AI Bias in Credit Underwriting

The increasing adoption of artificial intelligence (AI) in credit underwriting has transformed decision-making processes within banking and finance by enabling faster, data-driven, and cost-efficient assessment of borrower risk. In India, where financial inclusion, digital lending, and credit expansion are central to economic policy, AI-based underwriting systems are now widely used by banks, non-banking financial companies (NBFCs), and fintech firms. However, alongside efficiency gains, the emergence of AI bias in credit underwriting has raised serious concerns regarding fairness, transparency, regulatory compliance, and long-term financial stability. The issue is particularly significant in the Indian economic context, given structural inequalities, data limitations, and the scale of underserved populations.
AI bias in credit underwriting refers to systematic and unfair discrimination arising from algorithmic models that influence loan approval, pricing, and credit limits. Such bias can disadvantage specific social, regional, or economic groups, thereby undermining the objectives of inclusive growth and equitable access to finance.

Background: AI and Credit Underwriting

Traditional credit underwriting in banking relied on manual assessment, financial statements, collateral evaluation, and standardised credit scores. With digitisation and the availability of large datasets, AI and machine learning models are now used to analyse vast amounts of structured and unstructured data, including transaction histories, repayment behaviour, digital footprints, and alternative data sources.
In India, AI-driven underwriting has gained momentum due to:

• Rapid growth of digital payments and fintech platforms
• Expansion of unsecured and small-ticket loans
• Policy emphasis on financial inclusion and credit penetration
• Constraints of traditional credit assessment for thin-file or no-credit borrowers

While these systems improve speed and reduce operational costs, they also replicate and amplify existing socio-economic biases embedded in data and institutional practices.

Sources of AI Bias in Credit Underwriting

AI bias does not usually arise from deliberate discrimination but from structural and technical factors inherent in data and model design.
One major source of bias is historical data dependency. AI models are trained on past lending data, which may reflect earlier discriminatory practices or unequal access to credit. If certain groups, such as small farmers, informal workers, women entrepreneurs, or rural borrowers, were historically underrepresented or faced higher rejection rates, the model may learn to associate them with higher risk.
Another source is proxy variables. Even when sensitive attributes such as caste, religion, or gender are excluded, AI systems may rely on indirect indicators like location, occupation, spending patterns, or language preferences. In the Indian context, geographic and socio-economic segregation makes such proxies particularly potent, leading to indirect discrimination.
Data quality and representation also contribute to bias. Large sections of the Indian population operate in the informal economy with irregular income flows and limited digital records. AI models trained predominantly on urban, salaried, or digitally active borrowers may systematically disadvantage informal sector participants.
Model complexity further exacerbates bias. Many AI systems function as “black boxes,” making it difficult for banks, regulators, or borrowers to understand how decisions are reached. This opacity limits the ability to detect, explain, or correct biased outcomes.

Implications for the Indian Banking System

AI bias in credit underwriting poses significant risks to the Indian banking and financial system. From a banking perspective, biased credit decisions can distort risk assessment, leading to mispricing of loans and inefficient allocation of capital. Overestimation of risk for certain borrower segments may result in excessive credit exclusion, while underestimation elsewhere may increase non-performing assets.
For public sector banks, which play a major role in priority sector lending and government-backed inclusion initiatives, biased AI models can conflict with mandated social objectives. If AI systems inadvertently exclude small and marginal borrowers, banks may fail to meet priority sector targets and developmental goals.
In NBFCs and fintech-driven digital lenders, where AI underwriting is often central to business models, unchecked bias can lead to reputational risk, regulatory scrutiny, and erosion of consumer trust. Over time, this may affect market stability and investor confidence in India’s digital finance ecosystem.

Impact on Financial Inclusion and the Indian Economy

Financial inclusion is a cornerstone of India’s economic strategy, with initiatives such as Jan Dhan Yojana, digital payments infrastructure, and expanded credit access to micro, small, and medium enterprises. AI bias directly threatens these objectives by creating new forms of exclusion under the guise of technological neutrality.
Marginalised groups may face higher rejection rates, unfavourable loan terms, or reduced credit limits without clear explanations. This restricts entrepreneurship, consumption, and investment at the grassroots level, ultimately slowing inclusive economic growth.
At the macroeconomic level, persistent bias in credit allocation can reinforce income inequality and regional disparities. Credit-starved regions and communities may lag in development, reducing aggregate demand and undermining balanced economic expansion. Thus, AI bias is not merely a technological issue but a structural challenge with economy-wide consequences.

Regulatory and Legal Considerations in India

The Indian regulatory framework for AI in finance is still evolving. The Reserve Bank of India (RBI) has emphasised principles such as fairness, transparency, accountability, and explainability in the use of AI and machine learning by regulated entities.
From a legal standpoint, biased credit decisions may conflict with constitutional principles of equality and non-discrimination. Although India does not yet have a dedicated AI law, existing frameworks related to consumer protection, data protection, and fair lending practices are increasingly being interpreted in the context of algorithmic decision-making.
The proposed data protection regime and digital governance policies are expected to influence how financial institutions collect, process, and use data for AI underwriting. Regulators are also concerned about the absence of explainability, as borrowers have limited recourse if they cannot understand or challenge adverse credit decisions.

Mitigation Strategies and Best Practices

Addressing AI bias in credit underwriting requires a combination of technical, institutional, and regulatory interventions. One key approach is diversified and representative data collection, ensuring that training datasets reflect the heterogeneity of India’s population, including rural, informal, and low-income borrowers.
Algorithmic audits and bias testing are essential to identify discriminatory patterns before deployment. Regular monitoring of outcomes across demographic and regional segments can help institutions correct unintended biases.
Explainable AI models are increasingly emphasised in banking, allowing lenders to justify decisions in understandable terms. This improves accountability and aligns AI usage with regulatory expectations.
Human oversight remains crucial. AI systems should support, not replace, credit officers, particularly for borderline or socially sensitive lending decisions. Hybrid models combining algorithmic efficiency with human judgement are better suited to India’s complex socio-economic environment.

Broader Significance for Banking, Finance, and Policy

AI bias in credit underwriting highlights the tension between technological innovation and social responsibility in modern finance. In India, where banking and finance are closely linked to developmental objectives, the ethical deployment of AI is as important as its efficiency gains.
The issue underscores the need for coordinated action among banks, fintech firms, regulators, and policymakers to ensure that AI-driven credit systems promote inclusion rather than exclusion. Properly governed, AI has the potential to expand credit access and strengthen financial intermediation. Poorly governed, it risks entrenching inequality and undermining trust in the financial system.