Hey Bank! Are Your Credit Decisions Biased?

We are all Biased!

Bias is inherent to humans, and recognising it requires deliberate effort. No matter how elaborate your risk policy or how experienced the analysts, decisions are inevitably influenced by personal experiences. The challenge lies in identifying when past-experience crosses the line into bias.

In lending there’s a significant focus on financial modeling and developing complex risk metrics, but idiosyncratic cognitive biases are overlooked. These biases could arise from individuals handling credit risk function or from a systemic weakness owing to culture and processes.

To understand this better, I share 7 scenarios to illustrate most common cognitive biases and fallacies that tend to impact our credit judgement. (Note: I use Credit Officer in these scenarios only for ease to represent both individual and systemic biases within the bank/lender.)

1. Fallacy of Composition: What’s True for a Part is True for the Whole

Scenario: A large retail chain has rapid revenue growth in a few regions. Based on this, the company seeks a ?50 crore loan for nationwide expansion.

Bias at Play: The credit officer approved the loan, assuming that the strong performance in a few regions meant the entire national expansion would succeed. The officer overlooked that the broader market was saturated, and consumer behavior varied by region.

Assuming success in some regions guarantees national success.

2. Fallacy of Division: Assuming What’s True for the Whole Applies to All Parts

Scenario: The pharmaceutical industry is growing, driven by high-margin branded generics and exports. A mid-sized company manufacturing APIs seeks a ?50 crore loan to expand capacity.

Bias at Play: The officer approved the loan, assuming the company's growth would mirror the sector’s overall growth. However, API manufacturers faced stiff competition from Chinese companies and volatile raw material costs, which were not accounted for.

Assuming overall industry growth applies equally to every company in the sector.

3. Hindsight Bias: It Was Obvious All Along!

Scenario: A large retail company defaults on a ?300 crore loan after expanding into tier-2 and tier-3 cities.

Bias at Play: During the review, a senior officer claimed that the company’s expansion strategy was a red flag from the start considering the potential. However, at the time of the loan approval, the expansion was supported by positive growth trends in smaller cities and company had modified its format. The issue was due to weak supply chain management and over reliance on premium products, both of which were execution errors and were not obvious.

Believing the failure was predictable after it occurred, even though the risks weren’t clear at the time of approval.

4. Availability Heuristic: Over-Reliance on Recent Information

Scenario: A renewable energy company applies for a ?150 crore loan for a solar power park in Rajasthan. The credit officer recalls a recent wind energy project failure, where delays in land acquisition and regulatory approvals led to heavy losses.

Bias at Play: The officer rejected the loan, fearing similar obstacles, even though the solar project had faster regulatory approvals and fewer land acquisition issues. The project also had government-backed Viability Gap Funding (VGF), reducing financial risk.

Overestimating risk based on recent failures, despite differences in project specifics.

5. Sunk Cost Fallacy: Throwing Good Money After Bad

Scenario: A delayed construction project has already consumed ?100 crore, and the borrower requests an additional ?25 crore to complete the project.

Bias at Play: The officer approved the additional funding, reasoning that the prior investment would be wasted without completing the project. However, rising material costs and labor shortages reduced the project’s profitability, making the investment riskier.

Continuing to invest in a project based on prior costs, rather than assessing future viability.

6. Overconfidence Bias: Assuming Success is Certain

Scenario: A logistics company specializing in last-mile delivery applies for a ?150 crore loan to expand its fleet with electric vehicles (EVs). The company has grown rapidly using its CNG fleet.

Bias at Play: The officer approved the loan, confident in the company’s aggressive growth projections and rising demand for EVs. However, the officer failed to account for infrastructure challenges like charging stations and battery maintenance and limited availability of cargo EVS, which delayed the expansion plans

Overestimating the company’s ability to navigate complexities, leading to an overly optimistic decision.

7. Confirmation Bias: Ignoring Contradictory Evidence

Scenario: A textile company with a ?100 crore turnover applies for additional working capital, despite recent declines in exports and stretched payment cycles.

Bias at Play: The officer approved the loan, relying on the company’s strong 10-year repayment history, ignoring the current red flags—longer receivables and weaker demand in key export markets. The strain due to competition from other countries was visible. The officer assumed these challenges were temporary.

Focusing on positive historical performance while dismissing current negative trends.

End Note

The key to minimising these biases is recognising them. Individual training and increased awareness can help, but systemic biases require deeper cultural and procedural changes. Simple governance structures won’t suffice. Acknowledging that these biases exist is the first step toward making more objective, informed credit decisions.

This is an abridged version of 'Credit Risk Blindspots: Hidden Biases in Lending' by Amit Balooni, published on FrankBanker