In September 2026, the Unified Payments Interface (UPI) crossed <a class="link" href="https://www.npci.org.in/product/upi/product-statistics" target="_blank">24 billion transactions in a single month</a>, according to National Payments Corporation of India (NPCI) data. Each transaction must be matched across banks, switches, aggregators, and ledgers, often inside a daily settlement window.Volume is only part of the problem. New rails such as the Digital Rupee, richer ISO 20022 message data, and cross-border linkages add formats, currencies, and time zones that payment reconciliation software built for fewer rails was not designed to absorb.Regulation adds pressure on where this work runs. Since 2018, the Reserve Bank of India (RBI) has required payment system data to be stored only in India, which shapes how reconciliation platforms are hosted.For banks, this creates a widening gap between how fast payments change and how fast reconciliation can adapt. This guide covers eight shifts behind that gap, what each means for reconciliation, and the capabilities a future-ready platform needs.<h2 class="heading-node" id="61iq7us5u">1. UPI's Next Phase, UPI Lite, and International UPI: More Variables to Reconcile</h2>UPI is the foundation of digital payments in India, and its feature set keeps growing. Recent and proposed enhancements, such as overdraft-linked accounts, invoice verification, and signed intent, introduce new data points that reconciliation teams must track and match.UPI Lite's offline mode and NPCI's push to expand international merchant acceptance add more data sources and more reconciliation endpoints to an already high-volume rail.What this means for reconciliation: Platforms must connect to multiple UPI application programming interfaces (APIs), process settlement updates in near real time, and reconcile multi-currency transactions as cross-border UPI use grows.<h2 class="heading-node" id="zw9309opr">2. CBDC (Digital Rupee): Reconciling Against a Central Ledger</h2>The RBI's Central Bank Digital Currency (CBDC) pilot covers both retail and wholesale use. CBDC transactions differ from conventional bank transfers, and settlements may need verification against a ledger that the central bank maintains.What this means for reconciliation: Platforms will need ledger verification steps in their workflows. They must also reconcile CBDC settlements alongside traditional rails without duplicating records or losing data.<h2 class="heading-node" id="y8zccgg0c">3. Open Banking and the Account Aggregator Framework</h2>Under RBI regulation, the Account Aggregator (AA) framework lets regulated institutions share financial data securely, based on customer consent. For reconciliation, this could support transaction matching across institutions and products. A single view could bring together loan disbursements, card payments, and mutual fund redemptions across banks and fintechs.What this means for reconciliation: Engines must ingest standardized data formats, such as Non-Banking Financial Company–Account Aggregator (NBFC-AA) specifications, and operate across product lines and regulatory requirements.<h2 class="heading-node" id="o3rwfft3d">4. FASTag and Transit Payments: The Rise of Micro-Transactions</h2>FASTag use is extending beyond highway tolls into areas such as parking and urban transit. These high-volume, low-value payments often need aggregation before settlement.What this means for reconciliation: Systems must process large volumes of low-value transactions efficiently, so merchant settlements are not delayed.<h2 class="heading-node" id="ld4trxf5y">5. Tightening Compliance and Settlement Expectations</h2>Regulatory expectations for fraud monitoring, suspicious transaction reporting, and settlement timelines continue to tighten. Payment aggregators, for example, operate under RBI-defined settlement timelines for merchants.What this means for reconciliation: Reconciliation should shift from end-of-day batch processing toward continuous matching. Teams can then meet compliance thresholds as transactions occur, not only report on them afterward.<h2 class="heading-node" id="mlxf5jdrp">6. ISO 20022 Migration: Richer Data, More Complex Structures</h2>The RBI has indicated plans to adopt ISO 20022 for payment messaging systems such as Real Time Gross Settlement (RTGS) and National Electronic Funds Transfer (NEFT). The standard carries richer transaction metadata, which can improve matching accuracy. It also introduces more complex data structures.What this means for reconciliation: Platforms should be ISO 20022-ready, able to parse, match, and analyze the additional fields while maintaining processing performance.<h2 class="heading-node" id="20ngkvg62">7. Cross-Border Payment Integration</h2>India is connecting its payment systems with international partners, including Singapore through the PayNow–UPI linkage and the United Arab Emirates (UAE). These linkages bring more currencies, time zones, and regulatory regimes into everyday transaction flows.What this means for reconciliation: Systems must manage foreign exchange (FX) conversions, normalize time zones, and support compliance reporting across jurisdictions.<h2 class="heading-node" id="u6myblg3y">8. Data Localization and Security</h2>RBI guidelines require payment system data for Indian transactions to be stored only in India. Reconciliation platforms therefore need an architecture built for local data storage, while still integrating with global systems.India's Digital Personal Data Protection Act adds a broader data protection layer on top of these requirements. Our guide to the <a class="link" href="https://m2pfintech.com/blog/dpdp-act-data-residency-cloud-reconciliation-platforms/" target="_blank">DPDP Act and data residency for cloud-hosted reconciliation</a> platforms covers this in detail.What this means for reconciliation: Cloud-native solutions must run on India-based data centers and adapt as data privacy requirements evolve.<h2 class="heading-node" id="ars5y6yzw">What a Future-Ready Reconciliation Architecture Looks Like</h2>A future-ready reconciliation platform delivers six core capabilities:<ul class="list-node" id="2e9fn16g9"><li>API-first, modular design: Easy integration with new payment rails, fintech platforms, and regulatory APIs</li><li>Multi-rail, multi-currency support: Dynamic matching logic that adapts to UPI, NEFT, Immediate Payment Service (IMPS), FASTag, CBDC, and future payment types through configuration rather than major code rewrites</li><li>Predictive exception handling: <a class="link" href="https://m2pfintech.com/blog/ai-powered-payment-reconciliation-software/" target="">AI-driven</a> detection of patterns likely to cause mismatches, such as recurring delays from specific payment service providers</li><li>Continuous reconciliation: Streaming data processing in place of batch cycles</li><li>Compliance-by-design: Automated record retention, regulatory reporting, and audit trail creation</li><li>Cloud-native, geo-compliant infrastructure: Scalability that satisfies RBI data localization requirements</li></ul><h2 class="heading-node" id="chd91xdvo">How Recon360 Supports Future-Ready Reconciliation</h2>Recon360, M2P Fintech's reconciliation and settlement automation platform, is designed around these principles. It helps banks, fintechs, and payment businesses automate reconciliation across rails, products, and partners from a single platform.<table class="border-collapse border border-gray-300" style="min-width: 50px;"><colgroup><col style="min-width: 25px;"><col style="min-width: 25px;"></colgroup><tbody><tr class="border border-gray-300"><th class="border border-gray-300 bg-gray-100" colspan="1" rowspan="1">Capability</th><th class="border border-gray-300 bg-gray-100" colspan="1" rowspan="1">How Recon360 Supports It</th></tr><tr class="border border-gray-300"><td class="border border-gray-300 p-2" colspan="1" rowspan="1">Multi-rail, multi-source reconciliation</td><td class="border border-gray-300 p-2" colspan="1" rowspan="1">Reconciles payment, settlement, and General Ledger (GL) data across sources, entities, and currencies in one workflow</td></tr><tr class="border border-gray-300"><td class="border border-gray-300 p-2" colspan="1" rowspan="1">AI-driven matching and <a class="link" href="https://m2pfintech.com/blog/exception-management-reconciliation-best-practices/" target="_blank">exception handling</a></td><td class="border border-gray-300 p-2" colspan="1" rowspan="1">Applies AI-driven matching and configurable rules, and routes breaks to assigned owners with context attached</td></tr><tr class="border border-gray-300"><td class="border border-gray-300 p-2" colspan="1" rowspan="1">Continuous monitoring</td><td class="border border-gray-300 p-2" colspan="1" rowspan="1">Supports near-real-time transaction monitoring alongside scheduled reconciliation cycles</td></tr><tr class="border border-gray-300"><td class="border border-gray-300 p-2" colspan="1" rowspan="1">Configurable workflows</td><td class="border border-gray-300 p-2" colspan="1" rowspan="1">Lets teams adapt rules and workflows as new payment methods and data formats arrive</td></tr><tr class="border border-gray-300"><td class="border border-gray-300 p-2" colspan="1" rowspan="1">Audit readiness</td><td class="border border-gray-300 p-2" colspan="1" rowspan="1">Maintains a timestamped audit trail for every match, exception, and action</td></tr><tr class="border border-gray-300"><td class="border border-gray-300 p-2" colspan="1" rowspan="1">Deployment flexibility</td><td class="border border-gray-300 p-2" colspan="1" rowspan="1">Supports cloud and on-premise deployment, so institutions can align hosting with data residency requirements</td></tr></tbody></table>Because these capabilities sit on one platform, a bank adding a new rail can extend existing workflows instead of building a separate reconciliation process for each one. Teams also see exceptions, settlements, and ledger positions in the same place, which shortens the path from a mismatch to its cause.<h2 class="heading-node" id="laguux0j6">Why Strategic Adoption Matters Now</h2>Indian financial institutions face a twofold challenge: payment innovation cycles are shortening while compliance expectations tighten. The gap between what is possible and what is mandated keeps narrowing.Intelligent reconciliation helps institutions respond to both. It supports onboarding new payment methods in a structured way and meeting compliance requirements with less manual effort.Institutions that delay modernizing reconciliation face growing exception backlogs, settlement delays, and audit exposure as rails multiply. Customer expectations are also rising for faster refunds, real-time account visibility, and proactive fraud prevention.In a market where speed, accuracy, and trust shape outcomes, future-ready reconciliation is a strategic capability, not only an operational upgrade.To see how <a class="link" href="https://m2pfintech.com/recon360/" target="">Recon360</a> supports reconciliation across rails, products, and partners, connect with <a class="link" href="https://m2pfintech.com/contact-us/?utm_source=Blog&amp;utm_medium=Recon360&amp;utm_campaign=future_ready_reconciliation" target="">our team</a>.<h2 class="heading-node" id="k2108hyss">Frequently Asked Questions</h2>What is future-ready reconciliation? It is a reconciliation approach built to adapt to new payment rails, data standards, and regulations. It relies on API-first design, continuous matching, AI-driven exception handling, and built-in compliance.How do newer UPI features affect bank reconciliation? Features such as overdraft-linked accounts, invoice verification, and signed intent add new variables. Banks must integrate multiple UPI APIs and handle near-real-time settlements and multi-currency flows.Why does ISO 20022 matter for reconciliation? ISO 20022 carries richer transaction metadata, which can improve matching accuracy. Platforms must be able to parse these more complex structures without slowing down.How do RBI data localization rules affect reconciliation platforms? Payment system data for Indian transactions must be stored in India. Reconciliation platforms need India-based data centers while still integrating with global systems.How does Recon360 support future-ready reconciliation? Recon360 reconciles payment, settlement, and GL data across rails, entities, and currencies on one platform. It combines AI-driven matching, configurable exception workflows, audit trails, and cloud or on-premise deployment options.

Future-Ready Reconciliation: How Banks Can Keep Pace With India's Payment Shifts

Kenya and Nigeria are two of Africa's busiest digital lending markets. The Central Bank of Kenya reports that licensed digital lenders had disbursed KSh 150.56 billion across more than 8.37 million loans by May 2026. As volumes grew, both countries tightened the rules on two fronts. They brought digital lenders under formal licensing, and they passed data-protection laws that limit how far an algorithm can decide a loan without a person involved.For a lender using AI in underwriting, that creates a clear test. A human must remain accountable for material decisions. The lender must be able to explain each outcome. And a record must exist that a regulator or borrower can ask to see. This post covers what each market requires and how an origination platform can be built to meet it.<h3 class="heading-node" id="cg6lyyahg">Kenya: licensed lenders, limited automation</h3>Kenya's Central Bank (Digital Credit Providers) Regulations, 2022 put digital lenders under CBK licensing. The CBK assesses each applicant's business model, consumer-protection practices, and the suitability of its owners and managers. By mid-2026 it had licensed over 250 providers from more than 800 applications. The regime targets expensive borrowing, abusive collection practices, and misuse of borrowers' personal data.The Data Protection Act 2019, enforced by the Office of the Data Protection Commissioner (ODPC), adds two provisions that matter for AI-led credit:<ul class="list-node" id="q6ooi81op"><li>Section 35: People have the right not to be subjected to a decision based solely on automated processing, including profiling, when it significantly affects them. A loan approved or declined with no human involvement falls squarely within this.</li><li>Section 31: A data protection impact assessment is required before high-risk processing. ODPC regulations treat automated decision-making that determines access to services as high-risk.</li></ul>Enforcement is real. The ODPC has issued 184 compensation orders, 134 enforcement notices, and 20 penalty notices since the Act came into force. Digital lenders have been penalised over consent and personal-data handling.<h3 class="heading-node" id="0i4e44s2b">Nigeria: a conduct crackdown and a data law with teeth</h3>Nigeria divides oversight between two regulators.On conduct, the Federal Competition and Consumer Protection Commission (FCCPC) issued its Digital, Electronic, Online or Non-Traditional Consumer Lending Regulations in 2025. They took effect on 21 July 2025, with a compliance deadline of 5 January 2026. Any lender operating through an app, website, or other non-traditional channel must register with the FCCPC. The rules target abusive recovery, unauthorised access to borrower data, and predatory pricing. The Federal High Court in Lagos upheld them in July 2026 after a trade body challenged the FCCPC's authority.On data, the Nigeria Data Protection Act 2023 created the Nigeria Data Protection Commission (NDPC). Section 37 gives individuals the right not to be subject to a solely automated decision with legal or similarly significant effects, plus the right to human intervention and to contest the outcome. The Act also expects lenders to tell borrowers when automated decision-making or profiling is used. A DPIA is mandatory before high-risk processing.The NDPC enforces actively. It fined Fidelity Bank ₦555.8 million in August 2024 for processing customer data without valid consent. In June 2026 it announced a review of the Act focused on AI, robotics, and big data.<h3 class="heading-node" id="iw6ledkkd">Four expectations that apply in both markets</h3>The regulators and statutes differ, but their expectations of AI in credit are very similar.<ol class="list-node" id="mxf1h2obn"><li>A person stays accountable. A credit approval or rejection is the clearest example of a decision that significantly affects someone. Human oversight is how lenders stay on the right side of both laws.</li><li>Decisions are explainable. If a borrower can contest an automated outcome, the lender must show what drove it.</li><li>High-risk processing is assessed and documented. A DPIA presumes the lender can describe how its system uses data and reaches a result.</li><li>The evidence exists. Enforcement in both markets turns on consent records, processing records, and audit trails. A lender that can't produce them can't prove compliance, however good its model is.</li></ol><h3 class="heading-node" id="0mnw3lqxr">How M2P's Loan Origination System supports these expectations</h3>M2P's AI-orchestrated Loan Origination System runs digital, branch, embedded, and partner-led origination on one platform. Its configurable Business Rules Engine (BRE) handles credit decisioning, supported by AI agents for documents, fraud, financial analysis, scoring, and credit appraisal memos (CAMs). Here is how that design maps to the four expectations.<table class="border-collapse border border-gray-300" style="min-width: 50px;"><colgroup><col style="min-width: 25px;"><col style="min-width: 25px;"></colgroup><tbody><tr class="border border-gray-300"><th class="border border-gray-300 bg-gray-100" colspan="1" rowspan="1">What regulators expect</th><th class="border border-gray-300 bg-gray-100" colspan="1" rowspan="1">How the M2P LOS supports it</th></tr><tr class="border border-gray-300"><td class="border border-gray-300 p-2" colspan="1" rowspan="1">A human stays accountable</td><td class="border border-gray-300 p-2" colspan="1" rowspan="1">AI agents cut manual effort and turnaround across documents, fraud checks, financial analysis, and CAM generation, while approvals, policies, and accountability stay with the business. Straight-through workflows carry multi-level approvals and deviation management, and the Case Manager routes exceptions to people.</td></tr><tr class="border border-gray-300"><td class="border border-gray-300 p-2" colspan="1" rowspan="1">Explainable decisions</td><td class="border border-gray-300 p-2" colspan="1" rowspan="1">The BRE produces explainable decision outputs, with risk-based pricing and custom scorecards. The Credit Scoring Agent uses explainable AI, and the Policy Compliance Agent validates cases against internal credit policy and flags deviations with auditable outcomes.</td></tr><tr class="border border-gray-300"><td class="border border-gray-300 p-2" colspan="1" rowspan="1">Documented high-risk processing</td><td class="border border-gray-300 p-2" colspan="1" rowspan="1">Rules, scorecards, and pricing logic are configured explicitly, and the BRE supports back-testing against historical portfolio data. Lenders can describe and test how decisions are made before go-live, which is the groundwork a DPIA needs.</td></tr><tr class="border border-gray-300"><td class="border border-gray-300 p-2" colspan="1" rowspan="1">An audit trail that holds up</td><td class="border border-gray-300 p-2" colspan="1" rowspan="1">The BRE supports versioning and auditability for controlled policy enforcement. Case Manager keeps full audit trails with SLA and turnaround monitoring, and role-based dashboards and 100+ configurable MIS reports show decisions, deviations, and policy performance.</td></tr><tr class="border border-gray-300"><td class="border border-gray-300 p-2" colspan="1" rowspan="1">Controlled data handling</td><td class="border border-gray-300 p-2" colspan="1" rowspan="1">75+ pre-integrated APIs cover KYC, banking, bureau, documents, payments, and fraud services. M2P holds ISO 27001, ISO 22301, and PCI DSS certifications. [Product team to confirm deployment and data-residency options for Kenya and Nigeria.]</td></tr></tbody></table>The design principle is that AI supports the decision and the business owns it. The Document Intelligence, Bank Statement Analyser, Financial Analysis, and Fraud Detection agents do the preparatory work, such as classifying documents, parsing statements, and flagging tampering and velocity anomalies. Credit and risk teams then decide within policy. That division is what the automated-decision rules in both countries push lenders toward.The record matters most when a regulator or borrower asks for it. Because decisions run through a governed rules engine and exceptions pass through a managed case workflow, the evidence is created as the loan moves through origination. It doesn't have to be rebuilt afterwards.<h3 class="heading-node" id="z0w5dd8mc">Preparing for a Kenya or Nigeria launch</h3>A few questions are worth settling early:<ul class="list-node" id="k0vrzomv2"><li>Local data sources: Identity documents, credit bureaus, and bank or mobile-money statements differ by market. Plan the integration layer first, since this is where most localisation effort goes.</li><li>Human review points: Decide which decisions (declines, high-value loans, borderline scores) must go to a person, and configure them as explicit rules.</li><li>DPIA readiness: Document data inputs, scoring logic, and exception handling before launch, not after the first regulatory query.</li><li>Cross-border data flows: Both countries attach conditions to moving personal data abroad. Lenders using externally hosted AI models should confirm where borrower data goes and on what basis.</li><li>Consent and notice: Make sure borrowers are told when automated processing or profiling is involved, and that consent records are retrievable.</li></ul><h3 class="heading-node" id="s97y51d7y">Where the rules are heading</h3>Neither regulator has finished. Nigeria has said it will update its data law to address AI directly, and Kenya's ODPC has signalled closer scrutiny of automated decision-making. The rules on how machines may decide loans are becoming more explicit, and enforcement is already live.For lenders, the safest path is governed, explainable decisioning that keeps people in control. That meets today's rules and leaves room for what both regulators are drafting next.Planning to lend in Kenya or Nigeria? Talk to M2P about how our Loan Origination System can bring explainable, auditable credit decisioning to your lending operations. <a class="link" href="https://m2pfintech.com/contact-us/">Get in touch →</a>

Nigeria NDPA & Automated Loan Decisions: What Lenders Need

M2P has been recognized in the Chartis Research RiskTech100® 2027, securing the 75th position globally, placing us among the world’s leading providers of risk and compliance technology.&nbsp;The RiskTech100® is an independent evaluation of the world’s top 100 risk technology providers. It assesses companies across strategic vision, market impact, innovation, customer success, and execution, making it one of the industry’s most respected benchmarks for risk technology leadership.&nbsp;&nbsp;For M2P, this recognition reflects the strength of our Enterprise Fraud &amp; Risk Management platform and our ability to help financial institutions manage fraud across increasingly complex, real-time digital ecosystems.&nbsp;<h2 class="heading-node" id="oha41xh1o" style="text-align: left;">Built for the New Reality of Financial Fraud&nbsp;</h2>Fraud no longer happens within a single channel or at a single point in the customer journey. It moves across onboarding, login, cards, wallets, payments, transfers, BNPL, digital banking, and account servicing.&nbsp;Static rules and fragmented systems cannot keep pace with this complexity.&nbsp;M2P’s Enterprise Fraud &amp; Risk Management platform brings together real-time decisioning, AI and ML-driven risk models, explainable intelligence, behavioral profiling, device intelligence, and enterprise-grade case management in one integrated environment.&nbsp;This enables banks, fintechs, and financial institutions to detect emerging threats earlier, make faster risk decisions, and strengthen fraud operations without adding unnecessary friction to legitimate customer journeys.&nbsp;As Sid Dash, Chief Researcher at Chartis, noted: "M2P Fintech's innovations in fraud risk management provide integrated decisioning across channels, allowing the unification of data silos at scale and the detection of anomalies in real time. This and their high performance and the low latency/real-time nature of its solution is the principal driver for their debut appearance in our RiskTech100® 2027."&nbsp;<h2 class="heading-node" id="ctjbch9vx" style="text-align: left;">Intelligence That Goes Beyond Rules&nbsp;</h2>M2P’s EFRM platform is designed to continuously assess risk using multiple signals across transactions, customers, devices, channels, and behaviors.&nbsp;Its capabilities include:&nbsp;<ul class="list-node" id="g5y5kuwva"><li>Real-time fraud detection and prevention across financial products and channels&nbsp;</li></ul><ul class="list-node" id="4u80kk4zb"><li>Configurable rules and decision engines that allow institutions to adapt strategies as threats evolve&nbsp;</li></ul><ul class="list-node" id="w50h39ymp"><li>AI and ML-driven risk models for more accurate and dynamic fraud detection&nbsp;</li></ul><ul class="list-node" id="fqvg5we0n"><li>Explainable AI that gives risk teams greater visibility into model-led decisions&nbsp;</li></ul><ul class="list-node" id="omhep96tr"><li>Behavioral profiling and biometrics to identify unusual customer activity&nbsp;</li></ul><ul class="list-node" id="7jurjsyh3"><li>Device and contextual intelligence, including device fingerprinting, rooted or jailbroken device detection, IP intelligence, VPN and proxy detection, and location-based risk analysis&nbsp;</li></ul><ul class="list-node" id="951i7hu4f"><li>Listening mode, allowing institutions to validate models before full deployment and optimize performance while reducing false positives&nbsp;</li></ul>Together, these capabilities help institutions move from reactive fraud monitoring to adaptive, intelligence-led risk management.&nbsp;<h2 class="heading-node" id="vn0e3qjxp" style="text-align: left;">From Detection to Resolution&nbsp;</h2>Effective fraud management does not stop at generating an alert.&nbsp;M2P supports the complete fraud operations lifecycle through enterprise-grade case management. The platform enables automated case creation, intelligent prioritization, configurable analyst workflows, automated investigation support, and customer validation across SMS, IVR, and in-app channels.&nbsp;Detailed dashboards and reporting provide risk teams with the visibility needed to monitor patterns, accelerate investigations, maintain auditability, and respond more effectively to changing threats.&nbsp;The result is a more connected operating model that helps institutions detect, investigate, and resolve fraud faster.&nbsp;<h2 class="heading-node" id="a5c0l29f8" style="text-align: left;">Proven at Enterprise Scale&nbsp;</h2>M2P’s fraud and risk capabilities support banking and fintech environments spanning card programs, digital banking, customer onboarding, BNPL, wallets, and real-time payments. Our platform is designed for complex enterprise deployments and can be implemented across cloud and on-premises environments.&nbsp;With a presence across 30+ markets and engagements with 200+ banks, 300+ lenders, and 800+ fintechs, M2P combines global infrastructure scale with a deep understanding of how financial products, payment journeys, and risk operations work together.&nbsp;<h2 class="heading-node" id="utfd2byzf" style="text-align: left;">Recognition That Strengthens Our Ambition&nbsp;</h2>Being named in the Chartis RiskTech100® 2027 is more than an industry accolade. It validates our direction: building intelligent, scalable, and adaptive risk technology for the next generation of financial services.&nbsp;As AI, real-time payments, and digital financial journeys create new opportunities, they also create new risk vectors. M2P remains focused on helping institutions innovate confidently, combining speed and customer experience with the intelligence, control, and resilience required to operate securely.&nbsp;We are proud to be recognized among the world’s leading risk technology providers and even more determined to keep advancing what modern fraud prevention and risk intelligence can deliver.&nbsp; Explore: <a class="link Hyperlink SCXW154504172 BCX0" href="https://www.chartis-research.com/market-analysis/7947547/risktech100r-2027-ranking-and-award-winners" target="_blank" rel="noreferrer noopener">Chartis RiskTech100® 2027 rankings and award winners</a>.

M2P Named Among the World’s Leading Risk Technology Providers in Chartis RiskTech100® 2027

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/paisaback_m2p_blog_v8_2000x1000.webp" alt="M2P Powers Federal Bank Paisaback Co-Branded Credit Card" title="" id="azfbg8gmd" width="100%" height="451"><a class="link" href="https://m2pfintech.com/" target="_blank">M2P Fintech</a> is powering Paisaback, the co-branded credit card launched by <a class="link" href="https://timesofindia.indiatimes.com/business/india-business/federal-bank-paisabazaar-launch-paisaback-credit-card-with-7-5-cashback-on-grocery-spends/articleshow/134061265.cms" target="_blank">Federal Bank in partnership with Paisabazaar</a>. The card runs on <a class="link" href="https://m2pfintech.com/credit-cards/" target="_blank">M2P's Credit Card Stack</a>, which handles everything from issuance and day-to-day card management to the cashback at the heart of the product.The idea behind <a class="link" href="https://www.paisabazaar.com/federal-bank/paisabazaar-paisaback-credit-card/" target="_blank">Paisaback</a> is simple. Most people want a credit card that pays them back on what they already spend, without having to learn a points system or wait on a redemption catalogue. Paisaback does exactly that, offering cashback across everyday spend categories, including a distinctive cashback on everyday transactions made using the credit card. With UPI now the way so many Indians pay, from the neighbourhood grocer to monthly bills, rewarding those payments makes the card useful in the moments that matter most.Getting a proposition like this to market quickly takes more than a good idea. It needs a card platform that adapts to the product, not the other way round. That is where <a class="link" href="https://m2pfintech.com/blog/why-switch-to-a-new-age-credit-card-system/" target="_blank">M2P's Credit Card Management System</a> comes in. It manages the full life of a card, from onboarding the customer and issuing the card to setting credit limits, authorising transactions, generating statements and processing repayments. Just as importantly, M2P's Credit Card Management System can be configured quickly to support differentiated product constructs like this one. That flexibility lets issuers design card propositions for different customer segments and interests, and bring them to market faster. For Paisaback, it meant a fast launch and a platform ready to grow with the program.<a class="link" href="https://m2pfintech.com/blog/6-reasons-cards-issuers-need-modern-api-based-platform/" target="_blank">APIs</a> sit at the heart of the stack, which lets it connect smoothly with a partner's digital journeys. Customers can discover the card, apply for it and manage it through channels they already know and trust, which is exactly what a digital-first co-branded card needs.The cashback itself is delivered through <a class="link" href="https://m2pfintech.com/rewards/" target="_blank">M2P's Rewards platform</a>, working hand in hand with the <a class="link" href="https://m2pfintech.com/blog/what-is-a-card-management-system-the-complete-guide-to-modern-card-issuance-2026/" target="_blank">Credit Card Management System</a>. The rules engine decides how cashback is earned, whether by spend category, merchant, payment type such as UPI, spend thresholds or monthly caps. As transactions come through, rewards are worked out accurately and credited transparently, so customers can clearly see the value they are getting. For the issuer, the same platform makes it easy to fine-tune the program over time, whether that means running a festive offer, adding a partner campaign or shifting focus to a new spend category, all without touching the core card platform. Behind every swipe, UPI payment and cashback credit sits a set of transactions that must be matched and settled accurately. This is where <a class="link" href="https://m2pfintech.com/recon360/" target="_blank">M2P's reconciliation</a> product completes the stack. It automates the back-office work of matching transaction records across networks, processors and the bank's ledgers, and flags exceptions early so they can be resolved quickly. For a program like Paisaback, that means fewer manual checks, cleaner books and greater confidence as transaction volumes grow.<a class="link" href="https://m2pfintech.com/blog/why-co-branded-credit-cards-are-the-next-frontier-for-banks/" target="_blank">Co-branded cards</a> work best when the value is easy for customers to understand and the technology behind them is easy for issuers to shape. Paisaback is a great example of that. Paisabazaar knows what today's credit customers are looking for, and our role has been to give that insight a platform that can launch quickly and scale with confidence. We're excited to be part of this journey.Paisaback brings together three strengths. <a class="link" href="https://www.federal.bank.in/" target="_blank">Federal Bank</a> contributes its banking franchise and credit card expertise, Paisabazaar brings its deep understanding of consumer credit needs and wide digital reach, and M2P provides the technology that ties it all together. The result is a card that is simple, rewarding and digital-first.M2P Fintech looks forward to supporting Federal Bank and Paisabazaar as Paisaback continues to grow, and to enabling more innovative credit experiences across the BFSI sector.

M2P's Credit Card Stack Powers Federal Bank Paisaback Co-Branded CC Program with Paisabazaar

<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/blog_banner_ai_reconciliation_2000x1000.webp" alt="AI-Powered Payment Reconciliation Software: A Smarter Approach to Managing Transaction Complexity" title="" id="4s812ec51" width="100%" height="466">A single payment today can touch five or six systems before it's fully accounted for: a gateway, a switch, a bank, a card network, sometimes a wallet provider in between. Multiply that across UPI, cards, cross-border rails, and BNPL, and it's easy to see why reconciliation has quietly become one of the hardest operational problems in fintech.Most payment reconciliation software was built for a simpler version of this problem. It's why so many finance and operations teams still spend hours chasing exceptions that a rules engine couldn't quite figure out.<h2 class="heading-node" id="m7xhielkc">Why Transaction Complexity Has Outgrown Traditional Reconciliation</h2>A decade ago, reconciling payments meant matching a handful of predictable data formats. Today, a single business might reconcile UPI settlements, card network files, wallet payouts, and cross-border remittances, each with its own file structure, timing, and fee logic.Traditional rule-based reconciliation software handles this reasonably well when data behaves. The moment a partner changes a file format, adds a new fee line, or delays a settlement batch, rigid rules start throwing exceptions that need a human to sort out. At scale, that's not an edge case. It's the daily reality for most reconciliation teams.<h2 class="heading-node" id="a8cwx4qk2">Where Rule-Based Automation Still Falls Short</h2>Rule-based automated reconciliation software genuinely improved on spreadsheets. But "automated" and "intelligent" aren't the same thing.A rules engine still needs someone to write, test, and maintain every matching condition. New payment partner, new rule. New currency, new rule. New edge case, another rule, and another exception queue to clear manually.The deeper issue is that rules don't learn. If an analyst resolves the same type of mismatch fifty times a month, that knowledge stays with the analyst, not the system. The next similar exception starts from zero again.<h2 class="heading-node" id="5326xeoe7">What "AI-Powered" Actually Means in Reconciliation</h2>It's worth being precise here, because "AI-powered" gets used loosely in fintech marketing. In <a class="link" href="https://m2pfintech.com/blog/common-reconciliation-terms-finance-professionals/" target="">reconciliation</a>, it should mean something specific: the system can recognize patterns beyond exact-match rules, and it improves its matching logic based on historical resolution behavior.That looks like fuzzy matching that accounts for minor formatting differences, timing shifts, or fee deductions, rather than failing a match over a one-rupee variance. It also looks like a system that notices when a category of exception keeps getting resolved the same way, and starts handling it automatically, with a clear audit trail showing why.This is where agentic payment reconciliation software enters the conversation. Instead of just flagging a mismatch and waiting, an AI agent can investigate the discrepancy across connected data sources, propose a resolution, and act on it within defined guardrails, with a human able to review or override at any point.<h2 class="heading-node" id="r6h0mrz2s">From Matching to Resolution: The Real Shift</h2>Most reconciliation software stops at detection. It tells you what doesn't match and leaves the "why" and "what now" to a person.An agentic approach closes that gap. When a settlement file doesn't tie out to a gateway report, the system can cross-reference switch data, bank postings, and prior similar cases, then narrow down a likely cause before an analyst even opens the exception.This matters more than it sounds. In most reconciliation teams, matching isn't the bottleneck. Investigation is. Analysts spend far more time figuring out why something broke than actually fixing it once they know. Shifting that investigative work to the system, rather than just the matching work, is where the real time savings show up.<h2 class="heading-node" id="0wyno5yh2">What This Looks Like in Practice</h2>Consider a payment aggregator settling across multiple acquiring banks. A batch comes in where net settlement amounts are consistently short by a small, variable margin. A rules engine flags every instance as a break. An AI-driven system, over time, recognizes this as a recurring fee-deduction pattern tied to one specific acquirer and starts auto-classifying it, freeing analysts to focus on the exceptions that actually need judgment.Or take a digital bank reconciling GL entries against core banking postings and payment switch data simultaneously, across multiple legal entities and currencies. Doing this manually, or even with static rules, means reconciling each source pair separately and manually consolidating results. A platform designed for multi-source reconciliation can process all three simultaneously and surface a single, entity-level view of what's outstanding.<h2 class="heading-node" id="6ykgzr015">What CFOs and Ops Leaders Should Actually Look For</h2>With AI reconciliation now a crowded claim, a few questions cut through the noise:<ul class="list-node" id="yvvzjfw0e"><li>Is the matching logic explainable? A system that can't show why it matched or flagged something isn't audit-ready, no matter how advanced its AI sounds.</li><li>Does it learn from resolutions, or just detect exceptions? Detection without learning just moves the manual work downstream.</li><li>Is it genuinely platform and data-source agnostic? Reconciliation software that only works cleanly with a handful of formats will keep generating rule debt.</li><li>Can it scale across entities, currencies, and business lines without re-architecting the setup each time?</li></ul>This is the standard <a class="link" href="https://m2pfintech.com/recon360/" target="">Recon360</a> is built against. It combines AI-driven transaction matching with <a class="link" href="https://m2pfintech.com/blog/exception-management-reconciliation-best-practices/" target="">exception management</a> that learns from resolution history, <a class="link" href="https://m2pfintech.com/blog/gl-reconciliation-vs-payment-reconciliation-whats-the-difference-and-why-your-finance-team-needs-both/" target="">GL reconciliation</a> and settlement automation on a single platform, and full audit traceability across every automated decision, so finance and operations teams get speed without losing control.Transaction complexity isn't going to plateau. Payment rails, partners, and formats will keep multiplying. The reconciliation software built to keep pace won't just match faster. It will understand why things break, and get better at handling it every cycle.<a class="link" href="https://m2pfintech.com/contact-us/?utm_source=Blog&amp;utm_medium=Recon360&amp;utm_campaign=ai_powered_payment_reconciliation_software" target="">Get in touch</a> with our team to see how Recon360 can become the invisible engine behind faster, more accurate reconciliation.Follow us on&nbsp;<a class="link fui-Link ___1q1shib f2hkw1w f3rmtva f1ewtqcl fyind8e f1k6fduh f1w7gpdv fk6fouc fjoy568 figsok6 f1s184ao f1mk8lai fnbmjn9 f1o700av f13mvf36 f1cmlufx f9n3di6 f1ids18y f1tx3yz7 f1deo86v f1eh06m1 f1iescvh fhgqx19 f1olyrje f1p93eir f1nev41a f1h8hb77 f1lqvz6u f10aw75t fsle3fq f17ae5zn" href="https://www.linkedin.com/company/m2pfintech/mycompany/" target="_blank" rel="noreferrer noopener">LinkedIn</a>&nbsp;and&nbsp;<a class="link" href="https://x.com/m2pfintech" target="_blank">X</a>&nbsp;for insightful fintech bytes curated for curious minds like you.

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