The Algorithmic Promise
For decades, getting a loan in India depended on having a solid credit history, typically tracked by bureaus like CIBIL. This system often excluded a vast portion of the population, including small business owners, gig economy workers, and rural communities,
who had no formal credit footprint. FinTech companies changed the game by introducing algorithmic credit scoring. Instead of just looking at past loans, these complex systems analyse alternative data points to assess creditworthiness. This can include everything from utility bill payments and digital transaction history to even mobile phone usage patterns. By leveraging artificial intelligence (AI) and machine learning (ML), lenders can now evaluate individuals who were previously invisible to the formal banking sector, potentially catalysing greater financial inclusion.
How Invisible Bias Creeps In
The problem is that algorithms, despite their appearance of neutrality, can learn and amplify human biases. Algorithmic bias happens when a model produces systematically unfair outcomes for certain groups. This isn't necessarily because a programmer inserted a discriminatory rule. More often, it's because the historical data used to train the algorithm reflects existing societal inequalities. For example, if past lending data shows fewer loans were given to women in a particular region, the algorithm might learn to associate female applicants from that area with higher risk. Bias can also enter through 'proxy variables'. An algorithm isn't allowed to use a protected characteristic like caste, but it might use a variable like postal code, which could strongly correlate with it, leading to indirect discrimination.
The Impact on Financial Inclusion
The irony of algorithmic bias is that it can undermine the very goal of financial inclusion it was meant to serve. While these systems open doors for some, they can slam them shut for others, creating new forms of digital redlining. An applicant might be denied a loan not because of their individual merit, but because the algorithm has flagged their spending habits, the type of phone they use, or their irregular income pattern as high-risk. This creates a frustrating 'black box' where borrowers are rejected without a clear explanation. If left unchecked, these biases can reinforce and even worsen existing economic disparities, systematically denying credit to deserving individuals and communities and eroding trust in digital finance.
The Case for Constant Monitoring
The solution is not to abandon algorithms but to manage them responsibly. This is where monitoring becomes critical. Lenders and regulators are increasingly recognising that an algorithm cannot be launched and forgotten. It needs continuous oversight to ensure it remains fair and accurate. This involves regular 'fairness audits' to test if the model is producing equitable outcomes across different demographic groups. Firms must also prioritise transparency, making the logic behind their automated decisions explainable. In India, the Reserve Bank of India (RBI) has sharpened its focus on this issue, introducing guidelines for digital lending that emphasise accountability, transparency, and fairness. The regulator has stressed the need for lenders to regularly test and recalibrate their algorithms to prevent bias and ensure they function responsibly within the financial ecosystem.
