given its share of GDP and employment contribution, yet its potential remains unfulfilled. While the total contribution of SMEs to India’s manufacturing output is immense at 45%, SME financing lies in a state of distress. A significant barrier to their growth can be attributed to a lack of access to formal banking and credit systems. It is estimated that 40% of MSME lending is done through the informal sector where the interest rates can be at least twice the prevailing rates in the market. The SME landscape is witnessing a rapid change with digital technologies, which can create an impactful opportunity for both traditional lenders and FinTechs.
To get a deeper understanding of the current SME financing problems, Rupeepower and MEDICI came together to hold a close round table discussion with some of the top bankers to deliberate on the challenges and opportunities in the segment. Here are the excerpts of the discussion that followed:
Several impediments are restricting the traditional lending segment to service the small-ticket-lending requirements of the Indian SME industry. First, the sector has been starved of capital due to a combination of inclusive and not limited to lack of proper documentation, limited data footprint, lack of accounting expertise to predict future cash flows, and regulatory compliance concerns.
The prime concern shared by the participants was that onboarding an SME customer is primarily a manual and paper-based process. Filling up the business loan application form is a step that is quite tedious for an applicant. The discussion brought out the elaborate paperwork involved, like common loan documents (KYC, PAN), business financials (P&L reports, project revenue), bank statements, and proof of identity of sole proprietorship/private limited during the initial phase. Most of these documents are not readily available for SME customers, or they may be hesitant to provide the required paperwork due to fear of compliance and audit. These documents are further collected through a DSA-based offline model, which increases the cost of acquisition and credit for traditional lenders.
Setting up a contextual selling experience is also essential when the customers are spoilt for so many options on the market. Customer journeys are now phygital (physical + digital) which encompass an online check for research & assessment, and a physical touchpoint like branch/relationship managers. A good customer onboarding digital journey should be covered in not more than five data fields, and the loans should be sanctioned in less than 30 minutes.
Most SMEs are excluded from the loan books of traditional lenders as they lack a comprehensive CIBIL score. These reasons can be attributed to the dynamic and heterogeneous nature of the SME landscape. SMEs are highly sensitive to changes in the economic environment; they usually operate in high-risk markets and have high failure rates. The conventional approach followed by lenders to gauge the ability for SMEs to pay typically relies on the review of tax statements or income tax returns. The major limitation of this approach is that the tax returns are outdated by a year – which may not provide an accurate representation of the financial health –, and there is an aversion to tax compliance. Typically, SMEs have different cash flow models and no clear demarcation between the company and its promoters. This makes it difficult for a lender to authenticate character for credit underwriting.
The digital data footprint for SME is booming with the adoption of digital payments and smartphones. SMEs are gradually becoming digital savvy, thereby helping lenders to easily reach them and have richer data to assess & service them. New digital data sources like entity data (MCA details, Aadhaar), financial & tax data (GST, IT return), credit bureau data (entity-level credit data, individual credit), individual data (PAN, driving license), utility data (electricity, gas, telecom), and social & media data (social profile, geolocation, call logs, SMS data) have made it possible for lenders to underwrite better. Instant credit decisions can be taken by building an underwriting model with comprehensive inputs based on machine language, which can provide automated decisions based on other non-traditional data and sectoral inputs.
New-age financial companies can also adopt a clustering approach to streamline the process of making credit accessible to SMEs. In its simplest form, clustering organizes the diverse businesses into multiple groups with similar data points. Financial corporations can discover patterns amidst the scattered group of MSMEs by developing cluster lending capabilities and making credit appraisals easier.
Introduction of the Legal Entity Identifier by the RBI has made it mandatory for corporate borrowers with exposure of ₹5 crores & above to facilitate the assessment of aggregate borrowing by corporate groups and monitor the financial profile of an entity/group. Through LEI, key reference information can be gathered like entities participating in a financial transaction.
The RBI’s decision to set up a Public Credit Registry (PCR), incorporating unique identifiers – Aadhaar for individual borrowers and Corporate Identification Number for firms and data from entities like market regulator SEBI, Corporate Affairs Ministry, Goods & Service Tax Network (GSTN), and the Insolvency & Bankruptcy Board of India (IBBI) – was acknowledged as a step in the right direction. Such a registry would augment credit availability facilities to SMEs and access to credit information, including debt details and repayment history.
A daunting task agreed upon by all participants was the high collection cost for a traditional lender as a result of it being a highly labor-intensive job. While there is a digital way of loan origination, there is still no concrete way as to how technology can minimize operational costs for an on-ground activity like collections. In addition, there is still the issue of digital strugglers who have not moved to the formal economy. They are difficult to discover, and best met with a feet-on-street agent-backed model.
At the end of the discussion, the general consensus was that a shift needs to be made in how lenders calculate an applicant’s eligibility. Currently, creditworthiness is determined by analyzing their cash flow and business statements, but instead, a household income assessment can be done, and a set of questions that need to be asked to customers can be determined to filter both fraudulent cases and inept customers.
Considering all the challenges that the SME lending sector faces, the attendees felt it would be prudent to develop a tool that can replicate the actions of a Smart Credit Manager. The tool should consist of two modules – the right questions to ask and a gesture assessment. Further implementing an AI-powered assistant that can provide pop-up questions and help a credit manager determine the next question that needs to be asked depending on their previous response will help assess the customer’s psychometric.
There is also a need to develop a solution that will help segment a homogeneous set of customers that will, in turn, help reduce the TAT for disbursing loans. E.g., customers with small-ticket-size loans or having an annual turnover of Rs. 50-60 lakhs, or else lenders would lose some customers in the segment.
We also need to streamline the lending process to an extent where loans can be disbursed in a maximum of 2-3 days. Repetitive follow-ups with customers will result in adverse behavior during the collection process, where customers will avoid collection calls.
Looking forward, algorithmic lending and digital customer engagement will play a pivotal role. It will be interesting to see what the future holds for lenders vying for a slice of this vast SME market.
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