Credit scoring is important in modern financial systems, influencing loan approvals, interest rates and at marriage proposals. Scoring models analyse your financial history to assign a numerical value — typically between 300 and 900 — that lenders use to determine your creditworthiness.
Early this year, a family cancelled a wedding because of poor credit score of groom.
While this system aims for objectivity, it is a kind of a black box with certain biases that may harm a section of society. For example, families in rural areas do not have a credit history regardless of income, and therefore, they find it hard to procure loans. In May this year, Maharashtra CM Devendra Fadnavis asked banks not to insist on CIBIL score while disbursing loans.
These flaws aren’t always technical; they are embedded in historical discrimination and amplified by algorithms.
Bias in credit scoring?
Credit scoring systems tend to rely on algorithms that are trained on vast datasets of financial behaviour. However, these datasets often reflect societal inequalities, leading to biased outcomes.
For example, past data can lead to discriminations, such as practices that denied loans to specific neighbourhoods, perpetuating lower scores for those communities today.
Algorithmic bias arises from multiple sources. Incomplete data disadvantages low-income and minority borrowers, as AI models struggle to accurately predict risk without comprehensive information.
It is often emphasised that direct bias in datasets is a key concern, but indirect effects from attributes such as age, gender, or ethnicity also exacerbate the problem.
Prejudice embedded in the system
Prejudice in credit scoring isn’t always overt but systemic. Despite claims of race-neutrality, elements such as redlining echoes and exclusion of non-traditional payments (such as rent) disadvantage minorities.
This creates a feedback loop – lower credit scores lead to higher interest rates, more defaults, and further score degradation.
What you can do about it
You could start by reviewing your credit reports regularly for errors, which disproportionately affect minorities. Dispute inaccuracies with bureaus like Equifax, Experian, and Cibil requires them to investigate.
Disclaimer: Mint has a tie-up with fintechs for providing credit, you will need to share your information if you apply. These tie-ups do not influence our editorial content. This article only intends to educate and spread awareness about credit needs like loans, credit cards and credit score. Mint does not promote or encourage taking credit as it comes with a set of risks such as high interest rates, hidden charges, etc. We advise investors to discuss with certified experts before taking any credit.
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