The Micro, Small, and Medium Enterprises (MSMEs) sector has emerged as the cornerstone of the global economy, powering 70% of the workforce.
According to an IFC report, 63 million Indian MSMEs contribute 30% of the GDP and provide jobs to 110 million people.
Despite this, MSMEs often struggle with credit due to limited credit history, insufficient collateral, and dependence on informal loans.
In this scenario, data-driven financing changes the game by using advanced analytics and alternative credit score models to assess borrowers’ creditworthiness, making loans faster, easier, and more affordable—just what MSMEs need to grow.
This blog post explores the factors contributing to the persistent credit gap and how data-driven strategies revolutionise MSME lending to reshape financial inclusion.
5 Systematic Challenges to MSME Financial Inclusion
RBI mandates banks to reserve 40% of their Adjusted Net Bank Credit (ANBC) for priority sectors. These include agriculture, MSMEs, renewable energy, and economically disadvantaged sections.
As part of this framework, a dedicated sub-target requires banks to allocate 7.5% of their ANBC specifically for micro-enterprises, addressing the distinct financial hurdles faced by these smallest businesses.
Despite these policies, the IFC report reveals that MSMEs face an unrealistic credit gap of INR 33.3K crore.
This is because the policies fail to address the fundamental challenges, such as:
1. Lack of Reliable Financial Data
Since MSMEs often operate informally, they significantly lack audited financial statements or standardised balance sheets. Due to insufficient data, assessing borrowers’ creditworthiness is difficult.
2. Credit Gap and Accessibility
80% of MSMEs lack funding for formal financing. Formal lenders hesitate to engage with MSMEs due to the high risks and insufficient financial data.
3. Dependency on Informal Credit Sources
Due to the complex and lengthy processes of formal lending, they often resort to informal credit sources with exorbitant interest rates, perpetuating their financial struggles.
4. Limited Access to Alternative Credit Data
Most lenders do not fully utilise alternative data sources, such as bank statements, digital payments, transactional histories, and even social media activities, which can provide a more accurate financial profile of borrowers.
5. Fragmented Lending Ecosystem
The fragmented MSME lending ecosystem and lack of tailored credit products, like flexible repayment terms or variable loan sizes, create inefficiencies, increase costs, and limit their access to suitable financing.
In the face of such challenges, lenders are reimagining credit evaluation by leveraging the transformative power of data-driven decisioning using advanced risk assessment models and AI.
What is Data-Driven Decisioning?
Data-driven decisioning uses technologies, like AI-driven loan origination systems, that combine data analytics and machine learning to extract meaningful insights from diverse datasets.
It helps lenders leverage alternative sources like daily transaction patterns, UPI payments, and GST filings to assess a borrower’s creditworthiness more precisely.
Why Does This Matter in MSME Lending?
- Improve Risk Assessment: Analyse a variety of data sources to predict repayment behaviour.
- Enable Faster Loan Processing: Automate the borrower credit evaluation process and make it real-time with AI-enabled KYC frameworks and bank statement analytics.
- Cover a Wider Market: Reach underserved or unbanked businesses with geo-location tagging, digital footprint tracking, and customised credit analysis.
5 Benefits of Data-Driven Decisioning in MSME Lending
Here’s how the integration of technology in lending offers a promising solution to the challenges faced by MSMEs in securing loans:
1. Improves Risk Assessment and Credit Scoring
By analysing GST returns and e-invoicing data, lenders can view insights into business turnover and compliance patterns, while digital payment records provide information on cash flow health.
Supply chain data adds a crucial dimension by revealing a business’s reliability in vendor and customer transactions.
For instance, the Bank of Baroda established partnerships with multiple fintech companies, which helped them reduce their loan processing time while maintaining low default rates.
2. Expedites Loan Approval
Data analytics allows for real-time credit assessments, which cuts down loan approval TAT significantly.
Nabsamuruddhi Finance Limited, for instance, uses Finezza’s automated loan disbursement algorithms to approve business loans in less than an hour, a process that once took days.
3. Forstalls Loan Delinquency
Understanding granular data through data analytics enables lenders to tailor loan products to meet individual customer needs. For example, small businesses with seasonal sales can benefit from flexible repayment schedules.
Customisation helps MSMEs avoid loan delinquency and automate the loan recovery process, ultimately driving retention rates.
4. Manages Risk Proactively
Early warning systems act as financial health monitors, helping lenders prevent defaults and reducing NPAs. For instance, TransUnion CIBIL found that MSME lenders using data analytics saw a marginal improvement in loan portfolio quality within a year.
By integrating critical data points, like daily transactions and financial records, directly into lending platforms, MSMEs can spot struggling borrowers early and offer timely support to keep their loans healthy.
5. Identifies Cross-Selling Opportunities
Data-driven insights help lenders match customers with the right financial products at the right time. By understanding a business’s growth patterns and cash flows, Finezza enables lenders to spot cross-selling opportunities, such as working capital top-ups or invoice financing.
This helps MSMEs grow while building a profitable banking relationship.
Tech that Promotes Data-Driven Decisioning for MSME Lenders
Here are a few examples of how several tech tools enable data-driven decisioning:
1. AI-Driven Loan Lifecycle Management Systems
AI and ML algorithms analyse vast datasets to identify patterns and predict repayment probabilities.
For example, the cloud-based financial analytics company Finezza’s loan management system improves data-driven decision-making by centralising loan-related data, enabling real-time analytics, and automating risk assessments.
2. Big Data Analysis
Bank statement analysis tools process high-volume data, such as bank statements and credit card transactions, enabling lenders to detect patterns and anomalies in borrowers’ financial behaviour.
3. Credit Bureau Analysis
Analytics platforms offer proprietary insights into credit bureau data to visualise complex information beyond scores.
4. API-Based Integrations
APIs enable seamless data sharing between MSMEs, fintechs, and government platforms like GSTN, creating a holistic view of the borrower.
For instance, Finezza’s Loan Eligibility Estimator tool provides a realistic view of a borrower’s eligibility by combining various data points, like IT returns, GST filings, and credit scores.
How Does Finezza Enhance Data-Driven Decisions for MSME Lenders
As an end-to-end lending solution portfolio, Finezza can help lenders achieve maximum profitability and ease of operation. It provides a 360-degree view of borrowers, integrating various data sources to automate credit evaluation.
The platform’s Credit Bureau Data Analysis is packed with the following features:
- Consumer integration with CIBIL, Experian, CRIF, and Equifax
- Credit-focused grouping and alerts for highlighting risks
- Real-time access to applicant’s KYC information
- DPD analysis for all products
- Centralised view of loan and repayment history, along with a summary of borrowings
Key Takeaway
As India marches towards a trillion-dollar economy, empowering MSMEs with uninterrupted access to credit is essential.
The adoption of data-driven decisioning, powered by AI-powered automation, can bridge the MSME credit gap. It will also boost MSME contribution to GDP from 30% to 40% by 2025, as projected by CII and drive inclusive growth by formalising unbanked businesses.
As a cloud-based analytics lending lifecycle management platform, Finezza’s use of alternative data sources, superior fraud detection capabilities, and predictive analytics software help lenders identify potential risks and opportunities rather quickly.
Book a FREE demo to learn how Finezza’s customisable lending lifecycle management solution can improve MSME lending.
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