How Balancehero Uses AI to Balance Scale, Safety, and Inclusion

As India sharpens its oversight of digital lending and data governance, the role of AI in credit underwriting is entering a new accountability-first phase. Against this backdrop, Balanchero India Pvt. Ltd., the company behind the fintech platform Balancehero, has announced enhancements to its AI-driven underwriting engine, combining alternative data, behavioral signals, and an evolving generative AI framework. 

In a conversation with CiOL, Soumyajit Ghosh, COO of Balanchero, breaks down how the company is recalibrating its credit decisioning stack to stay aligned with regulatory expectations while expanding safe and responsible financial access. 

How does the reliance on 90,000+ alternative data signals align with RBI’s data minimization norms?

Soumyajit Ghosh explains that the company’s data architecture is built for regulatory evolution. Balanchero has strengthened internal governance mechanisms and implemented structured validation processes across all impaneled partners to ensure data safety. The focus is shifting from the sheer volume of signals to precision-based, modular data groups that can be pruned or reprioritized as regulations tighten.

What are the major technical and ethical risks of deploying a generative AI–based probability of default model?

According to Ghosh, generative AI is still in the early stages for financial risk modelling, so Balanchero uses it cautiously. Automation has improved accuracy and speed, but every output is anchored with deterministic checks and rule-based layers to avoid misclassification, hallucinations, or boundary-case errors. Human oversight remains central to the PD workflow to ensure responsible deployment.

As lenders increasingly depend on AI-as-a-Service for underwriting, what systemic risks emerge?

Ghosh notes that Balanchero maintains internal control over its credit decisioning and does not rely on external AI engines for underwriting. The company’s only external vendor is a subsidiary of a listed Indian firm, which provides supporting data insights—not core credit outcomes. This reduces concentration risk and prevents dependence on a single platform influencing credit decisions at scale.

How does Balanchero ensure inclusion without unintentionally increasing debt exposure for first-time borrowers?

Balanchero manages this risk through layered exposure controls. The company uses restriction checks, mapping rules, and validation filters to prevent overextension. Instead of pushing high-value credit to new entrants, Balanchero gradually increases exposure through micro-steps, ensuring borrower safety alongside financial inclusion.

What kinds of bias or model drift have surfaced during explainable AI audits?

Ghosh says the audit process is structured to detect distortions early. Model parameters are reviewed at regular intervals to ensure no single variable disproportionately influences decisions. When drift is identified, the company rebalances signal weights and resets decision rules while maintaining operational speed. The aim is to keep the models transparent and predictable even as borrower behaviour evolves.

How will future integrations of land records, municipal data, and account aggregators shape Balanchero’s AI models?

Balanchero expects upcoming integrations with public digital infrastructure to push the models toward a hybrid scoring approach. The goal is to balance deterministic data from verified sources with behavioural intelligence. The company is preparing connectors and compliance frameworks to ensure that any new dataset is integrated responsibly and remains aligned with RBI’s consent and verification standards.