Finance is among the best areas to leverage AI, and one of the easiest in which to get burned by it.
It is the best place because finance involves enormous amounts of repetitive decisions, a high frequency of events (such as payments, authorizations, and trades), and very messy data that people don’t have time to review (contracts, KYC files, chat logs, call transcripts, and research reports). And it is the best place because “value per correct decision” is often high: given its averted fraud ring, it’s prevented compliance miss, it’s improved credit decision, it’s a better hedge, it’s faster onboarding funnel.
It’s a place where it’s easy to get burned because finance is a regulated, safety-critical industry in practice, even when the product looks like “just software.” It’s not just a question of how accurate the models are. The real risks are model drift, latent bias, explainability & adverse action obligations, auditability, data leakage, security, and operational failure modes at scale. Regulators have cautioned that AI could amplify systemic risk as vast numbers of firms follow similar signals, depend on the same infrastructure, or automate decisions without robust controls.
This article is intended for Western founders and product leaders (USA/UK/EUEC/AU) who want an unbiased, evidence-first look at the leading AI solutions in finance, what “real” use cases look like, and what it takes to implement them safely—particularly if you’re thinking about building with an Indian product engineering team or agency.
I’ll be doing two things at the same time: first, I will be walking you through ten categories of AI solutions that consistently produce ROI in financial systems of record; second, I’ll be handing you a practical playbook around implementing covering data, security, model risk, and delivery execution so you can ship without surprises.
A brief remark on language. When people in most teams say “AI,” they mash three different things together. Predictive ML (risk scores, forecasts, anomaly detection). NLP and document intelligence (deriving meaning from text, contracts, IDs). And generative AI (LLMs that generate text, summarize, and answer questions). They each have different ways of failing, different control schemas, and different regulatory implications. Conflating them is one of the quickest ways to build the wrong system.
It’s not that the “Top 10” should be interpreted as “the only 10”. These are the ten areas in which finance teams repeatedly invest their funds and continue to invest because the systems deliver. As soon as the AI hype cycle shifts, these issues remain.
If you had to pick one AI area with a long track record of measurable value in finance, it’s fraud detection in payments. It’s largely a simple concept: A payment authorization is a real-time decision that must be made in the presence of uncertainty. You have milliseconds to decide on whether this transaction is legitimate, and the criminals who are running this business adapt more quickly than manual rule teams can keep up.
Traditional fraud engines are based on different types of models. It is common to see gradient-boosted trees or deep learning classifiers for risk scoring, graph features to detect rings (shared devices, shared merchants, shared email patterns), anomaly detection for “never seen before” behavior, and adaptive thresholds that adjust per channel and loss tolerance. You also see reinforcement-style optimization in some systems to balance approval rates versus fraud loss.
The forms of “real value” are generally fraud prevention, among other things, reduced false decline rates, and less effort in manual review. Visa has said that its AI-driven Visa Advanced Authorisation contributed to the prevention of an estimated $25 billion in annual fraud, according to its calculations. Reuters also cited a recent report saying Visa stopped around $40 billion in fraudulent transactions in 2023, highlighting AI and such technology investments as a major contributor. Don’t hold any particular number up as universal, but the trend is: Established payment networks consider risk scoring using AI to be part of the baseline infrastructure and not an experiment.
The first hard truth about an implementation playbook: “accuracy” isn’t the product metric. Your product metrics are the following: approval rate, fraud rate, chargeback rate, false decline cost, review queue size, and time-to-decision. Your model’s targets and thresholds need to be calibrated to those economics, and separately by segment (merchant category, geography, channel). Instead, “there is a pattern,” said Yasemin, “people try to optimize for a single global AUC while disregarding business trade-offs.” You can create a model that looks fantastic in offline evaluations and still lose money because it turns down far too many good customers.
From a technology perspective, fraud is also where you learn what “production AI” really means. You require features, in real-time, with feature definitions that are consistent in training and serving, plus a monitoring layer that identifies drift as fraud patterns evolve. In payments, drift is not a “maybe.” It happens.
If you are outsourcing this to an Indian team, the due diligence is not to see if they build a model. A lot of teams can. Your due diligence is do they make low-latency scoring services, stream pipelines, feature stores, and monitoring that can withstand adversarial behaviour. Ask them how they avoid training-serving skew, how they manage delayed labels (chargebacks roll in later), and how they build “fallback” modes when the model service is down. These answers tell you whether they have built real systems or only demos.
AML is one of the most painful and most impactful areas of banking and fintech. Traditional systems for AML are driven by rules, produce massive alert volumes, and exhaust compliance teams with false positives. AI assists by recognizing patterns that rules do not catch, ranking alerts, and connecting entities across data sets to reveal higher-risk clusters.
A prominent real-world instance is HSBC’s collaboration with Google Cloud on what HSBC refers to as Dynamic Risk Assessment. HSBC states that the system enabled them to detect “two to four times more financial crime than we did previously, with much greater accuracy.” Google Cloud’s own press material about its AML AI product mentioned HSBC taking an AI-first approach for transaction monitoring in strategic markets and highlighting enhanced detection and processing throughput, framing this as a production deployment and not a lab pilot.
Regulators and standard setters also consider technology-led AML a valid route, albeit with prerequisites. FATF has issued guidance on the potential benefits and challenges of new technologies for AML/CFT and stresses that effective utilization is determined by factors such as data availability, governance, and operational integration, and not solely by the acquisition of tools.
Execution is not so much “pick an algorithm” as it is redesigning the process. AML models this compliance function does not replace. Triage is modified. It determines which alerts get the most human time first. They provide better narratives for SAR drafts. They assist in entity resolution, so that “John A. Smith”, “J. Smith” and “JAS Ltd.” cease being viewed as distinct universes.
There are two tough engineering challenges you need to be prepared for here. First, entity resolution across dirty data. And two, explainability and audit trails. AML is not like a movie recommendation engine. You’ll be asked, “Why did you flag this?” and “Why did you not flag that?” You need reason codes, decision logs, model versions, and reproducible replays in regulated environments.
If an indian agency is doing this for you, demand an architecture that decouples “detection signal” from “case management workflow.” You want models you can iterate on without breaking your case system and a case system that can take multiple detection sources (rules, models, third-party intelligence). You will also want explicit controls for sanctions screening and adverse media workflows, as these are frequently subject to different regulatory expectations than transaction monitoring.
Credit is where AI generates the most upside and the most regulatory risk.
On the positive side, the more accurate risk models lead to fewer defaults, are priced more accurately, and ultimately lead to more credit when done in compliance with regulations. Exposure-wise, credit decisions have explainability requirements in several jurisdictions, and biased models can cause legal and reputational harm.
A commonly mentioned production example in the US is Upstart, which brands itself as an AI lending marketplace for bank and credit union partners. In its submissions, Upstart portrays itself as an AI lending marketplace that connects consumers with banks and credit unions using its AI models and applications. Don’t take it as specific advice to use Upstart; the advice is that several lenders have been moving towards ML-informed underwriting and pricing as a moneymaking/competitive lever, and the regulators are watching.
In the US, the CFPB has specifically reminded lenders that the use of AI, or other complex models, does not eliminate the obligation to provide specific and meaningful reasons when an adverse action (i.e., credit denial) is taken. This is one of those places where “just use a black box model” is not a product strategy. So, it’s a regulatory risk.
The use of simulation in credit should be conceptualized as a system at-predictor decision engine. You can have a clear split between the “risk score creation,” the “policy rules,” and the “human override.” You need reason codes that correspond to intelligible drivers. You need fairness testing (on protected classes, where appropriate) and a thorough examination of proxy variables that act like protected attributes. You need a monitoring program as well because borrower populations evolve, economic conditions change, and models can drift into unfairness even if they were fair at the time of launch.
Credit is also where banks seem to put the most rigorous governance around model risk management. Guidance such as SR 11-7 and the OCC’s model risk guidance in the US highlight strong validation, governance, and controls around model development, implementation, use, and validation. If you are not a bank, but do business with banks, you take on their expectations.
If you are working with an Indian team, the major question is not “can they create a model,” but “can they create the governance artifacts.” Banks require validation reports, monitoring plans, and model documentation. A good delivery team knows how to make these things as part of the build, not as an afterthought after procurement insists.
Finance has traditionally had a customer support cost problem, and AI materially shifts the unit economics. This is also the category in which the public fails the fastest because customers are directly impacted.
Bank of America’s Erica is probably the best-known large-scale AI-driven virtual assistant in consumer banking. Bank of America has announced that its virtual assistant Erica has served close to 50 million people since launch, exceeded billions of interactions, and is now used for tens of millions of interactions per month. This matters because it is AI support at “mass retail banking” scale, not just in a fintech app with a narrower mission.
For fintechs, Klarna has publicly stated that its AI assistant managed two-thirds of customer service chats in its first month, engaging in 2.3 million conversations, and that it led to a reduction in repeat inquiries and faster resolution times. OpenAI’s case study on Klarna repeats similar numbers and presents it as a large operational deployment rather than a limited pilot.
Implementation of this is really about guardrails. LLMs are good language models, but they hallucinate, make overconfident assertions, and can leak private data if you design your system badly. You’re looking for “tool use with permissions,” not a “free chat.” You want the assistant to access account state via controlled APIs, cite what it used, and escalate when confidence is low, or the request is sensitive. You should also have a QI program that tracks both hard metrics (resolution time, containment rate, cost per incident) and safety metrics (error rates in sensitive categories, privacy incidents, advice-related harm).
Security and privacy cannot be compromised. OWASP has explicitly addressed typical LLM application vulnerabilities such as prompt injection and exposure of sensitive data. Your finance helper is an especially tempting target for these attacks since it sits so close to money-moving workflows and personal data.
If you outsource this build, demand that the vendor can articulate their “safety architecture” without buzzwords. Inquire about their measures against prompt injections leading to data exfiltration. Inquire about their methods for logging and redacting transcripts. Inquire about their development of human escalation procedures. If you’re told “we’ll just fine-tune a model,” that’s usually a warning sign. Most production finance assistants are retrieval and tools systems with very strict controls, not pure fine-tunes.
Finance is based on paperwork. IDs, proof of address, bank statements, tax forms, payslips, company filings, loan covenants, ISDA schedules, and contracts in ever-increasing pages. People take time and cost a lot, and when it comes to you reading 1,000 pages of these documents, that’s inconsistent. Document intelligence is one of the most dependable areas of AI for ROI, as it removes manual work while increasing accuracy.
A well-known illustration is JPMorgan’s COiN (Contract Intelligence). Bloomberg stated that the tool could perform in seconds that previously required lawyers and loans. officers many hours, and cited a number of 360,000 hours of work consumed previously in a year. The ABA Journal also reported on the article, bolstering the claim that COiN was being employed to analyze commercial loan agreements and repeating the magnitude of time conserved.
Contemporary document pipelines combine OCR (if required), layout-aware extraction, classification, entity extraction, and validation rules. LLMs can also assist in summarization and clause extraction; however, in regulated workflows, you most often still root decisions in structured extraction and deterministic checks.
Implementation begins with ground truth. Don’t underestimate labelling. If your onboarding team can’t unanimously say “here’s exactly what a ‘valid address proof’ looks like in each country,” then your model isn’t going to fix that. Your best route is to establish a taxonomy of document types, specify required fields for each product, create a human-in-the-loop review application, and acquire feedback loops that enhance extraction quality.
A second key is exception handling. The true value is realized once the system can auto-approve the large majority of clean cases, and any outliers are sent to humans with specific reasons. That’s where the routing logic is, where operational wins occur.
If you’re working with an Indian agency, this is one of the categories where a very good team can really shine because it’s so much about engineering, workflows, and UI, not strictly on research. But you should also verify that they are aware of data privacy, PII redaction, and secure storage. Among the most sensitive data you handle is KYC data. Treat it as such.
This is where generative AI can deliver visible productivity improvement, but only when the system is closely tied to trusted internal knowledge and compliance restrictions.
Morgan Stanley is a compelling public example. The firm released AskResearchGPT, a generative AI assistant that helps users surface and digest insights from its research corpus. It also announced “AI @ Morgan Stanley Debrief,” an OpenAI-based tool to create meeting notes, action items, draft emails, and store notes in Salesforce, with client allowing—based on client consent. Morgan Stanley’s own write-up on Morgan Stanley stresses that the deployment was subject to a comprehensive evaluation framework to ensure reliability and consistency.
These examples matter because they illustrate the typical pattern that applies in finance: don’t let the model make up facts. Connect it to trusted internal sources. Measure it against specific tasks. Put a human approval loop on client-facing outputs.
Execution really depends on the quality of retrieval, evaluation, and supervision. You need to have adequate, strict permission levels so that advisors only have access to what they need. You need citations in the UI so the advisor can check where an answer came from. You need a “model behavior contract” stating what the assistant refuses to do , what it escalates to, and what it always verifies. You need the audit logs because compliance is going to ask what the tool said and why.
If you are outsourcing this build, evaluate the vendor’s maturity by how they talk about evaluation. A team that has actually shipped real GenAI products will speak about test sets, failure taxonomies, and continuous monitoring, not just about prompts. If they can’t say how they test hallucinations, assume they will produce hallucinations.
This category is more important than many founders realize, as it is where AI converges with regulatory enforcement. Broker-dealers and trading firms must also surveil for suspicious conduct, manipulation, and communications risks. Manual review doesn’t scale. AI and ML techniques also help identify patterns within trading, communications, and behavioral signals.
FINRA has issued a report on AI in the securities industry that specifically mentions firms building surveillance and conduct monitoring solutions that utilize deep learning models. This is a good signal on what constitutes “real” use: It is not speculative. It’s already how firms do monitoring.
Execution starts with data integration. Detecting market abuse frequently necessitates the correlation of order events, execution data, market data, client profiles, and even communications context. The alerts need to be explainable by the system. “Compliance people want to know why the system picked something up and what the evidence is behind it.” Unlike consumer apps, “black box and hope” is not how Business — or government — should work. False positives are costly because they generate review work, but false negatives are worse because they create enforcement risk. You need precise thresholding and constant adjustment.
If you are doing this with an Indian team, your primary diligence is domain expertise and audibility. Tell them to demonstrate how they would generate an “evidence pack” for each alert that can hold up during an internal audit. Ask them how they would version models and features so you can reproduce decisions made in the past. If they have never operated in regulated monitoring environments, they might not know that reproducibility is non-negotiable.
Not all “finance AI” revolves around fraud or compliance. Corporate treasury is a massive value area because accurate cash forecasting decreases borrowing costs, avoids liquidity surprises, and enhances capital allocation. Traditionally, many companies forecast cash flow with spreadsheets and tribal knowledge. AI disrupts this by learning patterns based on transaction history, seasonality, billing cycles, and operational signals.
J.P. Morgan has released content on its AI-driven cash flow forecasting methodology and is presenting it as a revolution in treasury operations, as well as a detailed customer story with Amtrak on using its Cash Flow Intelligence tool to improve forecasting accuracy. These are useful in that they demonstrate that major players are turning AI forecasting into enterprise tools, which is an indicator of lasting value.
Execution is a data engineering problem first. You need a crisp categorization of cash flows, you need a consistent mapping across accounts and entities, and you need a strategy for sparse or changing patterns. You also have to deal with “exogenous events” that disrupt patterns, such as changes in policy, renegotiations of contracts, or shocks to the supply chain. The best systems integrate statistical forecasting with business overrides and contingency planning. The model provides a baseline, and humans apply judgment when reality diverges.
If you’re outsourcing this build, make sure the team knows about time-series evaluation and leakage. Many prediction errors are a result of unintentionally training on future data or testing on unrealistic splits. A more sophisticated team will be able to talk about backtesting properly and show how they treat new entities with little history.
Fraud is not just a transaction issue; it is also related to identity and access. Account takeover, social engineering, SIM swaps, and deepfake-enabled impersonation are emerging threats. AI also plays a role in identifying anomalies in login activities, device usage, voice, and biometric signals, and in alerting on suspicious interactions prior to money moving.
HSBC UK said its Voice ID technology had stopped attempted fraud of nearly £249 million over a year, and it had seen a marked drop in attempts to scam its telephone banking customers. This is a tangible example of biometrics applied as a security control with quantifiable results.
Regulators are also explicitly cautioning about adversarial use of generative AI in fraud. FINRA’s literature includes guidance on threat actors abusing generative AI to engage in cyber-enabled fraud. And that’s important because it shifts the nature of the threat. Your verification system should assume the adversary can produce coherent text, speech, and possibly even synthetic media.
Execution is, in part, about ML models and in part about system design. A robust identity system employs layers of defenses: device intelligence, velocity checks, risk scoring, step-up authentication, and “friction only when needed.” They also have abuse analytics because fraudsters are continually probing your edges until they find the least resistant path.
If you’re building this product with a third-party vendor, demand security engineering leadership. This is not a place where you want “best effort.” Inquire about the standards they adhere to. Inquire about secrets, logs, and incident response. If you are handling card information in any way, you also need to be aware of the PCI DSS requirements and map your engineering controls to them.
The final category is by far the most misunderstood. A lot of people think “AI in markets” is a prediction. In practice, the most resilient consumption is often in optimization and surveillance. AI can optimize execution for lower market impact, and it can be alert for stress signals that humans miss.
Upon execution, public references to JPMorgan’s LOXM system do appear. For instance, the Ontario Securities Commission’s report on AI in capital markets cites an equity execution system grounded in deep reinforcement learning called LOXM for trade execution. The important message is not “predict the market.” It is “an efficient execution of large orders in the presence of evolving liquidity.”
On monitoring and early warning, the BIS has issued work on ML to use in monitoring financial market stress and related process dynamics. This is significant because it demonstrates that serious ML-based institutions view ML as a system monitoring tool, not only for alpha.
Execution in this space involves trade-offs around limits, security, and assessment under regime shifts. Market dynamics alter radically in periods of stress. Models that look fine to use in normal market conditions can break down when liquidity dries up. Any “AI execution” system needs to have guardrails to prevent it from destabilizing the system and needs to be tested in simulated stress scenarios.
If you are a startup that is building products in this space and has outsourced development, your success or failure hinges on your own quantitative engineering depth and risk controls. Request evidence that they have developed low-latency systems, are aware of backtesting pitfalls, and can deploy kill switches and limit checks similarly to a real trading system. And if they can’t articulate how they would prevent feedback loops and runaway behavior, they’re not ready.
Now that we have ten solution categories, the larger question is: How do you put any of these into practice in a way that works in the real world? Most failures aren’t due to a “bad algorithm.” They come from weak product framing, weak data foundations, weak governance, and weak operational controls.
This playbook is written for use whether you build in-house, through an Indian agency, or through a hybrid model.
Every successful financial AI begins with one obvious choice or flow. Accept or reject a transaction. Tier AML alerts. Route KYC cases. Write a meeting summary. Predict cash. Suspect jump trading. When you start with “we want an AI chatbot,” you usually end up with something that talks well and fails when it matters.
Be clear about what the specific decision is, what a positive outcome looks like, and what the cost of mistakes is. In fraud, a false negative costs money; a false positive costs approval and customer trust. AML false negatives cost enforcement risk; AML false positives cost compliance labor. False negatives cause credit to shrink, false positives cause defaults to increase, and they can also violate notions of fairness. You can’t tune a system if you don’t price errors.
And that’s also where you say, well, is the system permitted to make decisions and act on its own, or is it just supposed to advise people? Many of the best finance AI wins come from decision support first, and then automation second, after the system has been proven.
Two groups usually arrive late to the party: legal/compliance and security. That’s costly, in finance.
If you do business in or sell to the EU, you need to know how the EU AI Act defines systems and what obligations you face. The text of the AI Act, as submitted to the European Parliament, applies as of 1 August 2024, says the European Commission in a statement. If you design systems that touch credit, insurance, and access to vital services, assume more scrutiny.
If you are in US lending, you need to be aware of adverse action requirements and explainability expectations. The CFPB has made clear that when using AI or complex models, it does not absolve from including specific and accurate reasons for an adverse action.
You inherit the banking rules when you sell to banks. US supervisory guidance, like SR 11-7 and OCC model risk guidance, places a strong emphasis on validation, governance, and controls over model development and use. Even if your customer doesn’t have to apply these frameworks to your product by law, more often than not, their procurement and risk teams will.
A realistic approach to managing this is to leverage an existing risk framework for AI. NIST’s AI Risk Management Framework is emerging as a common method to consider trust, measurement, and risk mitigation in a structured notational form. For enterprise governance, ISO/IEC 42001 is being developed as an AI management system standard that meets risk and governance demands.
The bottom line is that you don’t need to “get certified” on day one. The point is you have to create governance artifacts as you create the system, because bolting on governance is slow and painful.
Finance AI suffers when data is treated as “whatever data we have.” Real systems have to have well-defined lineage, well-defined definitions, and well-defined permissions.
You have to know which data you are allowed to use, with what consent, in which geography, and for what purpose. You need to know how long you can keep it, where it can be processed, and who can see it. If you are leveraging third-party models or LLM APIs, you also need to know what data leaves your environment and what is retained.
For regulated customers, you should assume they will want these things spelled out explicitly. “Where is the data stored?” “Who has access?” “How do you prevent sensitive data leakage?” “How do you handle deletion requests?” If you can’t answer, you’ll lose deals.
This is one of those things at which a good Indian engineering partner can be a massive help if they’re mature: creating data pipelines, role-based access, logging, and structured storage. However, it will also be a place where an immature team will silently build massive risk by copying production data into test environments, storing PII in logs, or using real customer data for prompt development.
Specify data processing in the contract, during the delivery process, and in the engineering architecture.
In finance, it is essential that AI fail gracefully.
That means you design for degradation. In fraud scoring, you degrade to rules-only mode with more stringent thresholds if the model service is not available. In customer support, if the assistant isn’t sure, it hands things off to a human. In credit, if the model input is incomplete, you fall back to manual review. If the system can’t calculate features in AML, it shouldn’t quietly return “low risk.”
Fail-safe design is also about damage control. Even when a system is “working,” it can be wrong. So you add permission boundaries. The model recommends, but a policy engine enforces the decision. The model drafts, but a human signs off. The model can flag, but compliance monitors.
This is the distinction between high-risk activity and low-risk activity. Summarizing a research report for an internal colleague is not the same as producing client-facing advice. A system that is safe in one place can be dangerous in another.
If you build anything on LLMs, you have to treat it as a new attack surface. OWASP has listed LLM-specific threats, including prompt injection, insecure output handling, training data poisoning, and confidential material exposure. These risks aren’t theoretical in finance. Attackers want money and personal data.
It’s common to see unsafe patterns where an LLM is allowed to directly call tools with very broad permissions. A safer pattern is mediated tool use. The model proposes an action. A policy layer determines if the user can do it. The system acts. It never sees raw secrets, the model. The model doesn’t get to make permission decisions.
When you outsource GenAI development, the quickest way to identify a dubious team is to ask how they deal with prompt injection. If they look confused, you need to slow down. If they answer with a neat architecture—input sanitization, tool gating, least privilege, and red-team testing—you’re talking to someone more mature.
Model risk management sounds like “bank bureaucracy” until you deploy an AI system and can’t explain what it did.
A mature financial AI build is, by default, documenting use cases, training data sources, evaluation metrics, known failure modes, validation results, monitoring plans, and versioning. That is precisely the type of discipline described in bank model risk management guidance.
For GenAI systems, you also need evaluation schemes that assess factuality and refusal behavior, leakage of privacy, and ability on critical tasks. OpenAI’s Morgan Stanley case study highlights evaluation frameworks to achieve performance you can rely on. This is not Morgan Stanley’s problem; it is just one example. It’s a general lesson: without evals, you have no idea what you shipped.
If you are working with an Indian agency, make it clear that evaluation is a part of the scope. Not an option. Not “later.” It must be a deliverable.
Even if your AI system is brilliant, it won’t work when the delivery is chaotic. In finance, the value of the product includes reliability.
It’s helpful to view software delivery performance and reliability through a quantifiable lens. The DORA State of DevOps report remains a staple for delivery metrics and touches on how AI intersects with software delivery performance. The point isn’t that you have to copy some particular report. The point is that you have to run delivery with a clear release discipline, testing, observability, and incident response.
You’re not just buying “developers” when you outsource. You are purchasing their operating model. If their operating model is flawed, you get outages and delays and latent quality issues.
Now for the only part that most founders actually care about: If you are going to build these systems with an Indian agency or team, how do you distinguish between a team that can ship real financial AI and a team that will ship a pretty demo and vanish?
The most important step is to evaluate them as you would assess a senior internal team, not as you would a vendor proposal.
Begin by raising the bar on specificity. Have them tell you about a single production finance AI system they built, the precise business metric it improved, and the most difficult failure they had to fix. If they cannot discuss failures, they likely haven’t shipped at scale. Inquire how they manage audit logs and explainability. Inquire as to how they manage data privacy and redaction. Inquire how they manage model drift. Inquire about the outcome when the model service is unavailable. Inquire about their testing of GenAI outputs for hallucinations and leakage.
Then determine whether they speak the language of governing. Are they able to generate documentation towards risk frameworks such as NIST AI RMF? Are they familiar with bank model risk expectations such as SR 11-7? Do they meet expectations for lending explainability, such as those outlined in CFPB’s adverse action guidance? If they treat these as “paperwork,” you should anticipate more friction with regulated customers.
Lastly, determine whether they can develop secure systems. If you process payments or store card data, you need to know about PCI DSS. If you process identity verification, you need to know fraud threat models, and regulators are specifically calling out generative AI–enabled scams. This isn’t a place for “we are going to add security later.”
The best Indian teams are world-class at engineering execution when the problem scope is well defined, the governance is known, and the collaboration is structured. The worst consequence is when the founders delegate murkiness and hope the vendor will “figure out the product.” Ambiguity is a risk in finance AI.
