Alternative Data in Credit Underwriting

Alternative data in credit underwriting refers to the use of non-traditional, non-credit-bureau information to evaluate the creditworthiness of borrowers. In the context of banking, finance and the Indian economy, alternative data has become an increasingly important component of underwriting processes due to the large presence of informal employment, self-employment and first-time borrowers. It enables lenders to move beyond collateral- and document-intensive methods towards data-driven, inclusive and risk-sensitive credit decisions.
India’s rapid digitalisation, expansion of financial inclusion initiatives and growth of fintech ecosystems have significantly increased the availability and usability of alternative data for underwriting purposes. This shift is reshaping how credit is assessed, priced and monitored across the financial system.

Concept and Meaning of Alternative Data in Credit Underwriting

Credit underwriting is the process by which financial institutions assess the risk of lending to a borrower. Traditionally, underwriting in India has relied on income proofs, collateral, credit bureau scores and financial statements. Alternative data supplements or, in some cases, partially replaces these inputs by providing additional insights into a borrower’s financial behaviour and stability.
Alternative data includes information generated through routine economic activities, especially in digital and semi-formal environments. Its use in underwriting aims to reduce information asymmetry, improve risk assessment and extend formal credit to underserved segments of the economy.
Unlike traditional data, alternative data is often continuous, behavioural and real-time, enabling more dynamic underwriting decisions.

Evolution of Credit Underwriting in India

Historically, Indian banks followed conservative underwriting practices, emphasising collateral and relationship-based lending. While this approach ensured risk containment, it excluded a large share of economically active individuals and small businesses.
Economic liberalisation, financial sector reforms and technological advancements highlighted the need for more inclusive underwriting models. The rise of NBFCs and fintech lenders accelerated the adoption of alternative data, particularly for retail loans, microcredit and small business financing.
Government initiatives promoting digital payments, formalisation of business activity and data infrastructure have further strengthened the role of alternative data in underwriting.

Types and Sources of Alternative Data

Alternative data used in credit underwriting in India is drawn from multiple sources reflecting borrowers’ financial discipline and economic engagement. Key categories include:

• Digital payment transactions and bank account flows
• Utility, rent and telecom bill payment histories
• GST filings, invoices and business transaction records
• E-commerce purchases and platform-based ratings
• Mobile usage patterns and digital engagement behaviour
• Supply-chain and merchant data for small businesses

These data sources help lenders assess income stability, spending patterns and repayment behaviour in the absence of formal documentation.

Role in Banking Credit Underwriting

In banking, alternative data enhances traditional underwriting frameworks by filling information gaps. Banks use alternative data to assess new-to-credit borrowers, verify income indirectly and identify early signs of financial stress.
Incorporating alternative data allows banks to:

• Improve risk segmentation and borrower differentiation
• Reduce dependence on collateral-based lending
• Shorten loan processing time and reduce costs
• Enhance monitoring of loan performance post-disbursement

This strengthens portfolio quality while supporting responsible credit expansion.

Significance for NBFCs and Fintech Lenders

NBFCs and fintech institutions rely extensively on alternative data as a core underwriting input. Their business models prioritise speed, automation and scalability, which are enabled by data-driven decision-making.
Alternative data allows these lenders to offer small-ticket, short-tenure and customised credit products to underserved segments. Risk-based pricing and flexible repayment options are facilitated through continuous data analysis.
Partnerships between banks and fintech firms have also promoted the mainstreaming of alternative data underwriting practices within the financial system.

Implications for Financial Inclusion

Alternative data in credit underwriting plays a crucial role in advancing financial inclusion in the Indian economy. It enables formal lenders to serve individuals and enterprises traditionally excluded due to lack of documentation or credit history.
By recognising behavioural indicators of creditworthiness, alternative data reduces reliance on informal moneylenders and lowers borrowing costs. This supports entrepreneurship, income generation and resilience among low-income households and small businesses.
Inclusive underwriting practices also contribute to the broader goal of integrating informal economic activity into the formal financial system.

Regulatory and Governance Framework

The use of alternative data in credit underwriting is subject to regulatory oversight to ensure fairness, transparency and data protection. The Reserve Bank of India encourages innovation while emphasising responsible lending, borrower consent and data security.
Consent-based data sharing mechanisms ensure that borrowers retain control over their personal information. Financial institutions are required to maintain robust data governance frameworks and explainable underwriting models.
Regulators also monitor the use of automated decision-making to prevent discriminatory outcomes and excessive risk-taking.

Challenges and Risks

Despite its advantages, the use of alternative data in underwriting presents several challenges. Data accuracy and standardisation remain concerns, as alternative sources may be fragmented or inconsistent.
Digital exclusion poses a risk, as borrowers with limited access to digital platforms may still be underserved. Privacy and cybersecurity risks are significant, given the sensitive nature of personal and behavioural data.