Merchant Acquiring in Africa: Opportunities in Low-Card, High-Mobile Markets

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 Across Southeast Asia, much of the work of financial inclusion is done by small institutions. Rural banks, cooperative banks, credit cooperatives, people&#39;s credit funds and microfinance institutions serve the farmer, the fisherman, the market vendor and the micro-entrepreneur, people that large commercial banks often find too costly to reach.These institutions are trusted locally, but most are held back by the same two problems. The first is technology that is either too expensive or too rigid for an institution of their size. The second is the network itself: branches, agents and field officers often work across islands, mountains and rural districts where connectivity is slow, patchy or missing for hours.This blog looks at both problems together, because in practice they are one decision. Choosing a core banking system is also choosing how well the institution will serve customers at the edge of the network. It also explains how M2P&#39;s Core Banking System (CBS) addresses both, drawing on what it has already delivered for community-focused partner banks.<h2>The Reality for Smaller Financial Institutions in Southeast Asia</h2>Small institutions carry a large share of everyday finance across the region, and regulators are pushing them to modernise.Indonesia had 1,345 rural banks (Bank Perekonomian Rakyat, or BPR) as of March 2025, according to the Financial Services Authority (OJK). OJK is driving consolidation and digitalisation across the sector. By December 2025, it had approved the merger of 130 BPRs and Sharia rural banks (BPRS) into 45, with another 226 in the process of becoming 79. OJK has also named digitalisation as one of the pillars of its rural bank development roadmap.The Philippines had 374 rural and cooperative banks as of mid-2025, according to Bangko Sentral ng Pilipinas (BSP) data. In 2026, the BSP set out rules for technical assistance to fund rural banks&#39; digitalisation for up to three years under its Rural Bank Strengthening Program. This includes support for moving to cloud-based core banking. At the same time, the BSP requires thrift, rural and cooperative banks that operate like digital banks to meet the same P1 billion minimum capital as digital banks.Similar roles are played by people&#39;s credit funds in Vietnam, microfinance institutions in Cambodia and Myanmar, and credit cooperatives across the region. Most of them face the same pressures:<ul class="list-bullet"><li
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 >Legacy or fragmented systems: Many still run on ageing software, standalone branch databases, or a mix of spreadsheets and accounting packages. Customer data is split across branches, and reconciliation is manual.</li><li
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 >High cost of ownership: Traditional cores are priced and built for large banks. Licence fees, hardware, upgrades and specialist staff consume a budget that small institutions cannot spare.</li><li
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 >Rigid product design: Group loans, savings schemes, share capital, Sharia-compliant products and seasonal agricultural credit often need custom development, which is slow and expensive.</li><li
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 >Consolidation and mergers: Institutions merging under regulatory pressure need to bring multiple portfolios onto one system quickly and accurately.</li><li
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 >Rising regulatory expectations: Supervisors expect accurate, timely reporting, stronger IT risk management and better controls.</li><li
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 >Pressure to go digital: Customers expect mobile access, instant transfers and agent services, which a closed legacy core struggles to support.</li></ul>The result is that many smaller institutions spend more on keeping their systems running than on serving customers. A modern core needs to reverse that balance.<h2>What an Affordable, Configurable Core Must Do for Smaller Institutions</h2>A core banking system for a rural bank, cooperative or microfinance institution should not be a scaled-down version of a large bank&#39;s system. It should be designed around how these institutions actually work. Six capabilities matter most.<h3>1. Pricing that scales with the institution</h3>Small institutions need costs that grow with their customer base and transaction volumes, not large upfront licence fees. Cloud and SaaS deployment removes much of the hardware and data centre spend, an approach regulators such as the BSP are now actively supporting for rural banks.<h3>2. Products configured, not coded</h3>The product catalogue of a cooperative or rural bank looks very different from a commercial bank&#39;s. It may include member share capital, patronage refunds, group loans with weekly repayments, salary-deduction loans, harvest-linked agricultural credit and Sharia-compliant financing. A modern core should let staff configure these products, profit and interest rules, fees and limits through parameters, without waiting months for custom development.<h3>3. A single, accurate view of every customer</h3>All accounts, loans, savings and transactions should sit in one centralised, real-time ledger. This ends duplicate customer records across branches, simplifies KYC and gives management a reliable picture of the portfolio at any time. It also makes consolidation far easier when institutions merge.<h3>4. Open APIs for digital channels and local rails</h3>Customers increasingly expect to transact through mobile apps, agents and instant payment rails. An API-first core lets an institution connect to mobile banking, cards, national payment systems and fintech partners without rebuilding its core each time.<h3>5. Compliance and controls built in</h3>Regulatory reports, audit trails, maker-checker approvals and fraud monitoring should be part of the core, not separate projects. This matters even more for small institutions with lean compliance and IT teams.<h3>6. Migration without disruption</h3>Moving years of customer data out of a legacy system, or merging several institutions&#39; portfolios, is the step most institutions fear. The right partner brings proven migration methods and the ability to run alongside existing systems where needed, instead of forcing a risky big-bang switch.<h2>Southeast Asia&#39;s Connectivity Gap and Why It Matters for Core Operations</h2>Southeast Asia has made rapid progress online. According to GSMA Intelligence, mobile internet penetration in the region rose by almost 20 percentage points in five years to reach 76% by the end of 2025. But the gains are uneven. Across developing Asia Pacific, people in rural areas are 25% less likely to use mobile internet than those in cities, held back by affordability, digital skills and safety concerns.Geography adds to the challenge. Archipelagos such as Indonesia and the Philippines spread communities across thousands of islands, while mountainous and remote inland areas in countries like Vietnam, Laos, Cambodia and Myanmar face similar gaps. This is exactly where rural banks, cooperatives and microfinance institutions operate. For their operations, the gap shows up in practical ways:<ul class="list-bullet"><li
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 >Branches lose their link to the core during outages, and tellers cannot post deposits or check balances.</li><li
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 >Field officers and agents collect repayments in villages, markets and fishing communities with weak signal, so transactions are recorded late or on paper.</li><li
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 >Customers cannot use self-service channels because they lack a smartphone, a data plan or the confidence to use them.</li><li
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 >Reconciliation becomes a daily burden, as late-posted transactions create mismatches between agent cash, customer balances and the ledger.</li></ul>For a small institution, each of these problems erodes trust, adds operational cost and limits growth beyond the branch network. A core banking strategy for the region has to start from the assumption that connectivity will not always be reliable at the edge.<h2>Designing Core Operations That Hold Up When Networks Don&#39;t</h2>There are two broad ways to keep banking running where connectivity is weak. Both have a place, but they lead to very different outcomes for customers and agents.<h3>The traditional approach: store-and-forward</h3>In the store-and-forward model, an agent&#39;s device captures a transaction offline, stores it locally and uploads the batch when it reaches a network. The core then processes the batch, usually at end of day.This model keeps service going, but it has real drawbacks:<ul class="list-bullet"><li
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 >Late failures: A withdrawal can be approved at the counter and declined hours later during batch processing, leading to disputes and lost trust.</li><li
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 >Delayed settlement: Agents&#39; funds stay tied up until reconciliation is complete, which strains their liquidity.</li><li
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 >Security exposure: Transaction data stored on a device can be lost or compromised.</li><li
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 >Reconciliation effort: Mismatches between offline records and the ledger have to be resolved manually.</li></ul><h3>The modern approach: real-time, agent-led banking</h3>The alternative is to keep the core always available and move the connectivity requirement to the point where it is easiest to solve. Instead of every customer needing a smartphone and data, a network of trained agents carries the connected device. Customers visit a local agent, authenticate, and complete transactions that are posted to the core instantly.This model has clear advantages for rural and cooperative networks across Southeast Asia:<ul class="list-bullet"><li
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 >No customer-side barriers: The customer needs no smartphone, data plan or app.</li><li
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 >Instant confirmation: Balances are checked and updated in real time, so there are no late declines.</li><li
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 >Faster agent settlement: Agent accounts are updated as each transaction is posted.</li><li
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 >Simpler reconciliation: One real-time ledger means far fewer mismatches.</li></ul>To make this work, the core must be available around the clock, handle high volumes of small transactions, and integrate easily with agent devices and authentication services. It should also give the institution full control over agent limits, commissions and monitoring.The practical design principle is simple: keep the core always on, and reduce how much connectivity each transaction needs at the edge. The fewer steps that depend on the customer&#39;s own device and network, the more reliable the service.<h2>How M2P Has Solved This for Its Partner Banks</h2>M2P has already put the real-time, agent-led model into production for community-focused banks in India, a market with many of the same challenges as Southeast Asia: large rural populations, uneven connectivity and customers who are new to formal banking.M2P&#39;s Core Banking System is a 24x7 platform built for continuous, real-time processing. Rather than relying on delayed offline batches, M2P helps partner banks reach remote customers through networks of agents who carry out authenticated transactions that are posted to the core instantly.<h3>A cooperative bank reaching remote customers</h3>A cooperative bank in India migrated from its legacy system to M2P&#39;s Core Banking System. Many of its customers are senior citizens and less tech-savvy people in remote areas.To reach them, M2P implemented a biometric, agent-led payment service integrated with the core. Customers visit a local agent, authenticate with a fingerprint, and complete their transaction in real time. They do not need a smartphone, data plan, card or PIN. On the day the service went live, the bank onboarded more than 500 agents from remote locations.<h3>A rural-focused cooperative bank</h3>Another cooperative bank, focused on lower-income customers in rural areas, also moved to M2P&#39;s Core Banking System. The bank had built its rural strategy around mobile field agents who collect deposits in villages, along with debit cards, a mobile app with instant transfers, and biometric ATM access. A modern core gave the bank a stronger foundation for extending these services.<h3>A small finance bank migrating at scale</h3>Scale matters as much as reach. A small finance bank in India replaced its core banking system with M2P&#39;s Core Banking System, migrating 111 branches and over 1.5 million customers.Want to know more about these deployments? <a href="https://m2pfintech.com/contact-us/" rel="noopener noreferrer" target="_blank">Reach out to us</a> for detailed case studies.<h3>What this means for Southeast Asia</h3>The lessons from these deployments apply directly to rural banks, cooperatives and microfinance institutions across Southeast Asia:<ul class="list-bullet"><li
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 >Shift the connectivity burden to the agent, so customers without smartphones or data can still bank.</li><li
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 >Keep the core always on and real-time, so every transaction is confirmed instantly and agent cash stays reconciled.</li><li
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 >Use authentication that customers already have, such as biometrics linked to national ID systems, instead of relying on cards and PINs.</li><li
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 >Scale the agent network quickly, without adding branches across islands or remote districts.</li></ul>In each market, the same model can be connected to local payment rails and identity systems, based on the country&#39;s infrastructure and regulations.<h2>Why M2P Core Banking Is a Strong Fit for Southeast Asia&#39;s Smaller Institutions</h2>M2P&#39;s Core Banking System brings together the affordability, configurability and always-on design that smaller institutions in the region need.<ul class="list-bullet"><li
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 >Modern architecture: Built on microservices, API-first and cloud-agnostic, so institutions can deploy in the environment that suits their regulator and budget.</li><li
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 >Low-code product configuration: New savings, deposit and loan products can be designed and launched quickly through configuration rather than custom development.</li><li
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 >Real-time, 24x7 processing: Every transaction is posted instantly to a single ledger, supporting branches, agents and digital channels together.</li><li
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 >Built for local rails: M2P&#39;s Core Banking System already supports the Philippines&#39; InstaPay for real-time transfers and PESONet for batch interbank payments. Its open APIs connect the core to mobile banking, cards, KYC services and fintech partners.</li><li
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 >Built-in risk controls: A pre-integrated Fraud Risk Management (FRM) engine supports secure operations from day one.</li><li
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 >Proven migrations: M2P has moved banks from legacy systems to its Core Banking System at scale, including a small finance bank with 111 branches. This experience is valuable for institutions consolidating multiple portfolios.</li><li
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 >More than a core: Institutions can add lending, microfinance, cards, payments and KYC capabilities from the same infrastructure partner as they grow.</li></ul><h3>A partner with a long-term regional focus</h3>M2P serves more than 200 banks, 300 lenders and 800 fintechs across over 30 markets. The company has named the Philippines and Indonesia among its key markets for international growth. Its US$100 million Series D round, led by Helios Investment Partners, was raised to support expansion across Africa and Southeast Asia.For a rural bank, cooperative or microfinance institution, this combination means a modern core designed for real-time, last-mile banking, backed by a partner committed to the region for the long term.<h2>Conclusion</h2>Southeast Asia&#39;s rural banks, cooperatives and microfinance institutions are central to financial inclusion, but many are held back by systems that cost too much, adapt too slowly and break down where the network is weak. With regulators in Indonesia and the Philippines actively pushing consolidation and digitalisation, the time to modernise is now.An affordable, configurable core lets smaller institutions launch the products their customers need without heavy custom development. A real-time, agent-led operating model lets them reach customers at the edge of the network without asking those customers to own a smartphone or pay for data.M2P has already delivered this model for community-focused partner banks, with real-time agent networks reaching remote customers from day one. With a modern, API-first Core Banking System that already supports local payment rails, M2P is a strong option for smaller institutions across Southeast Asia that are ready to modernise.Ready to modernise your core? <a href="https://m2pfintech.com/contact-us/" rel="noopener noreferrer" target="_blank">Contact M2P</a> to explore how M2P&#39;s Core Banking System can support your institution&#39;s growth across Southeast Asia.

Core Banking for Southeast Asia's Rural Banks, Cooperatives and Microfinance Institutions: Scalable, Configurable, and Built for Patchy Networks

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 Walk into most conversations about fraud and risk management, and you&#39;ll still hear the same vocabulary that&#39;s dominated the industry for two decades: chargebacks, card-not-present fraud, velocity checks, device fingerprinting. It&#39;s not wrong vocabulary — it&#39;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&#39;t disappeared, but in market after market, it&#39;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 &quot;fraud prevention&quot; needs to mean.<h1>Why Most FRM Vendors Are Still Fighting Yesterday&#39;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&#39;s replaced it doesn&#39;t fit the same mental model at all — and that&#39;s the problem.Authorized push payment (APP) fraud — where the victim is deceived into initiating the payment themselves — doesn&#39;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&#39;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&#39;s laundered out. A fraud system scoring each transaction in isolation — the dominant architecture for most of the last decade — simply can&#39;t see this pattern. You need network-level analysis: who&#39;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&#39;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&#39;t share the transaction-level signatures the model was built around.What this means for FRM strategy: the shift isn&#39;t &quot;add more rules.&quot; It&#39;s a structural one — from transaction-level scoring to behavioral and network-level detection. That means modeling the victim&#39;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&#39;t rebuilt around this shift are, quite literally, optimizing for a fraud problem that&#39;s shrinking while the one that&#39;s growing goes unaddressed.<h1>What Fraud Looks Like in an Agentic AI World</h1>Here&#39;s a scenario that sounds speculative but is arriving faster than most risk teams have planned for: a consumer&#39;s AI agent — booking travel, managing subscriptions, comparison-shopping and checking out on their behalf — initiates a payment. No human clicks &quot;buy.&quot; 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&#39;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&#39;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 &quot;user&quot; at checkout is an AI agent, several things change at once:<ul class="list-bullet"><li
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 >Behavioral biometrics stop working as a fraud signal. There&#39;s no human typing pattern or navigation behavior to analyze — an agent&#39;s &quot;behavior&quot; is deterministic, fast, and doesn&#39;t vary the way a human&#39;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
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 >Authentication needs a new model entirely. SCA&#39;s &quot;something you know, have, or are&quot; 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
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 >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&#39;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
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 >Liability becomes genuinely unclear. If an AI agent makes an unauthorized or ill-advised purchase, who&#39;s liable — the consumer who delegated authority, the platform that built the agent, or the merchant that accepted the transaction? None of today&#39;s card network rules or regional payment regulations have a clean answer yet.</li></ul>What this means for FRM strategy: risk teams don&#39;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 &quot;agent-initiated&quot; 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>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 &quot;customers.&quot; A prospective fraudster doesn&#39;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&#39;s phishing kit successfully bypasses a particular bank&#39;s authentication flow, that exploit gets shared across the platform&#39;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 &quot;known fraud patterns&quot; to &quot;criminal infrastructure fingerprints.&quot; 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>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&#39;s payment security — including TLS connections, digital signatures, and card network authentication protocols. This isn&#39;t the same thing as &quot;quantum computers will let criminals hack your bank account next year.&quot; 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 &quot;cryptographically relevant&quot; quantum computer somewhere in the 2030s at the earliest — with meaningful uncertainty even in that estimate.The &quot;harvest now, decrypt later&quot; 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&#39;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 &quot;drop everything&quot; emergency, but it is a &quot;start the multi-year planning now&quot; 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>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&#39;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&#39;s models isn&#39;t a strategy for tomorrow&#39;s threats.The institutions that will manage this well aren&#39;t the ones with the single most accurate fraud model for today&#39;s known fraud patterns. They&#39;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>See What a Forward-Built FRM System Looks Like</h1><a href="https://m2pfintech.com/frm-aml/?utm_source=blog&amp;utm_medium=sept&amp;utm_campaign=ai-scam" rel="noopener noreferrer" target="_blank">M2P&#39;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&#39;re a risk leader trying to figure out whether your fraud stack is built for today&#39;s threats or still tuned to yesterday&#39;s, <a href="https://m2pfintech.com/contact-us/?utm_source=blog&amp;utm_medium=sept&amp;utm_campaign=ai-scam" rel="noopener noreferrer" target="_blank">reach out to M2P</a> to explore how our FRM system can help you get ahead of where fraud is actually headed.

Beyond Card Fraud: Why Today's FRM Systems Need to Fight Tomorrow's War

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 Sam was tired of getting promotional emails in his inbox. He kept deleting them every day, but the next day the cycle repeated, and the inbox was full. He had no idea why he kept getting these emails, even though he doesn’t remember subscribing to them. Further, they didn’t stop even after choosing the unsubscribing option. Sounds familiar, doesn’t it?Well, we get these emails whenever we sign-up for a new online service or website. We would come across these familiar messages but never take heed of them as our primary intent would be signing up for their services. Below are some lines that are permanent fixtures when signing up online:<ul class="list-bullet"><li
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 >I would like to receive marketing emails on discounts, promotions, offers, and other information about the product</li><li
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 >We may share your data with our third-party partners to bring you personalized offers</li><li
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 >I agree to receive monthly newsletters, blogs, or the latest stories</li><li
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 >Understand our usage of cookie data and how we partner with analytics service providers and social media</li></ul>Now, if you are wondering why they are a permanent fixture, it’s because they are mandatory by the GDPR guidelines.GDPR is the General Data Protection Regulation that came into practice to protect the individual’s privacy online. In simple terms, it is a list of data privacy rules and guidelines to safeguard customer data.It aims to provide consumers significant control over their data. At the same time, it serves as a valuable framework for organizations when processing customer data like name, address, photos, IP address, genetic or <a href="https://m2pfintech.com/blog/explore-the-future-of-payment-authentication-biometric/">biometric information</a>.<h2 style="text-align: justify;">The Beginning</h2>The European Union (EU) implemented the General Data Protection Regulation (GDPR) on 25th May 2018. The regulation attempted to limit data exploitation and to gain people’s trust in the digital economy.If a Non-EU area engages in transactions or data exchanges with an EU region, the former needs to follow GDPR. To elaborate, all the Indian companies working in IT, international e-commerce, and outsourcing also fall under GDPR since they deal with data processing. These regulations can even change the ways these companies do business with customers. Organizations should be aware of the GDPR’s laws, including the increased costs of managing and protecting customer information.For a successful implementation of data security measures, organizations must reflect the interest the customer has about his/her data in their sophisticated data collection methods.<h2 style="text-align: justify;">When can a company record a customer’s information?</h2><ul class="list-bullet"><li
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 >The customer consents to store information for as long as it is necessary</li><li
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 >The customer can access and rectify the information at any given time</li><li
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 >Companies store information on a commonly used format like CSV</li><li
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 >They explain to customers how their data is processed and used, for how long, and by whom</li><li
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 >They can delete customer information when the user thinks it is not necessary</li><li
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 >They inform customers of data breaches within 72 hours of the breach detection</li><li
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 >They notify customers if their data is vulnerable to a data breach</li></ul><h2 style="text-align: justify;">Why the need for this regulation?</h2>Imagine knowing that all your personal information can be found with a single click. All your details can be traced, documented, and used for a third party’s gain. For instance, all the information you share online isn’t entirely safe, and the internet still hosts some confidential information.So, GDPR offers organizations a clear pathway of dealing with sensitive customer information while at the same time protecting the customer’s right to privacy.<a href="https://m2pfintech.com/newsletter/">Subscribe</a> to our newsletter and get the latest fintech news, views, and insights, directly to your inbox.Follow us on <a href="https://www.linkedin.com/company/m2pfintech/mycompany/">LinkedIn</a> and <a href="https://twitter.com/m2pfintech">Twitter</a> for insightful fintech tales curated for curious minds like you.

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