Financial inclusion has emerged as a significant opportunity for lenders in India. As of December 2024, World Bank data estimated that India’s credit-eligible population, comprising consumers aged 18 to 80, stood at approximately 1,036 million.
However, only 27%, or approximately 277 million consumers, utilised formal credit facilities. Approximately 451 million Indians have limited or no formal access to credit.
Lenders comprising banks, Non-Financial Banking Corporations (NBFCs), neobanks, and fintech apps, however, are yet to capitalise on this opportunity. Lenders struggle to efficiently and effectively serve this market.
This is where alternative credit scoring can create a level playing field, especially for first-time borrowers.
In this blog, we deconstruct this lending approach. We also outline the long-term benefits, including enhanced risk assessment and the provision of relevant products.
What Is an Alternative Credit Score?
Alternative credit scoring is the process of leveraging alternative financial data to make more inclusive, accurate, and efficient underwriting decisions.
- The data sources used in this process are not used in credit history reports, which typically tend to rely on credit card usage patterns and previous loan behaviour.
Examples of alternative data sources include bank statements and Goods and Services Tax (GST) returns. - Credit history reports do not apply to first-time borrowers. This demographic is yet to develop a credit footprint in the banking ecosystem. However, excluding them comes at an opportunity cost for lenders.
The use of alternative credit scoring can help address some of these challenges.
How Alternative Credit Scoring Works
This type of credit scoring can empower lenders to target new markets and capitalise on new opportunities. Here’s how:
1. Expanding the Scope of Data Used
Financial data plays an important role in the alternative credit scoring methodology. However, traditional data sources, such as income and revenues, and credit scores, do not guarantee a borrower’s ability to repay a loan in the future.
Asset-related data is useful. However, a lack of assets that can be leveraged as collateral can be a disadvantage for borrowers who do not possess assets. This is where financial data, such as bank statements and GSTR, can be advantageous.
These data points enable lenders to assess cash flow and past financial behaviour. In turn, lenders are in a position to predict future credit behaviour more effectively.
2. Efficient Extraction and Organisation of Data
As the volume of data increases, efficient extraction and restructuring of data into relevant categories aid in accurate alternative credit scoring.
- Lenders can leverage AI-powered tools like loan management systems to automate the process of pulling, extracting, and organising.
- Data can be pulled via frameworks such as account aggregators. It can be extracted from over 700 document formats.
- Data is segregated into inflow and outlook categories. Each of these categories is further segregated into over 29 to 30 subcategories.
- This process ensures that every financial transaction is accounted for through the process, which is completed within minutes.
3. Comprehensive Financial Analysis
Once the data is comprehensively structured, it can be analysed efficiently and in-depth. When the analysis is complete, the software generates an alternative credit score. The software also generates reports that inform lenders about their underwriting.
- The alternative credit score can be compared with benchmarks across various use cases, from first-time borrowers to MSMEs.
- Lenders can cultivate a realistic understanding of past and real-time cash flows.
- The reports highlight aspects such as past credit behaviour and financial consumption patterns.
- The entire analysis and alternative credit scoring process is automated.
- Lenders can save time and resources and reduce errors. They can bring accuracy and data-centricity to underwriting decisions.
4. Superior Fraud Detection Through Alternative Credit Scoring
Access to a borrower’s complete financial data also enhances the chances of detecting potential fraud, money laundering, and the funding of illegal activities.
- The use of AI in financial analysis tools can detect irregular patterns in financial transactions
- Lenders can verify the source of incoming funds and the destination accounts of outgoing funds.
- They can detect fraudulent activities such as circular transactions, which camouflage negative cash flows.
- Superior alternative credit scoring software can also flag cases of identity theft and fraudulent documentation.
5. Data-centric Customised Solutions
Customised solutions are emerging as an important pillar of financial inclusion. Standardised lending rates and products cannot serve diverse audiences effectively.
Superior alternative credit scoring software automates the process of generating tailored solutions for individual borrowers. Thus, lending solutions become more relevant, cost-efficient, and inclusive for borrowers.
Lenders can also sanction loans of a smaller ticket size by leveraging data to remove traditional barriers. This approach enhances the potential for borrowers to repay loans based on their real-time cash flows and past financial behaviour.
Major Benefits of Alternative Credit Scoring
This can cultivate a culture of financial inclusion while mitigating risks:
Boosts Risk Assessment Capabilities
Lenders can effectively leverage data to predict if a borrower is likely to repay a loan in the future more accurately. They are more equipped to detect cases of potential fraud and criminal activity early on.
Also, they can build these capabilities with the practice of alternative credit scoring. In turn, they can mitigate risks more effectively.
Attracts New Audiences
The use of alternative scoring opens up new doors of opportunity for lenders. For instance, they can target the following underserved markets:
- First-time retail and business borrowers
- Micro, small, and medium-sized businesses
- Underserved rural audiences
Drives Revenues
Businesses can adopt a data-centric underwriting culture, which eradicates human bias and exclusion. This approach enables businesses to drive higher returns on investment by cutting operational costs and boosting revenues.
Takeaway
Traditionally, lenders have experienced challenges in successfully assessing risk. They have also struggled with providing relevant products to underserved credit seekers in India.
However, continuing to leave them outside the formal landing ecosystem comes with an opportunity cost to lenders.
By adopting an AI-powered loan management system, lenders can seamlessly integrate the practice of alternative credit scoring. These can offer relevant solutions to underserved credit seekers. In turn, lenders can convert more customers and drive revenues.
Finezza offers a comprehensive set of tools and solutions to manage your lending portfolio. Our end-to-end lending management solutions are trusted by brands like Ftcash, Hiranandani Financial Services, Gromor Finance, and UC Inclusive Credit, to name a few.
Finezza’s intuitive bank statement analysis software is geared to analyse bank statements with speed and accuracy and detect potential fraud.
Book a demo to know more.
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