Fintech and payments are areas where AI quietly makes (or breaks) a business.
Not because “AI is cool,” but because payments is such a fast-paced, high-adversary, high-precision environment. You are making decisions in real time in an environment of uncertainty, where money is on the line, and adversaries are trying to find your weakest link. At the same time, you’re struggling with a conversion problem. Every false decline is forgone revenue. Every additional step at checkout is dropoff. Every delay in KYC is a customer who doesn’t come back. Every chargeback is not only a loss, but also an operational burden and a reputation strain.
That’s why the most powerful AI in payments doesn’t look like a chatbot demo. It looks like a number of decisioning engines that sit within the payment lifecycle – who you onboard, what you accept, what you decline, how you route, how you recover, how you investigate, and how you prove to your partners and regulators that you did the right thing.
This post is written for Western (USA/UK/EU/AU) founders and product leaders who are currently evaluating the fintech and payments opportunity, and in some cases, considering Indian product engineering teams or agencies to build. I’ll name 10 AI solution categories that continuously provide quantifiable value, and show you how to do it in a bank-grade way, with security, compliance, and operational resiliency baked in.
A quick definitional note: When I say “AI” in this post, I’m referring to three distinct toolsets that get conflated. Predictive machine learning involves scoring and forecasting (fraud risk scores, churn risk, default risk). Document and identity intelligence is the process of pulling truth from chaotic inputs (IDs, selfies, bank statements, corporate docs). Generative AI is about language and summarization (support agent assist, dispute narratives, internal copilots). There is a separate failure state for each. Don’t assume they’re the same, or you’re going to build the wrong controls.
Here’s one more comment that matters if you’re selling to enterprise merchants, banks, or big PSPs. In payments, “trust” is a product feature. Standards and expectations such as the PCI DSS are in place because payment systems cannot rely on good intentions. PCI DSS v4.0.1 has been released as a limited revision to clarify intent without introducing new or removing existing requirements, and provides a baseline of technical and operational controls to protect account data. Your AI system will be embedded in that, and so it has to be able to get through security reviews, audits, and incident response — especially if you outsource any piece of the build.
In that spirit, let’s step through the ten most common AI solutions that appear again and again in strong fintech and payments products.
If you work on anything that involves card-not-present payments, bank transfers, wallets, or P2P, transaction fraud is not a “feature.” It’s existential.
Fraud is also the area where AI has the longest track record in payments because rules by themselves don’t evolve quickly enough. Criminal activity varies. Fraud rings coordinate. Hackers probe your boundaries, decode your limits, then automate the attack. The winning formula is a layered approach: machine learning risk scoring + rules + device intelligence + behavioral signals + human review for edge cases.
That gives you a sense of where this lies in being taken seriously at a network scale. Reuters cited that Visa had stopped 80 million fraudulent transactions with a value of $40 billion in 2023, attributing the success to investments in sophisticated technology, including AI and data security. That is not to say your startup needs to reach Visa-scale numbers; it just shows the most mature payments players consider AI risk scoring as core infrastructure.
Radar at the PSP layer Stripe is now promoting Radar as an AI-powered fraud system that attaches risk scores to payments using information from network signals, and Radar is said to reduce fraud by 38% on average. Stripe also briefly documented the engineering complexity of Radar, such as that it evaluates over 1,000 features of a transaction and comes to a decision in under 100 milliseconds, which is precisely the sort of latency constraint you have in real payment authorization. Adyen calls its approach to chargeback management through RevenueProtect the best of static rules and machine learning to fight fraud and maximize conversion. Decision Intelligence has been positioned by Mastercard as “AI / ML transaction risk monitoring,” which computes transaction scores and reason codes based on insights from your network.
The operational lesson from all these examples is the same: the model is only the beginning. The entire system includes feature pipelines, decision services, fallback behavior, and monitoring.
You have to build your fraud system around the economics of the business, not the model metrics. “Accuracy” is not a KPI. Approval rates, fraud rates, chargeback rates, false declines, manual review queue size, and customer friction are KPIs. A fraud system that catches more fraud but turns away too many good customers can lose money. A fraud system that lets too many risky customers through can also lose money. You want to be able to tune to the appropriate frontier, by segment, because the best such tradeoff varies for subscription merchants, marketplaces, high-AOV luxury, gaming, travel, and so forth.
You also need to account for delayed labels. In card payments, the label frequently comes in the form of a chargeback weeks afterwards. This makes training and evaluation tricky. If you outsource this work, your strongest diligence question is whether the team understands the time dynamics and can describe how they train models when “ground truth” comes in late. Teams that have mainly built offline demo models often miss this and ship something that looks good in a notebook but breaks in production.
And finally, you need to think like an adversary. Fraud is not static. It’s an adversary. That means you should anticipate “probing attacks” and “edge path attacks.” Attackers will try your weakest avenue first, such as refunds, partial captures, wallet top-ups, account linking, or promo credits. Your system has to consider the whole money movement lifecycle as a risk surface, not just the first payment.
Too many fintech teams obsess over transaction fraud, and the identity layer is underfunded and underinvested in. That’s a mistake; account takeover frequently becomes the lowest cost route to simply steal money when you tighten up your transaction controls.
Account takeover protection is a combination of device intelligence, behavioral biometrics, anomaly detection, and step-up verification. Here, the AI role is pattern recognition among behavior signals, which cannot be manually encoded as rules by humans. Also, it is continuous as attackers change their behavior when you introduce friction.
This category is evolving rapidly as generative AI makes social engineering easier. It’s now inexpensive to produce compelling scam messages, screenplays, and even synthetic identities. So your risk model has to assume the adversaries are able to mass-produce persuasion and impersonation.
Identity verification products give tangible samples of how AI is employed here. Stripe Identity says it uses machine learning to identify fake IDs and spoofed images, as well as to compare ID photos to selfies. how Stripe Identity leverages advanced machine learning to match a live selfie to a government ID, and that verification can now be completed swiftly for users. Onfido (now part of Entrust). They tout platform performance, such as onboarding speed and low false acceptance/rejection, in their own press materials, but you should take that as vendor-reported results. Jumio brands itself as an AI-powered identity verification solution that leverages machine learning and biometric technology, providing identity-based AML screening products.
The playbook for implementation here is to build identity as a risk-based flow rather than a one-time gate. You want to cause low friction for low-risk users, but high scrutiny for suspicious ones. That, in turn, demands a risk engine capable of determining when to request a selfie, when to request additional documents, when to verify against third-party sources, and when to send the final round of pre-peddlers for manual review. This also needs extremely careful logging and privacy handling because identity data is sensitive and often regulated.
If you’re outsourcing this build, your due diligence needs to cover two things: first, do they really understand anti-spoofing and liveness issues beyond just “face match”; second, can they create secure pipelines that don’t leak biometrics or ID data out into logs, test environments, or analytics tools?
This is among the most lucrative AI categories in payments, and a lot of founders skip over it because it doesn’t seem as “dramatic and immediate” as fraud. However, a one-percent improvement in authorization rate can have a big impact on your bottom line if you process enough volume.
Authorization uplift is about avoiding false declines and recoverable declines. Banks decline for all sorts of reasons unrelated to fraud: formatting quirks, issuer preferences, network routing, stale credentials, low authentication signals, or temporary risk flags. AI enables learning which modifications to a payment request lead to higher acceptance rates at a specific issuer and in a specific context, in real time.
Stripe promotes Authorization Boost as a package of AI-based acceptance enhancements and says that it raises acceptance rates by 2.2% on average. Stripe’s documentation on improving authorization rates discusses Adaptive Acceptance, which uses machine learning models to dynamically modify elements of the payment request in real time by making small percentage adjustments, while also running experiments comparing these changes across issuers to determine which ones work best.
Network tokenization is a key facilitator here as it decreases the dependence on static card numbers and may enhance both security and acceptance. Visa has now announced issuing its 10 billionth token, and that 29% of transactions processed by Visa use tokens, and with the statements that tokenization can reduce fraud by as much as 60% and increase approval rates globally, again, should be seen as network claim results. Visa’s token management documentation also states that network tokenization provided an average % reduction in their fraud reporting.
Adyen promotes Optimize as machine learning-enabled payment optimisation, with features such as AI-powered Auto Retry and risk insights with issuers to maximise conversion.
The main learning for implementation is that authorisation uplift is not a one-time trick. It is aflow involving request optimization, intelligent retries, credentials updates, tokenization strategy, authentication strategy, and at times, routing decisions. You have to fine-tune it so you don’t create a fraud hole. For instance, too aggressive retries may raise fraud or issuer suspicion. “Maximize acceptance” shouldn’t be the only goal – you need a safe and sustainable acceptance uplift.
If you are going to build this with an Indian team, make them explain how they would conduct controlled experiments that don’t hurt conversion or put you at risk. Teams that have actually done real payments will discuss A/B testing with guardrails, issuer segmentation, retry timing, fraud and dispute impacts monitoring — along with acceptance.
After all, when you start dealing with different countries, payment methods, and types of acquisition, routing is a game-changer. The question is not just “can I process this payment,” but “which route has the best success rate and cost efficiency while maintaining compliance.”
Routing decisions are which acquirer to use, whether or not to use local acquiring, if/when to try alternative means, how to sort payment types at checkout, and how to best retry with the least friction on failure. That is why machine learning can be better than static routing rules, because it´s a dynamic environment. Changes in issuer behavior. Changing network conditions. Changing local method reliability. Changing fraud pressure.
Adyen has touted “Intelligent Payment Routing” and has talked about how local acquiring and machine learning can be used to make payments more cost-effective without impacting authorization rates, making routing a lever for incremental revenue and operational efficiency. The language of Adyen’s Optimize messaging explicitly positions machine learning as a means to optimize transactions, the implication being that conversion rates are higher on payment flows. Stripe’s messaging related to authorization optimization and AI features points to a similar approach of having dynamic components that inform such strategies to optimize acceptances.
The difficult part is that routing is a multi-objective problem of optimization. You care about conversion rate, cost, risk of fraud, risk of disputes, timing of settlements, and user experience. You also live with local regulatory needs such as strong customer authentication flows where applicable, and data residency constraints.
Execution demands truthful measurement. Many teams release “smart routing” without clean attribution, and end up not having any idea if gains were from routing or unrelated seasonal effects. A real routing system needs to be tested with controlled experiments, and it has to have safety rails. If the model is wrong, you need to have a way out that’s known to be safe.
This is also where payments engineering experience matters more than data science. A routing model is useless if your integration can’t run the selected route reliably, or if your reconciliation and settlement tooling can’t deal with the added complexity.
Chargebacks are a secret levy on fintech and payments. They cause direct income disruption, immediate fees, and a significant operational burden. And they create risk — if you set off network monitoring programs, or merchant-level penalties.
Two ways AI helps here. Firstly, by stopping disputes altogether with good fraud decisions and good consumer communications. Secondly, they automate dispute workflows and compile evidence packs that are actually likely to win.
Stripe’s “Smart Disputes” is one example of AI applied to dispute operations. Stripe’s documentation mentions Smart Disputes leveraging an AI rules engine that addresses received disputes, applies relevant evidence to the dispute sourced from Stripe’s internal data and the merchant’s transaction data. Stripe also promotes dispute management by using AI trained on its payment volume to personalize and submit evidence, and it points to case studies such as Vimeo and Squarespace successfully recovering more chargebacks, which would be considered vendor-reported results.
Disputes are also intertwined with the legal and network rules from a governance point of view. Reuters noted a proposed settlement in which Visa and Mastercard would pay a total amount to settle a merchant class action regarding changes to the chargeback rules, a reminder that chargeback regimes are disputed and evolving. You need not commit that case to memory, but you should take that meaning to heart – disputes are part of the commercial and legal landscape of payments, not just an ancillary workflow.
Execution is primarily process engineering. You want clean dispute intake, categorization, evidence retrieval, merchant communication, and deadline monitoring. AI is valuable because it eliminates manual work and raises the win rate without creating false or misleading evidence. If your AI system invents evidence or distorts the transaction context, you can introduce legal risk. So you want rules-based grounding, evidence gets fetched from trusted data sources, and the system logs what it used.
If you are outsourcing dispute automation, make sure to assess the team’s ability to construct strong evidence pipelines and audit trails. A lot of teams can build a UI. Even less build systems that can survive disputes at scale with rigorous deadlines and evidence integrity.
Even if you don’t think of your business as a “bank,” a lot of fintech and payments companies are risk-wise effectively functioning as one. If you’re moving money, onboarding merchants, or facilitating cross-border flows, AML and sanctions requirements come knocking—either because you are subject to the regulations or because your banking partners want to see that you’re serious.
AI assists here by ranking alerts, decreasing false positives, and enhancing entity linking. But AML is also where naïve AI projects collapse because it’s not just about detection. It is the ability to be audited.
FATF has specifically addressed the challenges and opportunities presented by new technologies in the context of AML/CFT, noting that effective utilization of those technologies for AML/CFT purposes is a function of having the right conditions, policies, and practices in place, not merely the tools. That’s a fancy way to say: If your governance and data layer are bad, AI will not magically make it better.
In the fintech space, many identity providers also have AML screening solutions. For example, Jumio offers AML screening and monitoring for sanctions, PEP lists, and adverse media. The technical pattern is similar across vendors: you want reliable data sources, entity resolution, scoring, and case management.
Execution should be based on a clear scope. Are you performing customer screening (KYC), merchant screening (KYB), transaction monitoring (KYT), or some combination of these? Each of them has its own data and its own processes. There’s also a question about what to automate and what to have people look at. It’s common for regulated systems’ final decisions not be fully automated. AI assists in triage and evidence collection.
If you develop AML tooling with an external team, demand an “evidence pack” model. Each alert should have a narrative of why it was triggered, the supporting data, and actions taken on it. Your bank partner, your regulator – they want to see less about your choice of model and more about your ability to show what happened.
Fintech founders usually focus on getting users through the door and don’t realize that the quality of the onboarding process decides the shape of the customer portfolio. The merchant portfolio, in payments, is a risk asset. Bad merchants are the source of fraud, disputes, regulatory problems, and reputation damage.
Historically, merchant underwriting is rule-heavy and manual. AI makes it better by factoring in a number of signals: business registration information, website content patterns, product category risk, anticipated transaction patterns, results of founder ID verification, and earliest transaction behavior. It also enhances monitoring by identifying anomalies indicative of a merchant being compromised or engaging in “bust-out fraud,” a merchant that behaves normally while building trust, only to suddenly process high-risk volume.
This category is difficult to substantiate with public case studies, as numerous providers consider merchant risk to be proprietary. But the direction is still obvious if you look at the way the networks and big players invest in risk and cybersecurity. Decision Intelligence and its larger AI-powered fraud ecosystem that Mastercard publicly talks about now, makes our risk scoring and reason codes a key component of their authorisation level protections. Mastercard also announced tapping into generative AI technology to speed up the detection of card fraud and made performance assertions such as doubling the detection of compromised cards and cutting down on false positives in certain detection scenarios, which once again ought to be viewed as company-reported outcomes.
Merchant risk should also be viewed through a lifecycle lens in implementation. Its undercutting is the beginning. Post-onboarding surveillance is where you stop hemorrhaging, particularly for big losses. You want anomaly detection in merchant behavior, dispute rate monitoring, refund patterns, high-risk BIN usage, geographic mismatches, and traffic source signals. You also need automated “risk actions” with safe defaults — such as escalating verification, lowering limits, holding settlement, or routing to investigation. And these measures must be very carefully managed so they don’t play havoc with legitimate merchants.
If you outsource merchant risk tooling, the primary due diligence question is: Does the team know how to build action systems that can be reversed and audited? Decisions on payment risk may have an immediate impact on the cash flow of a merchant. If your AI mistakenly flags a hold, you can lose quality merchants fast.
Credit is an increasingly integral part of fintech and payments. BNPL, pay-in-4, merchant cash advance, invoice factoring, card-based credit products, and embedded lending are all powered by risk engines.
AI enhances these solutions through real-time underwriting, on-the-fly limits, and customized repayment plans. But credit, unlike fraud, is not. Credit-related decisions stimulate consumer protection aspirations that include, among other things, under many jurisdictions, the right to be provided a specific reason for the denial of credit.
In the US, the CFPB has issued guidance raising concerns that lenders applying AI or other complex models must continue to provide specific and exact reasons for adverse actions, and released a circular stating that creditors may not rely on “generic” checklist reasons under section 1002.48(a)(4) if those reasons do not accurately reflect the actual reasons for the adverse action.
Even if you are a fintech that is like “we are not a bank,” if you do the business of originating credit or partnering with a bank, these expectations may apply. And even beyond the US, expectations of transparency are codified in other legal forms.
Here, implementation means turning this into a controlled decision problem rather than a black box model. It outputs risk estimates. The policy layer adds eligibility, affordability, and product constraints. Reason codes generated by the explanation layer are faithful to the true driving factors. Drift and fairness signals are monitored by the monitoring component. If you are not able to produce credible reasons for something, you are going to have a hard time scaling partnerships with regulated institutions.
Another tough practical reality of implementation is data leakage. Many credit models aren’t inaccurate — they simply were trained on signals that are unstable, or that encode information from the future. If your outsourcing team is not highly specialized in credit ML, demand an evaluation design and backtesting that is more like early retirement than decision-imminent real-time.
Fintech customer support is more than “answer questions.” It’s also a payments operations layer: refunds, disputes, failed payment recovery, chargeback explanations, card replacement flows, suspicious login handling, and troubleshooting payment methods.
This is where generative AI has scored visible victories — but also visible failures, when used indiscriminately. The safe pattern is not “let the model talk.” The safe pattern is an assistant who can pull grounded information from authorized knowledge bases, call tools with tight permissions, and escalate to humans for sensitive cases.
The AI assistant of Klarna is one of the most cited examples. Klarna claimed that two-thirds of its customer service chats during its first month were handled by its AI assistant, with more than 2.3 million conversations, and that it had saved the work equivalent of 700 full-time agents, with more claims about resolution time and repeat enquiries. Reuters further reported that Klarna’s assertions stoked investor fears about artificial intelligence disrupting the call center business, highlighting that this was considered to be operationally significant rather than a novelty.
Risk in execution is hallucination and taking unsafe acts. In fintech assistance, a bad answer could lead to a financial loss. So you need guardrails and security. OWASP’s recommendations for LLM application risks also include sensitive information disclosure and prompt injection—not surprisingly, relevant when your assistant is integrated with money systems and user data.
If you are going to outsource an AI support assistant, your due diligence needs to be on tool permissioning, transcript redaction, and evaluation. A fully realized team will discuss how they test hallucinations, how they prevent prompt injection from causing unauthorized actions, and how that team monitors failure modes in perpetuity. A group that mostly talks about prompts is not ready for fintech-grade risk.
This last segment is under-discussed in marketing, but it is where the largest amount of leakages occur in fintech. Reconciliation errors, ledger misalignments, settlement irregularities, and reporting omissions can become existential as volumes scale. You don’t need a headline-making fraud incident to lose millions. You can bleed it to death through unmatched transactions, fee misunderstanding, and poor exception management.
AI assists with categorizing exceptions, matching records between systems, and identifying anomalies at an early stage. Sometimes you don’t even need heavy ML. You need solid automation combined with smart prioritization.
Stripe’s own products point in this direction. Stripe Sigma is sold as an AI-powered assistant to help users in querying and analyzing Stripe data directly from inside the dashboard, which shows a growing interest in natural-language analytics in payments ops. Stripe also describes automatic reconciliation flows in the context of invoicing, explaining how payments can be auto-reconciled against invoices through the use of virtual bank accounts — that’s less about AI and more about automation, but it’s still the same operational pattern of streamlining & automating away manual matching work.
Consulting firms have also commented that the finance function is no stranger to applying automation to reconciliations and like processes, and recent McKinsey writing does include reconciliations as a finance process ripe for automation.
Execution begins with data discipline here. You need to have consistent identifiers on the payment event, the ledger entry, and in the settlement file. You need to know clearly what “settled,” “captured,” “refunded,” and “disputed” mean. Then AI is added value as an exception handler: which mismatches require immediate attention; which are probable duplicates; which are few alterations; which are timing differences; and which are genuine errors.
If you hire someone to do this build, your main due diligence is whether the team understands double-entry ledger concepts and settlement reality. A lot of software teams seem to think of payments as “API calls.” Payment operations aren’t APIs. It’s a lifecycle with states, reversals, and delayed events. If your team doesn’t know this, your reconciliation/ledger systems will look right but crumble under real-world edge cases.
Now for the really difficult part. Knowing the top ten categories is handy to have, but it’s in the execution stage that most projects stumble. In fintech and payments, the failures are well known: poor data management, poor governance, poor security controls, poor monitoring, and a delivery model that can’t meet reliability.
This playbook is intended to make sure you don’t end up in those pits.
The first step is to begin with a decision and a metric. Fraud risk scoring is a choice. Uplift authorization is a decision. Routing is a choice. Dispute automation is a workflow choice. Each one must translate to a single business metric that everyone agrees to. Losses due to fraud per $1,000 volume. Approval rate by issuer category. Win rate for disputes and time to submit. Cost-per-ticket and duration of resolution. If your team can’t name the metric, they can’t tune the system.
The second step is to get your security and compliance envelope locked down before you start building. Payment systems tend to be closely connected with sensitive data. The purpose of the PCI DSS is to establish a baseline of technical and operational requirements that are designed to protect payment account data, and PCI DSS version 4.0.1 was released as a limited revision to clarify intent. So your architecture has to have segmentation, least privilege, logging, incident response, and secure SDLC — not “we’ll add security later.”
If you are building genAI features, consider them a security surface. OWASP’s LLM guidance covers topics such as prompt injection and sensitive information disclosure risks relevant also in the financial technology space. Your assistant should never have broad tool permissions. It should suggest a course of action. A policy layer must enforce authorizations. The system must perform activities. Log every sensitive event.
Step three is model governance and risk management. You don’t need the banking bureaucracy to be safe, but you do need discipline. NIST’s AI Risk Management Framework is intended to guide organizations in managing AI risks to enhance trustworthiness, and NIST has issued companion guidance pertinent to the risk profiles of generative AI. Take this as a mentality: establish intended use, establish failure modes, establish monitoring, and establish change control.
Step four is monitoring and drift management. Drift is not unknown in payments. It’s guaranteed. Changing fraud patterns. Altering issuer behavior. Changing routing conditions. Not only should your system track model accuracy, but it should track business results. Approval rates, rates of fraud, rates of disputes, load of manual review, and latency. Monitoring needs to be connected to alerting and incident response, because a silent failure can cost you more than a system failure.
The fifth step is the evaluation, which is broadcast live. Many fintech AI failures result from evaluations that leak future data or do not consider delayed results. Chargebacks come later. Disputes come later. Delinquencies come later. If you test it on a random split, you can make yourself think the model is good for production, but it really is not.
The sixth step is integration into a workflow and human-in-the-loop design. In every high-risk area —AML, merchant risk, credit— AI should assist with decisions and the collection of evidence, not decide, unilaterally and without supervision, such an outcome. The optimal pattern is to automate the boring parts and have humans handle the ambiguous, high-impact cases.
And finally, the delivery discipline. When you build with an Indian agency, the project can bloom into a fantastic success or slowly fade out based on the operating model. The fundamental questions aren’t about coding ability. They’re about maturity. Are they capable of building low-latency services? Can they architect secure processing of data? Can they generate audit logs? Can they produce documentation that a regulated partner will accept? Can they operate continuous monitoring (and on-call)? Can they describe how they meet PCI-related requirements and keep from leaking sensitive data in logs? If the answer is fuzzy, you’re at high risk.
An auspicious external sign is that regulators are themselves increasingly providing safe spaces for experimentation with AI in financial services. The UK FCA unveiled a “Supercharged Sandbox” with NVIDIA to enable firms to safely experiment with AI utilizing advanced computing and regulatory support, signposting the direction of travel: innovation is welcomed, but it is to be harnessed.
