Banks and NBFCs exist in a world where tiny mistakes get magnified into huge losses. A slight uptick in fraud approvals can erase a margin of months. A credit model that is even slightly biased can expose one to regulatory risk and brand damage. A poor onboarding process still converts fewer good customers and lets bad actors through. That’s why “AI in banking” isn’t about cool demonstrations. They’re decision systems that are measurable, auditable, secure, and resilient.
This post is intended for Western founders and product leaders (USA/UK/EU/AU) who are building products for banks or non-bank lenders, or partnering with them, and who may be considering an Indian product engineering team to execute. I’ll be very practical. I will describe the ten AI applications that are the most reliably valuable for banks and NBFCs, what actual implementations look like, what can go wrong, and how to bring these systems into your organization in a manner that survives compliance review and production reality.
A quick framing note. “NBFC” is an Indian regulatory term for non-banking financial companies, which frequently resemble a hybrid of speciality lenders, finance companies, and a number of fintech lending models. Much like non-banks also apply in other markets. What changes are the details of regulation, the data available, and the operational constraints, not the underlying engineering and risk management problems?
I will take the term “AI” in three senses according to the subject. Predictive ML for scoring & forecasting, document intelligence for extracting structured data from unstructured documents, and generative AI for drafting & natural-language interaction. Combine these incorrectly, and projects fail left, right, and centre.
Credit scoring is the obvious place to start, and it is also the simplest place to build something that “looks accurate” but still breaks in the wild. The base problem for a bank is not ‘predict default’ in a vacuum. The challenge is to deliver consistent decisions that strike the right balance between growth and risk, that meet fairness and consumer protection expectations, that result in explainable reasons for adverse action, and that don’t break when the economy turns.
Non-bank financiers and NBFCs generally have more acute incentives as their cost of funds can be higher, their portfolios more volatile, and they sometimes expand via digital modes where fraud and thin-file borrowers are more prevalent. They are also typically under more pressure to incorporate alternative data, which has the potential to increase model lift, but also increases compliance and reputation risk if such data acts as a proxy for protected characteristics.
A signal in the real world of how seriously regulators take this is the CFPB’s 2023 guidance for lenders using AI or any other black-box model: they still have to get very specific about the individual reasons for each determination if they’re sending adverse action notices and they can’t just hide behind a generic form letter if it isn’t doesn’t really provide the rationale for the decision. The CFPB also issued Circular 2023-03 regarding the same matter, putting the matter in more plain terms and stating it as an enforceable expectation under the ECOA/Reg B.
What would a positive outcome for an implementation be? It looks like a control system for decisions rather than a single model. The model generates risk predictions. A policy layer executes business rules, eligibility constraints, and overrides. A reason-code layer translates model drivers and policy triggers into human-interpretable narratives. A monitoring tier monitors drift, decay of performance, and signals of fairness borrow. A validation layer generates documentation that a bank’s model risk team can approve of.
If you do business with US banks, you will almost certainly be grilled about your model risk management and validation processes. The Federal Reserve’s SR 11-7 supervisory guidance and the OCC’s 2011-12 guidance remain benchmarks for how banks organize model governance, validation, and controls. Even if your customer isn’t legally required to adhere to those particular documents, their risk teams, in many cases, have similar expectations to those.
What surprises founders is explainability. When people hear “explainable AI,” they think it’s a UI widget. Explainability is a transactional term in lending. It groans from customer disputes, regulator probes, internal sign-offs, and strategy in the portfolio. If your model is complicated, you really want to have a process that’s rigorous enough to produce reasons that are both faithful to the model and understandable. If your outsourced team doesn’t have the vision to build this product end-to-end, the build will stall late in the project, when compliance reviews begin.
A lot of founders get focused on underwriting and ignore what happens after loan disbursement. But for banks and NBFCs, early warning and collections are also an equally big value driver because they reduce charge-offs, reduce collection costs, and, when managed well, can truly improve customer outcomes.
AI in collections is not “harassing customers more effectively.” The best systems do the reverse. They figure out which interactions are effective for which groups, what channel to use, when to communicate, and when to propose restructuring or a hardship program. It also helps uncover customers who are likely to self-cure, so your agents are not spending effort on cases that will close without escalation.
McKinsey talked about an “analytics-enabled collections model” in which machine learning determines the best treatment options for delinquent accounts and implements these through current processes. What’s important to learn here is not the brand. It’s the design pattern: the model doesn’t substitute the workflow. It enhances routing and decision-making within the workflow, but also learns over time as collectors and systems feed back results.
For NBFCs and digital lenders, the constraints of execution are sharper, as the size of their collections teams tends to be smaller in proportion to their growth, and the segment could attract more intense regulatory scrutiny and potential consumer harm. Your model needs to be coupled with policies that specify what acceptable contact strategies are, what is required for documentation, and when contacts must be escalated. If you do business in India, you have to design for the regulatory direction of travel. Reuters has reported on draft Indian regulations on illegal and unregulated digital lending, signaling a broader policy drive to rein in predatory practices. Even if you’re not in India, similar enforcement dynamics play out in jurisdictions around the world as consumer lending harm comes into focus.
A good outsourced engineering team can deliver data pipelines, decision services, and agent tooling for this category in a jiff. The weak spot is governance and customer harm controls. If your partner views collections as “just a CRM automation,” you can expect problems.
Fraud detection is among the most advanced AI areas in financial services, and it is still developing rapidly as criminals evolve. The reason why banks and NBFCs are willing to invest here is that the economics are brutal. The cost of fraud loss is immediate. A false decline also has an immediate cost as it causes loss of revenue and increased churn. Manual review is expensive and slow.
A commonly cited public signal is Visa’s claiming to have stopped 80 million fraudulently transacted things, worth $40 billion, in 2023, and that it is investing heavily in AI and security infrastructure. This is payments network example, but the same logic applies at issuing banks as well as at acquiring banks and fintechs. Sophisticated fraud stacks leverage transaction risk scoring, device intelligence, behavioral biometrics, and graph signals to find rings, along with dynamically tuned thresholds.
The practical takeaway for implementation is that detecting fraud is a problem of systems engineering as much as it is a problem of modeling. Low-latency scoring, consistent feature definitions between training and serving, and a monitoring strategy that detects drift are required since drift is guaranteed. You need some kind of fallback plan in case the model service is degraded. You need “delayed label” treatment because chargebacks come later. You also need adversarial thinking because attackers are actively probing your system.
This is where outsourcing can be very correct or very incorrect. There are many teams with the capability to train a classifier. There are fewer with the ability to build real-time feature pipelines, feature stores, streaming infrastructure, and reliability controls that actually work under the stress of production. Diligence questions should be oriented around that.
AML is a space where banks seem to burn through massive operational expenditure and yet still feel like they’re behind. Traditional, rule-based AML systems generate massive alert volumes. AI helps by surfacing higher-risk alerts earlier, reducing false positives, and improving entity linking across messy data.
HSBC has given a rare public insight into its AI financial crime detection system — known internally as Dynamic Risk Assessment — and claimed it was uncovering two to four times as much financial crime as before with “greater precision”. Google Cloud has released documentation for the high-impact partnership. HSBC has a very high transaction volume–over a billion monthly–making it a large-scale production deployment.
FATF has also issued a report or paper on new technologies and the associated risks and opportunities for AML/CFT, and highlights that the use of the technologies should be informed by conditions such as governance, data, and operational integration rather than solely driven by the purchase of tools.
What is often lost on founders is that AML “wins” are almost entirely workflow wins. The model is just one part. You want case management integration, reason codes and evidence packs, audit logs, and a retraining and drift monitoring procedure. You also have to keep privacy and access controls tight, because AML systems pull together a bunch of really sensitive data.
If your customer is a bank, expect them to treat their AML models as high-risk models within their internal model governance programs. For an NBFC customer, their governance might be lighter, but the expectations of their regulator can be quite stringent, more so with the increasing risk around digital lending and financial crime.
Banks and NBFCs gain/loose growth on onboarding friction. But they cannot relax their KYC and AML requirements. AI assists by pulling outstructured data from identity documents, bank statements, payslips, tax papers, and corporate filings, and by intelligent routing of exceptions.
A classic example has been JPMorgan’s COiN system, which Bloomberg claimed reduced the time it took to review commercial loan agreements, citing 360,000 hours of work annually that had been previously done by lawyers and loan officers. The ABA Journal reviewed that very same system and portrayed it as contract review automation for commercial lending. COiN isn’t actually “KYC onboarding,” but it is one of a series of document intelligence products that convert unstructured legal and financial documents into structured decisions.
For NBFCs in India as well, onboarding and know your customer (KYC) are governed by explicit regulatory instructions. The RBI’s Master Direction on KYC is regularly updated, and the RBI site updates up to 2025. And that is important because it reminds you remind yourself that KYC is not a one-time ask. Your product needs to adapt to continuous change.
Successful implementation relies on constructing a human-in-the-loop pipeline. You define the types of documents and the schemas of the fields. You mine with AI. You verify with deterministic checks. You pass ambiguous cases to humans with explicit justifications. You capture reviewer feedback, so the system gets better. The key metric was not “OCR accuracy.” It’s the straight-through processing rate at an acceptable level of risk.
If you are hiring out this build, demand strict data handling discipline. KYC data is the most sensitive data in your business. Poor logging and dirty test environments won’t cut it here.
Customer service operations is a massive cost center for banks, and AI is reshaping the economics. But the fact that AI generated wrong answers for customers is also where mistakes become public the quickest, because customers are given the wrong answer immediately.
Bank of America’s Erica is one of the most prominent examples on a large scale. Bank of America said that Erica has served nearly 50 million users since launch, has surpassed 3 billion interactions, and has tens of millions of interactions each month. That’s significant because it shows that AI assistants can be implemented at mass retail banking scale, and not just in niche fintech apps.
One fintech example is Klarna, which said its AI assistant managed two-thirds of customer service chats in its first month, engaging in 2.3 million conversations, and described it as the equivalent of the work of hundreds of agents. OpenAI’s use case on Klarna includes similar statistics and positions it as a significant operational deployment.
The pattern of execution that is successful in finance is that of constrained agents, not free-form chatbots. You perform retrieval from sanctioned knowledge bases. You make tool calls with rigid permissions for account-related actions. You record interactions using redaction. You implement safe rejections and escalation. You are constantly testing and evaluating the assistant, as the failure modes for genAI include hallucinations and leakage.
Security requirements are not optionally fulfilled here. OWASP’s Top 10 for LLM applications explicitly addresses risks such as prompt injection and sensitive information disclosure, which are very much applicable to banking assistants.
If you are going to try to outsource this category, judge the safety architecture maturity of the partner more than their prompt writing skill. In a bank, an assistant’s actions must withstand adversarial inputs, not just happy-path demos.
In the past couple of years, the hottest area of banking interest has now become internal copilots. It’s not as if the banks are just eager for the chance to “chat.” But because a bank’s knowledge work is costly. All day, people are reading policies, contracts, financial statements, reports, and emails. GenAI can reduce the amount of time spent summarizing, drafting, and searching, provided that the system can be restricted and audited.
Goldman Sachs is rolling out a generative AI assistant firmwide, Reuters reported in 2025, citing its ability to summarize documents, draft content, and enhance internal productivity. Reuters also reported in late 2025 that HSBC has teamed up with Mistral to speed up its generative AI rollout, comprising self-hosting models and integrating them into internal tools, reinforcing the importance of responsible AI governance.
Morgan Stanley offers a robust pattern sample. In 2024, it released AskResearchGPT – a generative AI assistant that surfaces and distills insights from its research corpus. A big takeaway from these types of deployments is that the assistant is based on trusted internal knowledge, has access controls, and comes with evaluation frameworks that test for reliability and reduce hallucination risk.
In Banks and NBFCs, this class primarily originates with policy search, call summarization, compliance drafting, and credit memo drafting. The quantifiable successes are a decrease in time per case, accelerated training for newcomers, and a more uniform output quality. The risks are information exposure, wrong guidance, and overtrust.
If you hire an outside team to do this, the single greatest failure mode is under-investing in evaluation. Your internal copilot needs to have a test suite and quality gates the same way your payment system does.
As AI advances,, so do the fraudsters. Deepfakes, synthetic IDs, and unbelievably scam scripts have all gone mainstream. Banks and NBFCs are thus leveraging AI for transaction fraud and in identity verification, authentication, and ATO (account takeover) protection.
A practical example is HSBC UK’s implementation of voice biometrics. HSBC UK stated that its Voice ID system blocked nearly £249 million in telephone fraud in one year and said it had seen a substantial drop in attempted fraud. This is a textbook example of AI-enabled authentication serving as a loss prevention control.
On the regulatory side, with the UK parliament’s Treasury Committee report on AI in financial services and recent coverage in major media describing risks such as AI-generated fraud and greater supervision required, mention of schemes such as FCA AI Live Testing and sandboxing approaches. The FCA itself announced a “Supercharged Sandbox” in partnership with NVIDIA to support firms in safe experimentation with AI.
For NBFCs in India, cyber and IT governance expectations are also codified. The IT Governance, Risk, Controls, and Assurance Practices Master Directions of the RBI were released in Nov 2023 and have been widely disseminated in secured pdf format, specifying clear governance expectations related to cyber risk management.
Execution in this domain requires defense-in-depth. Device intelligence, behavioral signals, and detection of anomalies, step-up authentication, secure management of sessions, and rapid incident response. AI contributes to detection and classification, but the control system must be designed as if attackers are actively seeking to compromise it, because they are.
Banks rely on liquidity. NBFCs are largely liquidity-driven, with funding being an additional constraint. So it is really important to have forecasting and stress monitoring beyond “nice analytics”. They impact the pricing of institutional risk and the cost of funds.
One way of looking at the increasingly widespread use of ML applied to financial market stress prediction and monitoring. BIS Working Papers have analyzed financial market stress prediction using machine learning, signaling institutional interest in ML for systemic and market monitoring. At the industry governance level, the Financial Stability Board has reviewed the financial stability implications of AI and noted that AI adoption in the absence of appropriate controls may amplify weaknesses, which is relevant for treasury and risk management functions.
Execution under this banner is very data-intensive. You need to combine account-level flows, product behaviors, customer segments, and macro signals. The models should be run through realistic backtests and should have scenario capabilities because regimes change, and you can’t just learn from existing pure patterns. In treasury and risk, the most powerful AI systems are decision-support systems that generate forecasts with uncertainty bounds and permit human overrides with audit trails.
And the last category tends to be less flashy but really valuable: compliance operations and regulatory reporting automation. Banks and NBFCs invest a lot in monitoring, paperwork, reporting, and audit readiness. AI can automate some of the manual work in policy mapping, issue triage, suspicious activity narrative drafting, and document organization, provided that the results are controlled and verifiable.
This is where governance frameworks come into play because ‘compliance automation with no governance is compliance risk.’ In the EU, the AI Act became law in 2024, and the official text is published in EUR-Lex as Regulation (EU) 2024/1689, with the European Commission stating entry into force in August 2024. This isn’t “bank-only,” but it signals a broad regulatory direction: AI systems that impact people and markets will be required to have some governance and transparency obligations.
Beyond the EU, instruments — such as NIST’s AI Risk Management Framework — afford relatively straightforward and high-level guidance on how to consider trustworthiness and risk controls for AI for any sector. ISO/IEC 42001 considers itself an AI management system standard for the ethical application and management of AI solutions. In reality, these sorts of frameworks are employed by many bank procurement and risk teams to assess vendors anyway, long before formal regulation requires them.
For founders, the key takeaway is that “RegTech AI” is more about strong traceability than complex models. Your compliance automation needs to generate proof, versioning, and logs. If you’re not able to show which rules you applied, what data went into those rules, and which model version produced the output, you haven’t reduced compliance risk. You have shifted it.
The above ten solutions have a pattern in common. The victors are not the ones with the flashiest model. The winners are the ones who create an end-to-end decision system, with governance, safety, and operational discipline at the team level.
This section is intended to serve as a playbook you can use for practical purposes, regardless of whether you build in-house, outsource to India, or use a hybrid model.
All AI applications require a small niche to start with. A particular choice. Approve a transaction. Make AML alerts a priority. Route KYC matters. Provide a restructuring plan. Prepare a credit memo synopsis for internal use. From “we want an AI platform,” you will start spinning in scope orbit and never launch.
Bind that choice to a single business-accepted economic metric. In fraud, that might be fraud loss and false decline cost. For example, in underwriting, it could be the net interest margin less expected loss and cost of operations. In KYC, that could be a straight-through processing rate at a given risk tolerance. In collections, it might be the cure rate and cost per recovered dollar. When the metric is spelled out explicitly, even every technical decision is made simpler.
In banking, documentation is not busy work. It is the tool through which risk is rendered manageable. Writing “model docs” at the end means you will get rejected by bank procurement and internal compliance.
Use well-known governance standards when appropriate If applicable. SR 11-7 and OCC guidance are the languages many model risk teams speak. If you are selling to US banks. If you are selling into the EU, you need to be aware that the AI Act is in place as a binding regulation, and customers will be looking at what that means for high-risk use cases. If you want a cross-sector framework to help structure your programme, NIST AI RMF is commonly referenced as risk thinking.
The bottom line is straightforward. You should have a living document that includes intended use, out-of-scope use, data sources, evaluation metrics, known failure modes, monitoring plan, escalation plan, and change management process. You don’t look at this as a compliance footnote. You should consider it as product design.
In banks, models should very rarely be the final decision-makers. The model generates a score or recommendation. A policy engine enforces constraints and business rules. The execution layer carries out activities through APIs and workflows with permissions and audit trails.
That separation leads to a safer system and one that’s easier to evolve. This also simplifies the management of offshoring, since responsibilities can be separated cleanly, and you don’t have as much “magic black box” behavior to manage. If a vendor delivers a monolith wherein model logic, business logic, and execution of actions are tightly coupled, it will be a nightmare to maintain.
Production AI breaks in predictable ways. Data shifts. Labels come late. Models exhibit drift. The system is being probed by attackers. Services are unavailable. Integrations stop working. Your system should be designed to fail safely, therefore.
Adversarial thinking is a must in fraud and identity. For genAI assistants, prompt injection and data exfiltration risks are very real and documented by security communities like OWASP. In lending, the requirements for explainability do not go away just because the model is complex, as the CFPB stated.
You‘ve got to define fallback behavior for every critical service. “Fail-safe” means. It’s about conservative defaults. It is about having kill switches and escalation paths. It is about leading and not just lagging indicators.
Banks purchase outcomes, but they also purchase reliability, because reliability is risk control. How you deliver is as important as what you deliver.
DORA’s State of DevOps research continues to be a benchmark for software delivery performance, and its 2024 report explores the impact of AI on software development and the significance of stable priorities and user-centricity for organizational success. Consider this a warning: if your vendor can’t deliver reliably, you’re going to rack up risk fast.
That’s particularly true when you’re outsourcing. You’re not just buying developers. You’re buying an operating model. If your operating model is poor, your AI project will be brittle, and in finance, a brittle system becomes a business risk.
India’s top teams have the potential to create their own world-class banking systems. Best founders among the worst vendor experiences are the worst when that founder outsources ambiguity and hopes the vendor will “figure out the product.” In banking AI, ambiguity is a factor of compliance risk and reliability risk.
Therefore, your assessment should focus on three things: bank-grade systems engineering, mature governance, and well-disciplined security.
Firstly, systems engineering. Inquire about training-serving skew, delayed labels, feature pipelines, real-time constraints, and monitoring. When the model service fails, ask what happens. While you’re at it, inquire them, How do they replay historical decisions? These are the questions that separate the demo builders from the production builders.
Secondly, governance maturity. Inquire of them how they would generate documentation for compliance with model risk expectations like SR 11-7 or OCC guidance when selling to US banks. Inquire how they address adverse action explainability if it touches credit decisions. Inquire with them how they consider responsible AI frameworks like NIST AI RMF. While a seasoned group will discuss this naturally, not defensively.
Thirdly, the security. Inquire how they manage secrets, PII, logging, redaction, and access controls. And if you process payments or store card data, you have to comply with regulations like PCI DSS, and the PCI Security Standards Council recently issued updates like PCI DSS v4.0.1. Even if your startup is not directly certified, your bank customer will care about whether your architecture meets these expectations.
If you want one final indicator of maturity, listen to how the team discusses evaluation. Good teams discuss test sets, failure taxonomies, monitoring thresholds, and incident playbooks. Bad teams talk mainly about model selection and prompt tricks.
