KYC for Microfinance: Biometric eKYC in Low-Connectivity, Field-Led Onboarding

<img src="https://m2p-website-file.s3.ap-south-1.amazonaws.com/marketing-cms/m2p-blog/2026/10/baas-cover-fraud-loss-ownership_2x1.webp" alt="In a BaaS Stack, Whose Fraud Loss Is It, Really?" title="" id="x6grswgvy" width="100%" height="60">A customer gets defrauded on a neobank app. The app was built by a fintech. The fintech doesn't hold a banking license — it operates under a sponsor bank. The sponsor bank doesn't run the technology stack — that's handled by a Banking-as-a-Service (BaaS) platform sitting in between. The card that was compromised was issued through a program manager. The transaction that moved the money ran over a card network and a processor neither the customer nor, often, the fintech's own support team has direct visibility into.Now ask the obvious question: whose fraud loss is this?In a traditional bank, the answer is uncomfortable but at least clear — it's the bank's, full stop, because the bank is the only regulated entity in the relationship and the customer's contract is with no one else. In a BaaS stack, the honest answer is: it depends on the contract, it depends on which party had operational control at the moment of failure, and in a meaningful share of cases, it depends on who blinks first in the dispute. That's not a regulatory framework. That's a negotiation.This is one of the more uncomfortable open questions in modern financial infrastructure, and it's worth examining directly, because every month that it stays unanswered, more transaction volume flows through stacks where the question hasn't been settled.<h2 class="heading-node" id="7lr4v0tra">The Stack: More Parties Than the Customer Ever Sees</h2>A typical BaaS arrangement for a consumer neobank or embedded finance product involves, at minimum:<ul class="list-node" id="qyifg25jq"><li>The sponsor bank — the regulated, licensed entity whose charter underpins the whole arrangement, legally the entity "providing" the financial service</li><li>The BaaS/infrastructure platform — the technology layer that provides APIs, core banking rails, card issuing processing, ledgering, and often the actual fraud and risk tooling</li><li>The program manager or fintech — the brand the customer actually sees and trusts, who designed the product experience and owns the customer relationship</li><li>The card network — Visa, Mastercard, RuPay, or equivalent, which has its own rulebook on chargeback liability</li><li>The processor — handling authorization and settlement, sometimes a separate entity from the BaaS platform</li><li>Downstream service providers — KYC/identity verification vendors, fraud scoring vendors, dispute management vendors — each potentially a different company again</li></ul>A customer onboarding into this product has a relationship with the fintech brand. Legally, their account is with the sponsor bank. Operationally, their transaction data, fraud scoring, and dispute workflows run through the BaaS platform. None of these three — brand, license holder, and infrastructure operator — necessarily has full visibility into what the other two are doing at the moment a fraud event occurs.This isn't a hypothetical structure drawn up for the sake of argument. It's the default architecture of embedded finance today, in India under the NBFC-fintech co-lending and PPI issuance models, in the US under the sponsor-bank model that's powered most neobanks and embedded finance launches of the last decade, and increasingly across Southeast Asia, the GCC, and Africa as digital-first banking licenses proliferate. The question of who owns fraud risk isn't a corner case. It's the central unresolved question of the entire BaaS category.<h2 class="heading-node" id="jr3nukzcb">Why "The Contract Decides" Isn't a Satisfying Answer</h2>The reflexive answer from most legal and commercial teams is that the contracts between these parties allocate liability, and in a narrow sense, that's true — most BaaS and program agreements do contain indemnification clauses, loss-sharing formulas, and service-level commitments that assign financial responsibility for different fraud scenarios.But this answer breaks down in practice for three reasons.First, contracts are written before the fraud happens, and fraud typologies evolve faster than contract renegotiation cycles. A loss-sharing clause drafted around card-not-present fraud risk in 2022 may say nothing coherent about a 2026 scam where the customer is socially engineered into authorizing their own transaction, moved through a mule account the sponsor bank's own compliance team should arguably have flagged, using a device fingerprint the BaaS platform's fraud engine scored as "unusual" but didn't block. Which clause governs that? Often, none cleanly does — and the parties end up negotiating the specific incident rather than applying a pre-agreed formula.Second, contractual liability and operational control frequently don't line up. The sponsor bank may be contractually on the hook for a category of loss, but the fraud model that failed to catch it was built, tuned, and operated entirely by the BaaS platform, with the bank having limited visibility into its thresholds or decisioning logic. Conversely, the fintech brand — the party the customer trusts and who designed the user experience that may have made the scam more or less likely to succeed — often carries little to no direct financial liability at all, because they're not a regulated entity and their contract with the sponsor bank was deliberately structured to limit their exposure. The party with the most contractual liability is not always the party with the most operational ability to have prevented the loss, and that mismatch is exactly why these disputes drag on.Third, regulators have not caught up to the structure. Most financial regulation — in the US, India, the EU, and most of APAC and MEA — was written with a bilateral mental model: one regulated institution, one customer, one relationship. Outsourcing and third-party risk guidelines (RBI's guidelines on outsourcing of financial services, the EBA's guidelines on outsourcing arrangements, the US OCC and FDIC's third-party risk management guidance) all gesture at the idea that the regulated entity remains accountable for outsourced functions — but "remains accountable" is a supervisory statement about who the regulator will come after, not an operational answer to who absorbs the loss, who built the control that failed, or how four or five companies should coordinate incident response in real time during an active fraud event. The regulatory answer and the commercial answer are different documents, written by different people, for different purposes, and they often don't reconcile cleanly.<h2 class="heading-node" id="xpjxeb4hj">A Concrete Scenario Worth Walking Through</h2>Consider a realistic case: a customer of a BaaS-powered neobank falls victim to a phishing scam and is tricked into authorizing a push payment to a mule account. The transaction passes SCA-equivalent authentication because the customer, under the scammer's instruction, approves it themselves. It passes the fraud model's real-time scoring because the device, location, and amount are all consistent with the customer's normal behavior — the only anomaly is the payee, which was flagged as "new" but not blocked, per the risk threshold the BaaS platform configured by default.Now trace the aftermath:<ul class="list-node" id="y0z893cgh"><li>The fintech brand fields the customer's complaint first, because that's the app the customer opened. It has no direct mechanism to reverse the transaction and limited visibility into the fraud model's decisioning.</li><li>The sponsor bank is the regulated entity responsible for the account and, in most jurisdictions, is the party a regulator or ombudsman will ultimately hold accountable for customer treatment — but it likely didn't configure the specific payee-risk threshold that let the transaction through.</li><li>The BaaS platform owns and operates the fraud model, and may have offered a stricter threshold as a configurable option — one the program never enabled, whether due to cost, conversion-rate concerns, or simply not being flagged as a priority during onboarding.</li><li>The card network or payment rail, if involved, has its own dispute rules, which may or may not cover authorized-but-coerced transactions at all — most do not, by design, since the customer genuinely authorized the payment.</li></ul>Every party in this chain has a partially valid claim that the loss isn't "theirs" alone, and every party also had some sliver of ability to have prevented it. This is precisely why these disputes are frequently resolved through negotiation, internal write-offs, or — worst of all — simply left unresolved, with the customer absorbing the loss because no single party accepts full ownership quickly enough to make them whole.<h2 class="heading-node" id="4cb6t7ab8">Three Structural Reasons This Keeps Happening</h2>1. BaaS is multi-party by design, and no single party owns the full transaction lifecycle. This isn't a bug to be patched — it's the entire value proposition of the model. A bank gets distribution without building consumer product. A fintech gets a banking license without becoming a bank. A BaaS platform monetizes infrastructure instead of owning customer relationships. That division of labor is exactly what makes embedded finance commercially viable — but it also means the full transaction lifecycle, end to end, isn't visible to or controlled by any single entity, which is precisely the condition that makes fraud liability so hard to assign cleanly.2. Regulatory frameworks still presume a bilateral relationship. Virtually every consumer protection and fraud liability regulation on the books — whether it's Regulation E in the US, RBI's customer liability circulars in India, or PSD2/PSD3's liability provisions in the EU — was drafted assuming the customer's financial institution is a single, identifiable, directly accountable entity. These frameworks are only slowly being updated to contemplate multi-party technology stacks, and in the meantime, the gap gets filled by private contracts that were never designed to do the job regulation is supposed to do.3. Commercial incentives push liability downstream rather than toward the party best positioned to prevent the loss. In commercial negotiations between banks, BaaS platforms, and fintechs, liability allocation is a point of leverage, not a principled design exercise. The party with the most negotiating power — frequently the sponsor bank, given its licensing position — tends to push liability toward the parties with less leverage, regardless of which party actually controls the systems that could have prevented the fraud. This produces contracts that are internally consistent and legally enforceable, but structurally disconnected from where operational risk actually sits.<h2 class="heading-node" id="v4aptyex9">What a More Defensible Model Looks Like</h2>None of this means the problem is unsolvable — it means it hasn't been treated as a design question with the seriousness it deserves. A few principles point toward a more defensible structure:Liability should be mapped to operational control, stage by stage. Rather than a single blanket liability clause, BaaS agreements need granular control-mapping: who configures authentication thresholds, who owns fraud model tuning, who monitors real-time alerts, who has authority to freeze a suspicious transaction before settlement. Liability for a given failure mode should sit primarily with whoever had the operational ability to prevent it — not whoever has the weakest negotiating position.Shared, real-time fraud visibility has to replace siloed dashboards. A meaningful share of these disputes exist because the bank, the fintech, and the BaaS platform are each looking at partial data after the fact. A fraud and risk management layer built for multi-party visibility — where the sponsor bank, the program manager, and the infrastructure platform see the same transaction-level risk signals in real time, not reconstructed after a complaint — removes much of the ambiguity about who knew what, when.Incident response needs a predefined, cross-party playbook, not an improvised negotiation. When a scam or account takeover is detected, the question of who freezes funds, who notifies the customer, and who initiates recovery shouldn't be settled contract-clause-by-contract-clause during the incident itself. The institutions handling this well have pre-agreed, tested incident response protocols that span all parties in the stack, triggered automatically by defined fraud signals.Regulators and industry bodies need to keep building multi-party liability guidance, not just bilateral frameworks. Some progress is visible — the UK's Confirmation of Payee and APP fraud reimbursement rules under the PSR, and evolving guidance from US regulators following several high-profile bank-fintech partnership failures, both represent early attempts to address multi-party accountability directly. APAC and MEA regulators drafting open banking and BaaS-specific frameworks have a real opportunity to build this in from the start rather than retrofitting it later.<h2 class="heading-node" id="3weu95n7b">The Bottom Line</h2>The honest answer to "who owns fraud risk in a shared BaaS stack" today is: it's split across several parties, unevenly, inconsistently, and in ways that frequently don't match who actually had the ability to prevent the loss. That's not a satisfying answer, but it's the accurate one, and pretending otherwise — treating a signed indemnification clause as if it resolves the operational question — is exactly how these disputes end up taking months to settle while customers wait for resolution.Getting this right isn't primarily a legal exercise. It's an infrastructure exercise. The institutions that will handle this well are the ones that build — or partner for — fraud and risk systems designed from the outset for multi-party visibility and clear, stage-by-stage control mapping, rather than treating shared liability as something to be resolved after the fact through negotiation.If you're a bank, fintech, or program manager operating in a BaaS stack and this question has come up in a real dispute rather than a hypothetical one, it's worth examining whether your fraud and risk infrastructure actually supports shared visibility and clear control attribution — or whether it's quietly assuming the bilateral model that regulation was built around. <a class="link" href="https://m2pfintech.com/frm-aml/?utm_source=blg&amp;utm_medium=frm&amp;utm_campaign=oct" target="_blank">M2P's FRM</a> system is built for exactly this multi-party reality, giving every party in a BaaS stack visibility into the same transaction-level risk signals rather than reconstructing them after a dispute. If this is a live question for your organization, <a class="link" href="https://m2pfintech.com/contact-us/?utm_source=blg&amp;utm_medium=frm&amp;utm_campaign=oct" target="_blank">reach out to M2P</a> to talk through what a clearer ownership model could look like for your stack.

Who Owns Fraud Risk in a Shared Banking-as-a-Service Stack — and Why Nobody's Really Answered This Yet

<img src="https://m2p-website-file.s3.ap-south-1.amazonaws.com/marketing-cms/m2p-blog/2026/09/acs_readiness_mena_blog_v4_2000x1000.webp" alt="ACS Readiness: The Step MENA Banks Can't Skip" title="" id="bmihr66ay" width="100%" height="362">Digital card issuance across the Middle East and North Africa is accelerating, driven by a mix of national digital economy strategies, growing e-commerce volumes, and a new generation of digital-first banks and fintechs entering the market. As card portfolios scale, so does exposure to <a class="link" href="https://m2pfintech.com/blog/intelligent-access-control-securing-digital-payments/?utm_source=blg&amp;utm_medium=acs&amp;utm_campaign=4" target="_blank">card-not-present (CNP) fraud</a> — and with it, the importance of getting Access Control Server (ACS) infrastructure right from the outset.Unlike markets where <a class="link" href="https://m2pfintech.com/blog/3d-secure-your-barrier-against-cyber-attacks/?utm_source=blg&amp;utm_medium=acs&amp;utm_campaign=4" target="_blank">3-D Secure (3DS)</a> was added onto legacy systems over time, many MENA issuers have the opportunity to build authentication infrastructure correctly the first time. Doing that well requires a clear-eyed view of what "ACS readiness" actually means before scaling issuance volumes.<h2 class="heading-node" id="c9p10zbvy">Why Readiness Matters Before, Not After, Scale</h2>It's tempting to treat ACS as something to configure once volumes justify it. In practice, authentication infrastructure is much harder — and more expensive — to retrofit once a card program is live and processing meaningful transaction volumes. Issues with challenge flow design, scheme compliance, or risk-engine calibration surface as customer complaints, failed transactions, or chargeback disputes, not as clean engineering tickets.For MENA banks planning to scale digital card issuance, ACS readiness is best assessed across five areas.<h2 class="heading-node" id="a5wx6nrgp">1. Regulatory and Scheme Compliance Alignment</h2>MENA <a class="link" href="https://m2pfintech.com/blog/psd2psd3-and-dora-learnings-for-apac-mea-markets-from-european-regulations/?utm_source=blg&amp;utm_medium=acs&amp;utm_campaign=4" target="_blank">regulators</a> and central banks are increasingly issuing guidance on strong customer authentication, often informed by frameworks like PSD2's SCA requirements in Europe, while also reflecting local market context. Issuers need an ACS platform that:<ul class="list-node" id="gf11gobg0"><li>Supports EMV 3DS 2.x specifications across all schemes the bank issues on (Visa, Mastercard, and regional/domestic schemes where applicable)</li><li>Can be configured to align with jurisdiction-specific regulatory requirements as they evolve</li><li>Provides audit trails and reporting sufficient to demonstrate compliance to regulators and card networks</li></ul><h2 class="heading-node" id="irjrarb14">2. Risk-Based Authentication Calibration</h2>A core value proposition of 3DS 2.x is the ability to authenticate low-risk transactions frictionlessly while reserving step-up challenges for higher-risk ones. This depends on a risk engine that can:<ul class="list-node" id="iryqsxowr"><li>Ingest transaction, device, and behavioral data to make real-time risk decisions</li><li>Perform reasonably well even with limited historical data during early scale-up, and improve as data volume grows</li><li>Be tuned by the issuer's risk team rather than operating as a fixed black box</li></ul>Banks scaling issuance quickly should ask providers directly how the risk engine behaves in the first months of a new card program, when transaction history is thin.<h2 class="heading-node" id="ldgpgozbx">3. Multi-Channel and Multi-Device Experience</h2>MENA consumers transact across a wide range of devices and channels — mobile apps, mobile web, and desktop browsers — often with varying network conditions. ACS readiness means the challenge experience:<ul class="list-node" id="et951us0a"><li>Renders correctly and loads quickly across device types and connection speeds</li><li>Supports authentication methods relevant to the market (OTP, biometric, app-based approval) without adding unnecessary friction</li><li>Is tested against real regional usage patterns, not just reference implementations</li></ul><h2 class="heading-node" id="r8k2q0e4a">4. Multi-Network and Multi-Market Scalability</h2>Many MENA issuers operate — or plan to operate — across multiple countries and multiple card schemes simultaneously. An ACS platform that requires separate integration work per scheme or per market becomes a scaling bottleneck. Readiness here means confirming the platform can:<ul class="list-node" id="g5firhzig"><li>Handle multiple schemes through a consistent architecture</li><li>Extend to new markets without a ground-up re-implementation</li><li>Maintain consistent uptime and performance as issuance volume grows across regions</li></ul><h2 class="heading-node" id="tvpkwx2go">5. Integration with the Broader Fraud and Risk Stack</h2>ACS doesn't operate in isolation. It works alongside <a class="link" href="https://m2pfintech.com/frm-aml/?utm_source=blg&amp;utm_medium=acs&amp;utm_campaign=4" target="_blank">fraud and risk management (FRM) systems</a>, issuer processing platforms, and dispute management workflows. Before scaling, banks should confirm:<ul class="list-node" id="0rm6vx09n"><li>How ACS decisions feed into (and are informed by) the bank's broader FRM capability</li><li>How chargeback and liability-shift data flows back into dispute management processes</li><li>Whether the ACS provider offers this as a connected capability or requires the bank to stitch systems together independently</li></ul><h2 class="heading-node" id="8t3w6uw0j">The Cost of Getting This Wrong</h2>Issuers who scale digital card issuance without a <a class="link" href="https://m2pfintech.com/blog/build-vs-buy-the-real-cost-of-running-an-in-house-acs/" target="_blank">properly readied ACS</a> tend to encounter the same set of problems: elevated false decline rates that push customers away, inconsistent challenge experiences that frustrate mobile users, weaker footing in chargeback disputes, and compliance gaps that surface only during a regulatory review or scheme audit. Each of these is more costly to fix retroactively than to design correctly upfront.<h2 class="heading-node" id="9b1g6wgko">Building Readiness as a Deliberate Step</h2>For MENA banks planning to scale digital card issuance, ACS readiness shouldn't be an afterthought bundled into a broader card processing decision. It deserves its own evaluation — one that tests regulatory alignment, risk-engine performance, device experience, and scalability against the bank's specific growth plans, not just generic vendor claims.Banks that treat this as a deliberate, upfront step are the ones that scale issuance smoothly, with fewer surprises as volumes grow.<hr>M2P Fintech provides <a class="link" href="https://m2pfintech.com/acs/?utm_source=blg&amp;utm_medium=acs-mena&amp;utm_campaign=4" target="_blank">Access Control Server (ACS)</a> infrastructure built for EMV 3DS 2.x, designed to support issuers scaling digital card programs across the Middle East and North Africa. To assess ACS readiness for your card program, <a class="link" href="https://m2pfintech.com/contact-us/?utm_source=blg&amp;utm_medium=acs-mena&amp;utm_campaign=4" target="_blank">get in touch</a> with our team.

ACS Readiness: What MENA Banks Need Before Scaling Digital Card Issuance

<img src="https://m2p-website-file.s3.ap-south-1.amazonaws.com/marketing-cms/m2p-blog/2026/09/3ds_africa_blog_v3_2000x1000-1.webp" alt="3DS in Africa: Building Authentication Right, From the Start" title="" id="na911gquf" width="100%" height="362">Africa's digital payments landscape is expanding faster than almost anywhere else in the world. Mobile money, card issuance, and e-commerce are all growing in tandem, and with that growth comes a parallel rise in card-not-present (CNP) fraud exposure. For banks and fintechs issuing cards across the continent, 3-D Secure (3DS) authentication has moved from a "nice to have" to a foundational requirement — one that directly affects approval rates, chargeback liability, and customer trust.This piece looks at where 3DS adoption in Africa currently stands, the specific challenges issuers face in rolling it out, and where the near-term opportunities lie.<h2 class="heading-node" id="7rj18wg1d">Why 3DS Matters More in Africa Than in Mature Markets</h2>In markets with long-established card infrastructure, 3DS is largely a compliance checkbox layered onto systems that have been in place for years. Africa's issuers are in a different position: many are scaling card programs and digital acquiring in parallel, often leapfrogging legacy rails altogether. This means 3DS isn't being retrofitted — it's being built in as part of a first-generation digital payments stack.That timing is an advantage. Issuers rolling out card programs today can design authentication flows around EMV 3DS 2.x from day one, rather than carrying forward older, less secure implementations. But it also means the ecosystem — scheme rules, regulatory guidance, merchant readiness, and consumer familiarity with step-up authentication — is still maturing at different speeds across markets.<h2 class="heading-node" id="mllq4oqor">Where Adoption Stands Today</h2>3DS adoption across Africa is uneven, shaped by a few consistent factors:<ul class="list-node" id="8cvo97mh7"><li>Regulatory pace varies by country. Some central banks have issued clear guidance on authentication and fraud liability; others are still developing frameworks, leaving issuers to align voluntarily with scheme mandates from Visa and Mastercard.</li><li>Mobile-first usage patterns. A large share of card transactions in Africa originate from mobile browsers or in-app checkouts, which places a premium on ACS platforms that render authentication challenges cleanly across low-bandwidth and lower-spec devices.</li><li>Merchant-side readiness lags issuer-side readiness. Even where issuers have deployed capable ACS platforms, full frictionless-rate benefits depend on merchants and payment gateways correctly implementing 3DS Server and Directory Server integrations on their end.</li><li>Fraud patterns are still being learned. Risk-based authentication engines depend on transaction data to fine-tune frictionless approval rates. Newer card programs have less historical data to draw on, which can mean more conservative (and more challenge-heavy) authentication in the early stages.</li></ul>M2P has worked directly with issuers navigating this environment — including supporting a leading Bank in Nigeria on <a class="link" href="https://m2pfintech.com/blog/the-ultimate-guide-to-access-control-servers-securing-the-future-of-digital-payments/?utm_source=blg&amp;utm_medium=acs&amp;utm_campaign=4" target="_blank">ACS deployment</a> — and the pattern that emerges is consistent: issuers that treat 3DS as core infrastructure, not a bolt-on, see it pay off in both fraud reduction and customer experience.<h2 class="heading-node" id="2mbdoubrs">The Core Opportunities for Issuers</h2>1. Reducing false declines while managing genuine fraud risk A well-tuned ACS, paired with a fraud and risk management (FRM) layer, allows issuers to move more transactions through frictionless flows without opening themselves up to unnecessary fraud exposure. This is especially valuable in markets where declined-but-legitimate transactions can push customers toward alternative payment methods or competitor banks.2. Strengthening dispute and chargeback positioning Liability shift rules under 3DS give issuers a stronger position in chargeback disputes when authentication has been correctly performed. For issuers scaling card portfolios quickly, this reduces one of the more unpredictable cost centers in card operations.3. Building for multi-network, multi-market growth Many African issuers operate across multiple card schemes and, increasingly, multiple countries. An ACS built to handle Visa, Mastercard, and other network specifications consistently — rather than requiring separate integrations per scheme — simplifies scaling into new markets and new product lines.4. Meeting regulatory expectations proactively Even where local mandates are still developing, issuers that implement robust authentication now are better positioned for future regulatory tightening, and can point to strong authentication practices as a trust signal with both regulators and merchant partners.5. Improving the mobile checkout experience Given how much <a class="link" href="https://m2pfintech.com/blog/intelligent-access-control-securing-digital-payments/?utm_source=blg&amp;utm_medium=acs&amp;utm_campaign=4" target="_blank">CNP</a> volume in Africa originates on mobile, an ACS designed with mobile-optimized challenge screens directly affects conversion. This is less about compliance and more about commercial outcomes — every unnecessary drop-off at the authentication stage is a transaction lost.<h2 class="heading-node" id="q5o7jkdg7">What This Means for Issuers Evaluating ACS Providers</h2>For banks and fintechs building or upgrading their authentication stack in Africa, the practical questions worth asking an <a class="link" href="https://m2pfintech.com/blog/modern-accces-control-server-acs-architecture-guide/" target="_blank">ACS provider</a> include:<ul class="list-node" id="w4ehs4ea9"><li>Does the platform support EMV 3DS 2.x with both frictionless and challenge flows out of the box?</li><li>How does the risk engine perform with limited historical transaction data, and how quickly does it improve as data accumulates?</li><li>Is the challenge experience genuinely optimized for mobile and lower-bandwidth conditions?</li><li>Can the platform scale across multiple card schemes and multiple countries without re-architecture?</li><li>What support exists for local regulatory nuances as frameworks evolve?</li></ul>3DS adoption in Africa is still taking shape, which means the issuers building strong authentication foundations today are the ones best positioned to scale confidently as digital card issuance accelerates across the continent.<hr>M2P Fintech supports issuers across Africa with <a class="link" href="https://m2pfintech.com/acs/?utm_source=blg&amp;utm_medium=acs-africa&amp;utm_campaign=4" target="_blank">Access Control Server (ACS)</a> infrastructure built for EMV 3DS 2.x, including deployments in a leading Bank in Nigeria. To discuss ACS readiness for your card program, <a class="link" href="https://m2pfintech.com/contact-us/?utm_source=blg&amp;utm_medium=acs-africa&amp;utm_campaign=4" target="">get in touch</a> with our team.

The State of 3-D Secure Adoption in Africa: Opportunities for Issuers

<img src="https://m2p-website-file.s3.ap-south-1.amazonaws.com/marketing-cms/m2p-blog/2026/09/m2p_kyc_suite_creative_2000x1000.webp" alt="CKYC, eKYC, Video KYC — Explained. Consolidated. Compliant." title="" id="1g0aimrri" width="100%" height="362">Every bank, NBFC, fintech, insurer, and increasingly every government service desk in India runs on the same underlying question: is this person who they say they are? The answer used to take days and a stack of photocopies. Today, it's expected to take seconds — without compromising on compliance, fraud prevention, or customer experience.That shift is why "KYC software" has become one of the most searched terms in Indian BFSI technology procurement. But the term hides a lot of complexity. Search for it and you'll find a dozen vendors, a dozen acronyms — CKYC, eKYC, VKYC, AUA, KUA — and very little that actually explains how these pieces fit together, what to look for before buying, or what problems still don't have great answers.This guide is meant to be the reference point. It covers what KYC software actually is, the components that make up a complete stack, the evaluation questions most buyers don't think to ask, the compliance landscape it has to operate inside, and where the category is headed next.<h2 class="heading-node" id="9wni4ji28">What Is KYC Software, Exactly?</h2>KYC(Know Your Customer) is a regulatory requirement, not a product. KYC software is the technology layer that lets a regulated institution meet that requirement digitally: capturing a customer's identity, verifying it against authoritative sources, checking it for fraud or tampering, and maintaining an auditable record, all without a human being physically checking a photocopy of a PAN card.In India, this has a specific technical shape because of two connected but distinct government identity infrastructures:<ul class="list-node" id="mi30ir1k1"><li>CKYC (Central KYC Registry) — a central repository (maintained by CERSAI) that stores KYC records once and lets any regulated entity retrieve them instantly, rather than re-collecting the same documents every time a customer opens a new account elsewhere.</li><li>Aadhaar-based eKYC — verification against UIDAI's Aadhaar database, accessed through licensed AUA/KUA (Authentication User Agency / KYC User Agency) integrations.</li></ul>Everything else in a modern KYC software stack — video verification, document OCR, biometric matching, fraud scoring, masking — exists to make those two verification paths faster, more secure, and usable across more channels (web, app, branch, call centre, kiosk).<h1 class="heading-node" id="1klysmv0w">The Components of a Complete KYC Software Stack</h1>If you're evaluating KYC software for the first time, the acronyms are the first wall you hit. Here's what each piece actually does and when you need it — because in practice, most institutions need several of these working together, not just one.<h2 class="heading-node" id="bvuq852ab">CKYC Retrieval</h2>Pulls a customer's existing KYC record using their OVD (officially valid document) number, date of birth, and mobile number. If the customer has ever completed KYC anywhere in the regulated financial system, this can complete onboarding almost instantly — no fresh document upload needed. This is usually the first check a well-designed KYC software flow should attempt, since it's the fastest and cheapest path when it hits.<h2 class="heading-node" id="hphmnisyx">Aadhaar eKYC (AUA/KUA)</h2>When there's no CKYC record to retrieve, Aadhaar-based authentication is the next layer — verifying identity via OTP, fingerprint, face, or iris, and fetching demographic data (name, DOB, address, photograph) directly from UIDAI. This requires the vendor to hold live AUA/KUA licensing, not just claim "Aadhaar integration" — a distinction worth confirming during vendor evaluation, since sub-licensing arrangements vary in reliability and audit-readiness.<h2 class="heading-node" id="rvlxo8k65">Video KYC (VKYC)</h2>RBI's video-based customer identification process (V-CIP) allows fully remote, branch-free onboarding — a human or AI-assisted agent verifies the customer's live video, checks document authenticity, and confirms liveness, all in one session. This matters enormously for lenders and neobanks trying to acquire customers in geographies where opening a physical branch isn't viable, and for institutions that need to serve customers without reliable Aadhaar OTP access (NRIs, elderly customers, low-connectivity regions).<h2 class="heading-node" id="wz3dugzb1">Min-KYC</h2>A lesser-known but increasingly important option: a CKYC-driven onboarding path that doesn't require Aadhaar verification at all. This exists for use cases where full KYC can be completed later (e.g., certain prepaid instruments, small-value accounts) and matters for products that need to go to market fast, in geographies or customer segments where Aadhaar-linked onboarding adds friction or isn't the ideal first step. It's rarely discussed in KYC software marketing, but it's often the fastest, cheapest onboarding path available for eligible use cases.<h2 class="heading-node" id="mwda25dsb">Instant Background Verification</h2>Beyond identity, many onboarding flows also need to check employment history, court records, and address — particularly for lending, hiring, and high-value account opening. Paperless, consent-driven background checks reduce this from a multi-day manual process to something that runs inline with digital onboarding.<h2 class="heading-node" id="45un4891s">Aadhaar Masking</h2>A narrower but non-negotiable requirement under UIDAI rules: any entity storing or displaying Aadhaar numbers must mask the first eight digits. Good KYC software handles this automatically and extends it to custom document formats — not just standard Aadhaar cards — since institutions often need to mask Aadhaar numbers embedded in other scanned documents too.<h2 class="heading-node" id="bld62nlcj">Identity Microservices</h2>For engineering teams building bespoke onboarding flows rather than adopting an off-the-shelf journey, granular APIs matter more than a packaged UI: liveness detection, OCR extraction, face match, GST/KYB (Know Your Business) checks, crime record checks, and AML screening as individually callable services. This is the layer that lets a product team compose their own flow instead of being boxed into a vendor's default journey.<h1 class="heading-node" id="evsu6w33e">What to Actually Evaluate Before Buying KYC Software</h1>Most KYC software comparisons stop at "does it do Aadhaar eKYC and Video KYC." That's table stakes. The questions that actually differentiate vendors — and that most vendor pages, including product pages across the industry, don't answer clearly — are these:1. Fraud detection accuracy, not just fraud detection claims. Almost every vendor claims "AI-powered fraud detection." Few publish what that means in practice: false-positive rates, spoofed-image detection accuracy, deepfake resilience for video KYC, and how forensic checks perform against real attack patterns (photo-of-photo, screen replay, synthetic identity documents). When evaluating a KYC software vendor, ask for these numbers directly, or for reference customers who can speak to fraud outcomes post-implementation — not just onboarding-speed outcomes.2. On-premise vs. cloud availability. Cloud-only KYC software is the default assumption in most vendor pitches, but it's a dealbreaker for public sector institutions, PSU banks, and any entity with data-residency or air-gapped infrastructure mandates. A vendor that genuinely supports both cloud and on-premise deployment — not just "can be discussed" — is solving a real, underserved requirement.3. Total cost of ownership, beyond the per-transaction rate. Per-verification pricing is easy to compare; what's harder to see upfront is the cost of integration effort, the cost of maintaining compliance as regulations change, and the operational cost of handling verification failures and manual review queues. A lower headline rate with a higher failure/fallback rate can cost more in practice. Ask vendors for real fallback-rate and manual-review-rate data, not just headline accuracy claims.4. Coverage beyond Aadhaar, if you operate outside India or serve NRI/foreign-national customers. Aadhaar-centric KYC software is built for the Indian resident use case. If your institution serves NRIs, foreign nationals, or operates in other markets, ask explicitly how the platform handles non-Aadhaar identity documents (passports, foreign national IDs) and whether that capability is native or requires a separate integration. This is a genuine gap across most Indian KYC software vendor pages, ours included as of today — and one worth pressing on directly in RFPs, since the answer varies a lot between vendors who've actually built for it and vendors who haven't.5. Compliance depth: RBI Master Direction, UIDAI audit checklist, and DPDP Act. KYC software doesn't operate in a compliance vacuum. It needs to satisfy RBI's KYC Master Direction (record-keeping, risk categorization, periodic updation), UIDAI's audit checklist for any Aadhaar-linked processing, and increasingly, the Digital Personal Data Protection (DPDP) Act's consent and data-minimisation requirements. Ask any vendor how their audit trail, consent logging, and data retention policies map to all three — not just RBI.6. Low-code/no-code configurability vs. rigid pre-built flows. Institutions with in-house product teams often want to configure onboarding journeys — reordering steps, adding conditional logic, swapping verification methods by customer segment — without a fresh engineering sprint every time. Whether a KYC software platform offers genuine no-code workflow building, or just customization via vendor professional services, materially changes how fast you can iterate post-launch.<h1 class="heading-node" id="mkkhxutgo">Why Institutions Are Consolidating Toward a Single KYC Software Suite</h1>A pattern worth naming explicitly: most institutions didn't set out to buy "a KYC suite." They bought Aadhaar eKYC from one vendor, added Video KYC from another when RBI's V-CIP norms landed, bolted on a background-verification tool for lending use cases, and built Aadhaar masking in-house or through a fourth integration.The result, a few years in, is a fragmented identity stack: multiple vendor contracts, multiple compliance audits, multiple points of integration failure, and no single, coherent view of a customer's verification history across products.Consolidating onto a single KYC software platform — one that covers CKYC, Aadhaar eKYC, Video KYC, Min-KYC, masking, background verification, and identity microservices under one contract, one dashboard, and one audit trail — isn't just a cost play. It removes the integration overhead of maintaining multiple vendor relationships, gives compliance teams one place to pull audit records from, and lets product teams reuse the same identity infrastructure across new products (lending, cards, neobanking, merchant onboarding) instead of re-procuring for each one.<h1 class="heading-node" id="in1nf1hdi">Where KYC Software Is Headed</h1>A few shifts worth watching over the next few years:<ul class="list-node" id="odv04qucp"><li>Agentic AI in verification workflows. Beyond rules-based fraud flags, AI models that can reason across a full onboarding session — cross-referencing document data, liveness signals, and behavioural patterns — are starting to move from research to production in identity verification.</li><li>Deeper video KYC automation. Fully AI-assisted VKYC sessions (reducing dependency on live human agents for routine cases, while retaining human review for edge cases) are becoming a genuine efficiency lever for high-volume onboarding.</li><li>Convergence with AML and fraud monitoring. KYC software and fraud/AML systems have historically been separate purchases; the operational logic for combining them — shared identity signals, shared risk scoring — is increasingly hard to ignore.</li><li>Government and public-sector adoption. Digital identity verification is expanding well beyond BFSI into direct citizen service delivery, welfare disbursement, and public-sector onboarding — a segment most KYC software vendors aren't built to serve at that scale or compliance bar.</li></ul><h1 class="heading-node" id="7im0ydgyj">Where M2P's KYC Suite Fits</h1>M2P's <a class="link" href="https://m2pfintech.com/kyc-suite/?utm_source=blg&amp;utm_medium=kyc&amp;utm_campaign=suite" target="_blank">KYC Suite</a> is built around the consolidation logic described above — CKYC retrieval, Aadhaar-based AUA/KUA authentication, Video KYC (available on both cloud and on-premise), Min-KYC, Aadhaar masking, instant background verification, and identity microservices, all accessible through a single low-code/no-code platform with one audit trail.It's currently used by 75+ BFSI institutions and 5 state governments, processing 3.8 billion digital identification transactions and 10–13 million verifications a day — a scale that reflects both consumer fintech and public-sector deployment, which is a meaningfully different bar than consumer-only KYC software vendors are built to clear.If you're a bank, NBFC, fintech, or government body evaluating what to consolidate onto — or simply trying to move past the CKYC-vs-eKYC-vs-VKYC confusion this guide opened with — it's worth a closer look at the KYC Suite and a <a class="link" href="https://m2pfintech.com/contact-us/?utm_source=blg&amp;utm_medium=kyc&amp;utm_campaign=suite" target="_blank">conversation with the team</a> about which modules map to your actual onboarding gaps.

M2P's AI-Powered KYC Suite: A Complete Guide to Digital Identity Verification, Compliance, and Onboarding at Scale

<img src="https://m2p-website-file.s3.ap-south-1.amazonaws.com/marketing-cms/m2p-blog/2026/09/frm-banner-fraud-model-yesterdays-war_2x1.webp" alt="Your Fraud Model Is Fighting Yesterday's War" title="" id="h94ooqa0e" width="100%" height="362">Walk into most conversations about fraud and risk management, and you'll still hear the same vocabulary that's dominated the industry for two decades: chargebacks, card-not-present fraud, velocity checks, device fingerprinting. It's not wrong vocabulary — it's just increasingly incomplete.The uncomfortable truth is that a meaningful share of the FRM industry is still optimized for a fraud landscape that peaked a decade ago. Card fraud hasn't disappeared, but in market after market, it's being overtaken by categories that most legacy fraud stacks were never built to catch: authorized scams, mule networks, fraud rings that operate with the sophistication of venture-backed startups, and — coming faster than most risk leaders expect — a world where AI agents, not humans, are the ones initiating payments.This blog looks at four converging shifts that every risk leader should be thinking about right now — not as far-off, speculative trends, but as forces already reshaping what "fraud prevention" needs to mean.<h1 class="heading-node" id="bs27tteor">Why Most FRM Vendors Are Still Fighting Yesterday's Fraud</h1>For most of the last two decades, fraud prevention was built around a fairly clean mental model: a criminal steals a card number or account credential, and uses it to make an unauthorized transaction. Card networks, issuing banks, and FRM vendors built an entire architecture — 3D Secure, CVV checks, velocity rules, device fingerprinting, chargeback dispute workflows — around catching exactly this pattern.That architecture worked. Card-not-present fraud rates, as a share of transaction volume, have been broadly declining across mature markets for years as this tooling matured. But the fraud that's replaced it doesn't fit the same mental model at all — and that's the problem.Authorized push payment (APP) fraud — where the victim is deceived into initiating the payment themselves — doesn't look like fraud from a transaction-data perspective. The right cardholder authenticates with the right device from the right location and sends money to an account they were told, convincingly, was legitimate. Every signal a traditional fraud model was built to catch — mismatched device, unusual location, failed authentication — is absent. The fraud lives in the story the victim was told, not in the transaction's technical fingerprint.Mule account networks are the second blind spot. Modern fraud rings no longer rely on a single stolen identity moving money in one shot. They recruit or fabricate dozens or hundreds of mule accounts, each one individually unremarkable, and move money through them in small, plausible-looking increments before it's laundered out. A fraud system scoring each transaction in isolation — the dominant architecture for most of the last decade — simply can't see this pattern. You need network-level analysis: who's sending to whom, how often, and whether the graph of relationships between accounts looks like an organic customer base or a laundering pipeline.This is why the industry's obsession with model accuracy on historical fraud data is, in a real sense, backward-looking. A model tuned to catch card-present and card-not-present fraud with 99% precision can still be functionally blind to the categories now driving the majority of losses in many markets — because those categories were never in its training data to begin with, and they don't share the transaction-level signatures the model was built around.What this means for FRM strategy: the shift isn't "add more rules." It's a structural one — from transaction-level scoring to behavioral and network-level detection. That means modeling the victim's behavior during a transaction (hesitation patterns, unusual call activity, first-time payee additions under time pressure), not just the transaction itself. It means building graph-based mule detection that looks at the relationships between accounts, not each account in isolation. Vendors and institutions that haven't rebuilt around this shift are, quite literally, optimizing for a fraud problem that's shrinking while the one that's growing goes unaddressed.<h1 class="heading-node" id="rintx2zaq">What Fraud Looks Like in an Agentic AI World</h1>Here's a scenario that sounds speculative but is arriving faster than most risk teams have planned for: a consumer's AI agent — booking travel, managing subscriptions, comparison-shopping and checking out on their behalf — initiates a payment. No human clicks "buy." No human enters an OTP. The agent authenticates using credentials or delegated authority the consumer set up in advance, and the transaction completes autonomously.This isn't a hypothetical for 2030. Agentic checkout flows, agent-to-agent commerce protocols, and AI-driven personal assistants with payment authority are already being piloted by major platforms and card networks. And it fundamentally breaks assumptions that almost every fraud model — and almost every regulation, including PSD2's Strong Customer Authentication requirements — was built around: that the entity present at the moment of transaction is the accountholder, and that human behavioral patterns (typing cadence, mouse movement, session behavior) are a reliable fraud signal.When the "user" at checkout is an AI agent, several things change at once:<ul class="list-node" id="ijzw32q93"><li>Behavioral biometrics stop working as a fraud signal. There's no human typing pattern or navigation behavior to analyze — an agent's "behavior" is deterministic, fast, and doesn't vary the way a human's does, which means an entirely new signal set is needed to distinguish a legitimate agent transaction from a compromised or spoofed one.</li><li>Authentication needs a new model entirely. SCA's "something you know, have, or are" framework assumes a human is present to know, hold, or be something. Agent authentication needs to prove delegated authority — that this specific agent, with this specific scope of permission, was genuinely authorized by the accountholder to make this specific class of transaction.</li><li>New attack surfaces emerge. If an agent can be manipulated — through a compromised instruction, a poisoned data source, or an impersonated merchant API — the fraud doesn't require stealing a credential at all. It requires tricking the agent, which is a fundamentally different threat model than tricking a human or stealing a card number.</li><li>Liability becomes genuinely unclear. If an AI agent makes an unauthorized or ill-advised purchase, who's liable — the consumer who delegated authority, the platform that built the agent, or the merchant that accepted the transaction? None of today's card network rules or regional payment regulations have a clean answer yet.</li></ul>What this means for FRM strategy: risk teams don't need to solve this today, but they do need to start architecting for it — building fraud systems flexible enough to score a new category of "agent-initiated" transactions with their own signal set, rather than forcing agent transactions through a fraud model built entirely around human behavioral assumptions. The institutions that start this work now, before agentic commerce reaches meaningful volume, will have a multi-year head start over those that wait for the first major agentic fraud incident to force the issue.<h1 class="heading-node" id="ppzy94437">Fraud-as-a-Service: When Organized Crime Runs Like a SaaS Business</h1>If you want to understand why fraud has scaled so quickly in the last few years, look at how fraud rings now organize themselves — because it looks less like traditional organized crime and increasingly like a software business.Fraud-as-a-Service (FaaS) platforms, sold and marketed on dark web forums and encrypted messaging channels, now offer productized fraud tooling with the same packaging conventions as legitimate SaaS: tiered pricing, customer support, regular feature updates, and even reviews from other criminal "customers." A prospective fraudster doesn't need technical skill anymore — they can rent phishing kits with ready-made bank login clones, subscribe to services that generate synthetic identities complete with fabricated credit histories, or buy access to botnets that automate account takeover attempts at scale. Some platforms offer deepfake-generation-as-a-service specifically for bypassing liveness-detection KYC checks.This productization has three consequences that matter enormously for FRM strategy:Speed and scale have decoupled from skill. A single low-skill operator can now launch attacks that would have previously required a technically sophisticated team. This means the volume of attempted fraud attacks is scaling far faster than the sophistication curve most fraud models were tuned to expect.Attack patterns get shared and replicated instantly. When a FaaS platform's phishing kit successfully bypasses a particular bank's authentication flow, that exploit gets shared across the platform's entire customer base — potentially thousands of fraudsters — within days. A vulnerability that used to take months to spread through criminal networks now spreads at software-update speed.Detection needs to shift from "known fraud patterns" to "criminal infrastructure fingerprints." Because so much fraud now runs on shared tooling, the same phishing kit, the same synthetic-identity generator, or the same botnet infrastructure shows up across seemingly unrelated fraud attempts against different institutions. This is exactly why cross-institution data sharing and consortium fraud intelligence — recognizing the infrastructure behind an attack rather than just the transaction pattern — is becoming one of the highest-leverage defenses available. A single institution seeing an attack in isolation is fighting blind; a network of institutions sharing infrastructure-level signals can identify a FaaS-powered attack wave before it reaches its second or third target.What this means for FRM strategy: the response to an industrialized, productized fraud economy has to be equally systemic. That means investing in threat intelligence feeds that track known FaaS infrastructure (phishing domains, known botnet IP ranges, synthetic identity generation patterns), participating in consortium data-sharing arrangements where regulation allows, and treating fraud defense as an ecosystem problem rather than a single-institution one.<h1 class="heading-node" id="sj9n72r97">Quantum Computing and Fraud: Urgent Risk or Overhyped Threat?</h1>No thought-leadership conversation about the future of fraud is complete without someone raising quantum computing — usually with either breathless urgency or dismissive skepticism. The honest answer sits in between.The real risk is narrower than the headlines suggest, but it is real. A sufficiently powerful, fault-tolerant quantum computer could, in theory, break the asymmetric encryption (RSA and elliptic-curve cryptography) that underpins much of today's payment security — including TLS connections, digital signatures, and card network authentication protocols. This isn't the same thing as "quantum computers will let criminals hack your bank account next year." Current quantum computers are nowhere near the scale, error-correction capability, or stability needed to break production-grade encryption, and credible timelines from cryptography researchers generally place a genuine "cryptographically relevant" quantum computer somewhere in the 2030s at the earliest — with meaningful uncertainty even in that estimate.The "harvest now, decrypt later" risk is the part worth taking seriously today. Encrypted data intercepted and stored now could, in principle, be decrypted once quantum capability matures — meaning sensitive financial data with long-term value (account numbers, identity documents, transaction histories) is already at theoretical future risk, even though the decryption capability doesn't exist yet. This is why NIST finalized post-quantum cryptography standards in 2024, and why major payment networks, cloud providers, and financial institutions have begun (slowly) planning migration paths to quantum-resistant encryption.What this means for FRM strategy: this is not a "drop everything" emergency, but it is a "start the multi-year planning now" issue. Practical steps for risk leaders include asking technology and infrastructure vendors (including FRM vendors) about their post-quantum cryptography roadmap, prioritizing crypto-agility (the ability to swap encryption algorithms without a full system rebuild) in any new infrastructure investment, and treating long-lived sensitive data with an eye toward its multi-decade exposure window, not just its current-year risk. Institutions that wait until quantum computing is a proven, immediate threat will be starting a multi-year migration years too late.<h1 class="heading-node" id="5fmrbprze">The Common Thread: Fraud Prevention Has to Get Ahead of the Curve, Not Chase It</h1>These four shifts — scams and mules outpacing card fraud, agentic AI breaking today's authentication assumptions, fraud-as-a-service industrializing attacks, and quantum computing reshaping the long-term cryptographic horizon — look unrelated on the surface. They share one underlying lesson: the fraud landscape is moving faster than the architecture most FRM systems were built around, and reactive tuning of yesterday's models isn't a strategy for tomorrow's threats.The institutions that will manage this well aren't the ones with the single most accurate fraud model for today's known fraud patterns. They're the ones building FRM infrastructure flexible enough to extend to new signal types (behavioral, network, agent-authority, infrastructure-level) as fraud typologies shift — because in every one of these four areas, the next major fraud wave will look nothing like the last one.<h1 class="heading-node" id="hh2x447bq">See What a Forward-Built FRM System Looks Like</h1><a class="link" href="https://m2pfintech.com/frm-aml/?utm_source=blog&amp;utm_medium=sept&amp;utm_campaign=ai-scam" target="_blank">M2P's FRM </a>system is built around exactly this principle — real-time network and behavioral analysis for scam and mule detection, an architecture flexible enough to extend to new transaction types as agentic commerce and new payment models emerge, and infrastructure designed for the kind of adaptability this fraud landscape demands.If you're a risk leader trying to figure out whether your fraud stack is built for today's threats or still tuned to yesterday's, <a class="link" href="https://m2pfintech.com/contact-us/?utm_source=blog&amp;utm_medium=sept&amp;utm_campaign=ai-scam" target="_blank">reach out to M2P</a> to explore how our FRM system can help you get ahead of where fraud is actually headed.

Tag: "Banking As A Service Baas (17)"

Product

About

Compliance

Resources