Lending

Improving Credit Information Infrastructure for MSMEs - Proposals for Reform

RupeePower Head of Public Policy

I.Introduction

India has 63 million MSMEs contributing 31.6% of the GVA and employs 111 million people across all sectors. Yet, an important barrier to these engines of growth remains lack of access to formal financing channels. A recent study released by Omidyar-BCG estimates that 40% of the MSME lending is through the informal money markets. The credit information architecture is an important inhibiting factor for the aforementioned statistic. Credit reporting addresses the important problem of asymmetric information between lenders and borrowers that may, in turn, lead to adverse selection and moral hazard issues. A robust credit information framework provides the basis for fact-based and quick credit assessment, thus facilitating access to credit to borrowers with good credit history.

Accordingly, this article will take a deep-dive into the issues facing us and discuss potential solutions, especially from an MSME perspective.

II. Unavailability of Data/Absence of Surrogate Data

Unavailability of data, fragmented data, and incomplete credit bureau coverage of MSMEs is an important bottleneck inhibiting the flow of formal financing to the MSME sector. About 99% of the MSMEs are micro-enterprises and 95% are operating as sole proprietorships/partnerships, paccording to the National Sample Survey. There is presently no comprehensive repository of entity-level data for these unincorporated businesses.

The “Udyog Aadhaar Memorandum” project started by the Government is a potential future repository of MSME-specific data; UAM captures the following fields:

  • Aadhaar number

  • Enterprise name

  • Type of organization/address

  • Date of commencement

  • Bank details

  • Major activity

  • NIC Code

  • Persons employed/investment in plant and machinery

However, at present, only over 5.6 million MSMEs out of the estimated 63 million have filed a UAM. Furthermore, UAM data is self-reported data and thus cannot be relied upon by lenders without further verification.

An allied project of the Ministry of MSME is “MSME data bank.” For facilitating the promotion and development and enhancing the competitiveness of MSMEs, the Ministry of MSME vide Gazette Notification No. 750(E) dated 29.07.2016 had notified the MSME Development (Furnishing of Information Rules, 2016) under which all MSMEs are to furnish information relating to their enterprises online to the Central Government in the data bank maintained by it. This data bank will enable the Ministry to streamline and monitor the schemes and pass on the benefits directly to MSMEs. It will also provide real-time information about the status of MSMEs under various parameters. However, as of December 2017, only 1.47 million MSMEs have updated relevant data-points on the data-bank hitherto; thus, the repository is yet to gain critical mass. The absence of formalization/verifiable data makes initial due diligence by formal sector lenders unviable commercially (especially relative to the ticket-size of the credit demand).

In face of foregoing, surrogate data such as utility payments, rentals, and telecom bills can offer proxy to their credit behavior but are presently not captured by credit information companies (or any other aggregator) so credit institutions do not have the benefit of that data in credit appraisal.

All these factors create a “vicious cycle” effect in that an unincorporated MSME is constrained to borrow at higher than optimal rates from money-lenders tenure upon tenure.

III. Fragmented Data

Furthermore, to the extent an SME is an incorporated enterprise or is otherwise part of the formal sector (through being subject to GST law and such) data relevant to MSMEs is either spread between several data silos viz, the MCA, CIC, CERSAI, RoC, and the like. Each of these entities has its own reporting format that can often overlap. Moreover, there is a lag in uploading data on the respective portals denying the users access to real-time/near information about the borrower thus leaving potential vulnerabilities in the appraisal process. On the flip side, the reporting obligations add to the compliance costs and time for the credit institutions. In the present reporting framework, banks must file 300 reports.

IV. Potential Solutions

Formalization of MSMEs: As pointed out above, the UAM project initiated by the government is a potential repository for data from MSMEs that operate in unincorporated form. However, the UAM “density” will need to be significantly higher for it to function as a public database that lenders can benefit from. Moreover, since UAM data is self-reported, policymakers and the private sector must develop a mechanism for verifying the authenticity of data through an audit process, potentially subsidized by the Government. On the lines of BMO model in Germany, follow-on validation/audit responsibility may also be delegated to an empaneled industry body. Furthermore, linking up UAM database to the MCA will enable lenders to comprehensively access both incorporated and unincorporated MSMEs through MCA portal.

Surrogate Data: Positive data from telecom/utilities payments history for an MSME in rural, hi excluded from formal financing can increase the probability of it accessing formal financing if that positive payments information is impounded into CIC data. The absence of these surrogate data-points also appears to have a bearing on credit bureau coverage. Enabling credit bureau access to telecom/utilities payments data and insurance premium payments data would increase bureau coverage from 55.9 % of India’s adult population up to an estimated 70% of the adult population of the country and would be a major boost to credit eligibility of lower ticket borrowers creating a virtuous cycle for the MSME sector. Under the extant Credit Information Companies law framework, a notification from the RBI notifying the utilities/telecom data/insurance premium as “credit information” would be necessary for enabling CICs to access the said information. Ancillary reforms in telecom and insurance law would also be required.

The public credit registry (PCR) recently proposed by the RBI is another model that can be leveraged for aggregating utility/telecom data. In this model, the PCR acts as the aggregator of data. The CICs utilize the data and distill it in their credit scores.

A third (disaggregated) model, prevalent in the US, is to enable (license) specialized industry-specific for-profit credit bureaus that gather and share specific information with creditors and other businesses. Apart from Experian, Equifax and TransUnion, there are over 400 smaller, regional or industry-specific credit bureaus in the US. They collect and share information that can range from pending litigation information, telecom/payments data to rental payments history of a given natural/legal person.

Fragmented Data: As pointed out above, multiple repositories including CERSAI, MCA, CICs aggregate data presently and they have different formats. Streamlining formats across these repositories to de-duplicate data will improve the utility of data for the users. Fragmented data and weaker predictability of default are co-related; as such, making the information comprehensive would result in better credit decisioning and sound risk management.


About RupeePower: RupeePower is a leading CreditTech company in India. RupeePower’s platform “Crediton” is a comprehensive digital-first product suite that enables banks & lending companies to transform themselves into state-of-the-art digital lending enterprises at scale. CreditOn provides lenders with flexibility & scale to manage in real-time their credit decisioning criteria, sourcing channels, customer onboarding journey, underwriting workflows and digital partner ecosystem - across the whole range of retail and SME credit. The product suite enables seamless online & offline origination with its acquisition platform, Loan CRM, customizable BRE with instant decisioning & Loan Origination System. Smart AI/ML-powered tools like KYC Box, Address Match & decisioning based on non-traditional sources of data enable paperless loan origination within a robust credit assessment framework. The CreditOn suite has consistently demonstrated a multifold increase in origination throughput for banks & lending companies while preserving (if not enhancing) portfolio quality.CreditOn has created client success across banks & lending companies with names like State Bank of India, Kotak Mahindra Bank, Standard Chartered, RBL Bank, YES Bank, Mahindra Finance, Fullerton India, AU Small Finance Bank, Edelweiss & Ujjivan Financial Services. The platform has enabled these lenders to disburse over USD 4 billion in retail and SME credit to roughly 2 million customers over the last four years.

________________________\ \ 1. Deosthalee Committee Report on Public Credit Registry available at https://rbi.org.in/scripts/PublicationReportDetails.aspx?ID=895#F10 \ 2. Fintech India- The Changing Landscape of SME Lending (July 2017) \ 3. https://msme.gov.in/sites/default/files/MSME-AR-2017-18-Eng.pdf\ 4. See note 3, paragraph 1.4.3 (The Data Bank is accessible at www.msmedatabank.in) \ 5. See note 1 at p.25 \ 6. See https://udyogaadhaar.gov.in/Web/doc/Background.pdf\ 7. https://www.omidyar.com/sites/default/files/file_archive/18-11-21_Report_Credit_Disrupted_Digital_FINAL.pdf (p. 29) \ 8. R Gandhi, Keynote Address delivered at the 7th Annual CIBIL TransUnion Credit Information Conference (2015)\ 9. WB figures (2019) available at http://www.doingbusiness.org/content/dam/doingBusiness/country/i/india/IND.pdf (p.55) \ 10. See 2 (d) of the Credit Information Companies (Regulation) Act, 2005\ 11. See note 1, p. 24

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