India’s AI landscape is undergoing a transformation that is not only technical, but infrastructural and ethical. At the centre of this shift are the India AI Governance Guidelines released by the Ministry of Electronics and Information Technology (MeitY), under the IndiaAI Mission, and the digital public infrastructure powering finance at a planetary scale.
Together, they are catalysing a new model of financial technology, one where underwriting, fraud prevention, lending, and customer intelligence operate within a system that rewards transparency, consent, and explainability.
From ‘Black Box’ to Explainable Credit
Financial institutions have historically been cautious with advanced AI. Risk teams and regulators worry about opaque models, biases that disproportionately punish vulnerable demographics, or decision systems that are hard to audit and defend.
Amit Das, founder & CEO of Think360.ai, an analytics startup working on a series of end-to-end initiatives, argues that the guidelines represent a key inflexion point for the BFSI sector. These “give the financial sector a clear, consistent framework for building AI systems that are fair, explainable, and auditable,” he said.
He added that the new model means “moving from ‘black-box’ models to systems where decisions can be traced, justified, and governed.”
Das highlighted that adoption lagged not due to a lack of capability or demand, but rather due to uncertainty.
“Institutions have stayed somewhat away from ML/AI models in underwriting or fraud because of a clear guideline on how they will be evaluated,” said Das.
Now, he expects a structural change: “We should gradually see the shifts in AI being enterprise-ready… the uncertainty around the use of AI in front-line workflows [will] increase, as the uncertainty around compliance goes down.”
Compute as a Public Good
IndiaAI Mission’s subsidised sovereign compute, 38,000 GPUs priced at ₹65/hour, acts as the second pillar of this transformation. For the first time, banks, fintechs, and AI-first startups have access to compute infrastructure that can compete with hyperscalers, but at a fraction of the cost.
According to Das, this is “transformational for unlocking innovation.” He noted that affordable compute and national datasets will “shrink the development cycles from quarters and years to weeks,” adding that a product manager in a bank is limited only by their imagination.
This shift democratises experimentation. It means that risk modelling teams no longer need multi-million-dollar budgets or external vendors to train production-grade models; they simply need a hypothesis and access credentials.
MeitY’s guidelines require that the speed of innovation be coupled with model governance. As Das emphasises, these tools will enable “vernacular innovation at scale,” context-aware fraud systems, and “continuous behavioural modelling and intervention,” but always within auditable frameworks.
Reducing Risk and Institutionalising Accountability
With 3,000+ datasets and a curated pool of pre-trained models specifically designed for enterprise adoption, AIKosh reconfigures the relationship between BFSI and AI vendors.
Das explains the value succinctly: AIKosh “shifts control back to financial institutions by providing curated, audit-ready datasets and models.” Instead of “blindly trusting vendor-built black boxes,” banks can validate lineage, assumptions, and performance benchmarks.
He added that such repositories make models “portable, inspectable, and testable,” dramatically lowering dependency on third parties and “strengthening regulatory defensibility.”
In practical terms, this means that the next compliance query from a regulator need not produce hand-wavy narratives about feature weights. Instead, teams can present lineage traces, bias tests, and reproducible training logs, features built into the architecture of their AI pipelines.
While the BFSI sector is known for governance-heavy operational models, MeitY’s guidelines push ethical AI from compliance checklists to core business architecture. For many enterprises, this will require cultural change.
Piyush Goel, founder and CEO at Beyond Key, a Chicago-based IT services and consulting firm, stresses that the guidelines “raise the bar by embedding ethical safeguards into basic engineering and procurement standards.” They are not only for AI labs, but “product, legal, privacy, and compliance teams must also codify the rules, logs, and incident playbooks.”
In other words, every model deployed in a lender’s stack should be treated like an employee with documentation, reviews, and escalation protocols.
Goel’s perspective is highly pragmatic. Red-team testing, model cards, bias monitoring, and escalation protocols are “not optional extras but rather practical imperatives.” He said that compliance will become a market signal as consumers and regulators want verifiable assurance of safe, understandable operations.
Consent as Infrastructure
The Account Aggregator (AA) ecosystem enables secure sharing of verified financial data across more than two billion accounts. As a result, India functions as a live laboratory where models can train on diverse, consent-driven signals.
Das warned, however, that the Digital Personal Data Protection Act (DPDP) has changed the game. Consent should no longer be soft or assumed. It must be “granular [in] purpose definition, revocability, traceability, and strict data minimisation.” He highlighted the shift “from deemed consent to explicit consent.”
“Expand access to high-quality and representative datasets, providing affordable and reliable access to computing resources, and integrate AI with Digital Public Infrastructure (DPI),” he said. For BFSI, this is particularly salient, as India’s DPI serves as the data or infrastructure backbone for many financial services.
The challenge is operational. Data pulled through APIs must be tied to permissions, purpose, and logs. Platforms like Think360.ai’s ConsenPro provide “a real-time consent and governance fabric,” so BFSI institutions can prove proper usage, Das said. They enable innovation “while staying structurally compliant.”
Responsible consent systems will be as central to risk management as credit bureaus or treasury oversight.
Revenue-Based Financing With AI
Nowhere is explainability more critical than in lending to startups and small businesses. For founders, a credit decision is not a statistical artefact, it can determine whether a team survives the quarter or lays off staff.
Abhinav Sherwal, co-founder of fintech startup Recur Club, claimed that MeitY’s guidelines “help bring more structure and accountability to how AI is used in financial decisions.” For their business, this translates to more trust with founders and lenders.
Sherwal emphasised that Recur Club already offers transparency, but the guidelines raise expectations. They require “clear documentation of how our models make decisions” and “stronger oversight on model bias and data quality,” particularly in credit decisions, because “any small bias can exclude good businesses.”
“User-first consent, founders decide what data they share, and they can revoke access,” he added. The startup uses “only what is relevant, business cashflows,” and enforces “no black-box outcomes.” All decisions have to be “explained in plain language.”
Sherwal also made a broader point that may resonate across BFSI: models are tools, not judges. The company enforces “human-in-loop for edge cases, we never let the model auto-decline without review,” he said.
Goel cautioned that subsidised compute can amplify risk if improperly used. Organisations must avoid “move fast, break trust.” To do so, production access should be gated with a “deployment approval board, threat modelling, and mandatory pre-deployment bias/security checks,” he added.
He said that vendors must “design for consent, minimal data use, and strong privacy-preserving defaults,” including encryption, least-privilege access, and DPI-aligned audit logs.
In essence, every model utilised in a lending institution’s framework should be treated with the same level of seriousness as an employee, ensuring a commitment to ethical practices and accountability in the deployment of AI technologies.
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