How AI Can Reduce Fraud Losses

In my conversations with policymakers, I often hear concerns of how AI is making scams worse. Truth is, we don’t talk enough about how AI is used in combating scams. Often, the scale of the threat is hard to grasp as it's not about individual bad actors, but organized, sophisticated abuse - and that’s where AI can make a difference in taking the fight to scammers. 🔍 Search: Our AI-powered scam detection systems helped catch 20-times the number of scammy pages. For example, new protections decreased scams impersonating official sites by more than 70%. 🖼️ Ads: Thanks to 50+ LLM enhancements, AI significantly improved fraud detection at account setup. AI was key in combating a new challenge: AI-generated impersonation scams, contributing to a 90% drop in reports. 📍Maps: Our machine learning models are trained to find patterns that indicate fraudulent behaviors like a sudden surge in ratings. In 2024, we caught 12 million attempts from fraudsters trying to create entirely fake listings. I’m excited to see AI taking center stage in our fight against fraud and look forward to shifting the conversation in this space, and keeping our users safe online. 🛡️

Mastercard's recent integration of GenAI into its Fraud platform, Decision Intelligence Pro, has caught my attention. The results are impressive and shows the potential of “GenAI in Advanced Business Applications”. As someone who follows AI advancements in Fraud across the FSI industry, this news is genuinely exciting. The transformative capabilities of GenAI in fortifying consumer protection against evolving financial fraud threats showcase the potential impact of this integration for improving the robustness of AI models detecting fraud. The financial services sector faces an escalating threat from fraud, including evolving cyber threats that pose significant challenges. A recent study by Juniper Research forecasts global cumulative merchant losses exceeding $343 billion due to online payment fraud between 2023 and 2027. Mastercard's groundbreaking approach to fraud prevention with GenAI integrated Decision Intelligence Pro is revolutionary. - Processing a staggering 143 billion transactions annually, DI Pro conducts real-time scrutiny of an unprecedented one trillion data points, enabling rapid fraud detection in just 50 milliseconds. - This innovation results in an average 20% increase in fraud detection rates, reaching up to 300% improvement in specific instances. As we consider strategic imperatives for AI advancement in fraud, this news suggests what future AI models must prioritize: - Rapid analysis of vast datasets in real-time, maintain agility to counter emerging fraudulent tactics effectively, and assess relationships between entities in a transaction. - By adopting a proactive approach, AI systems should anticipate and deflect potential fraudulent events, evolving and learning from emerging threats to bolster security. - Addressing the challenge of false positives by evolving AI models capable of accurately distinguishing legitimate transactions from fraudulent ones is vital to enhancing overall security accuracy. - Committing to continuous innovation embracing AI is essential to maintaining a secure and trustworthy financial ecosystem. #artificialintelligence #technology #innovation

Can AI Outpace Fraudsters in Real-Time? A payment platform detects and blocks fraudulent transactions before they happen, all in milliseconds. Here’s how one fintech did it: AI analyzed user behavior to spot anything unusual. Machine learning models evolved daily, adapting to new fraud tactics. Risk scores in real-time flagged suspicious payments instantly. The result? Fraud cut by 60% without slowing down legitimate users. In a world of instant payments, AI is the secret weapon to stay secure. How are you protecting your platform?

Why most payment teams are solving the wrong problem. They're optimising to catch more fraud. That sounds right. But the real revenue leak isn't the fraud that slips through. It's the good customers you're turning away. Mastercard estimates false declines cost merchants around $118 billion a year. Actual card fraud losses? Roughly $9 billion. That's a 13x gap, which most risk teams still aren't accounting for. The metric they watch is fraud caught. However, the metric that matters is customers wrongly rejected. So how do you solve that problem? Whenever I speak with Acquirers and PSPs, this conversation often leads to the classic Buy, Rent, or Build discussion. And while there are great off-the-shelf solutions to buy or rent, I wouldn't be able to call myself a Data Scientist (who has built several of the ML-based Fraud Engines) if I didn't lean towards the latter. This is where NVIDIA agrees, and to make it easier, they have released their AI Blueprint for fraud detection, helping companies springboard into building their own solutions. Using Graph Neural Networks, NVIDIA's model doesn't just ask whether a transaction looks fraudulent. It understands the full behavioural context, spending patterns, device history, merchant relationships, and transaction sequences to distinguish a genuine customer from a bad actor with far greater precision. The difference is significant. Traditional rule-based fraud systems protect against fraud by blocking anything uncertain. GNNs protect revenue by being accurate enough to approve the uncertain ones that are actually fine. NVIDIA's 2026 State of AI in Financial Services survey shows 34% of institutions are already tackling fraud detection through AI. The early movers aren't just seeing lower fraud rates. They're seeing higher authorisation rates at the same time. These two things aren't supposed to improve at the same time. That's the point. Adyen demonstrated this publicly at NVIDIA GTC 2025. By applying AI across their full payment funnel, they achieved a 22% increase in fraud recall and a 46% reduction in auth rate loss. Better fraud detection improved authorisation. The tradeoff most teams assume is real turned out to be a data problem, not an inevitability. If your fraud model's primary KPI doesn't include false positive rate, you're measuring half the equation. Any system optimised purely to block fraud will always sacrifice legitimate revenue to feel safe. The smarter question to ask your risk team this week: how many good customers did we turn away last quarter? That number is usually bigger than anyone wants to see.

Fraudsters are moving at breakneck speed with AI. And only AI can effectively fight AI. The numbers back this up. The FTC reported fraud losses jumped 25% to $12.5B in 2024. But the real problem isn't the scale, it's the fundamental mismatch in approaches. This reminds me of 2010 at LinkedIn. Our data processing pipelines worked fine when we had a few million profiles. But as we scaled to hundreds of millions of active users and real-time product functionality, those same data systems started breaking. We couldn't just optimize the existing data architecture. That's why we built Kafka. Fraud detection is hitting the same inflection point. Rule-based systems designed for human fraudsters that are checking velocity limits and flagging geographic anomalies can't keep up with AI that can generate thousands of synthetic identities per second or create deepfake documents that bypass traditional verification methods. You need systems that can analyze patterns at the same speed attacks are evolving. At Oscilar, that means real-time AI-powered risk decisions with full transparency. → Streaming data keeps signals fresh, governed #ML and #GenAI co-pilot speed up model building and explainability. → #AgenticAI powers specialized agents that learn your standard operating procedures, evaluate different risk dimensions, share insights, and operate within a governed framework, with human oversight where needed. The result: faster decisions, fewer false positives, and clear audit trails.

If you think Stripe Radar is enough, you're protecting against 30% of fraud vectors. Modern fraud isn't just stolen cards. It's account takeovers, synthetic identities, bot attacks, friendly fraud, and money laundering, each requiring a different defense layer. That's why fraud prevention has split into a full stack of specialized tools. Card fraud is still massive, but its share of total fraud losses keeps shrinking. In many verticals, transactional fraud is no longer the biggest threat. Sift is a good illustration. Often perceived as a generic fraud tool, it actually processes signals far beyond payments: - ~70% of its detections relate to non-payment events (logins, signups, content abuse) - ~1 trillion events analyzed per year - ~34,000 sites and apps protected globally The same pattern exists across the ecosystem: Forter and Riskified for e-commerce, Sardine for fintech and crypto, Persona for identity, ComplyAdvantage for AML, and Arkose Labs for bot mitigation. Fraud Prevention Tools reflect the diversity of attack vectors: - End-to-End Fraud Platforms: Sift, Forter, Riskified, Signifyd, Sardine, SEON - Device Intelligence & Behavioral Biometrics: Fingerprint, Incognia, BioCatch, ThreatMetrix, Castle - Identity Verification & KYC: Persona, Alloy, Sumsub, Socure, Onfido, Veriff AML & Transaction Monitoring: ComplyAdvantage, Hawk AI, Unit21, Feedzai, Featurespace - Bot Protection & Account Takeover: Arkose Labs, HUMAN, DataDome, Cloudflare, Kasada Capital Management - Chargeback & Dispute Management: justt, Chargeflow, Ethoca, Verifi Inc., Kount, an Equifax Company Attacker behavior explains this shift. AI-generated synthetic identities, credential stuffing at scale, and organized fraud rings have made single-layer defenses obsolete. By 2026, fraud losses break down roughly as: - ~45% from account takeovers and identity fraud - ~30% from transactional and card fraud - ~25% from chargebacks and friendly fraud Real-time decisioning, shared fraud networks, and AI-driven risk scoring continue to accelerate this trend. Fraud prevention is no longer a feature. It's becoming a critical infrastructure layer of every digital business. PS: I post about payments with Suby, stablecoins & the reality of building a payment startup, every week. Follow for more!

Do You Know Why AI and Enterprise Architecture Are Inseparable in 2025? (9 Core Reasons)   In the modern enterprise, Artificial Intelligence (AI) is the engine of innovation, but Enterprise Architecture (EA) is the chassis, steering, and rulebook that allows it to race ahead safely and effectively. In 2025, their fusion has evolved from a competitive advantage to a core operational necessity. EA provides the crucial scaffolding that allows AI — especially Generative AI — to be scaled responsibly, efficiently, and in alignment with emerging global regulations.   Here are the 9 core reasons why they are inseparable:   1. Eliminating Data Silos for AI to Work Problem: Silos in legacy systems (e.g., CRM, ERP) prevent AI from accessing a unified, accurate view of enterprise data. Solution: EA designs and governs modern data mesh architectures, which provide a unified governance layer over distributed data domains, enabling secure and seamless data access for AI without creating monolithic, hard-to-manage data lakes. Example: -Procter & Gamble used EA principles to transition from 50+ legacy systems to a governed data mesh on Azure, enabling AI-driven demand forecasting. -Result: 15% reduction in stockouts.   2. Reducing Unplanned Downtime with Predictive Maintenance Problem: Unexpected equipment failures cost manufacturers millions in downtime and lost productivity. Solution: EA creates the integrated platform that connects IoT sensors, historical data, and AI models for real-time failure prediction and prescriptive maintenance. Example: -Siemens uses its Industrial Edge platform and AI to predict failures in manufacturing equipment, scheduling maintenance before breakdowns occur. -Result: 20% fewer breakdowns, saving $50M/year.   3. Cutting Fraud Losses in Financial Services Problem: Manual and rules-based fraud detection is slow, inefficient, and misses sophisticated, evolving patterns. Solution: EA embeds AI/ML models directly into the core transaction processing systems, enabling real-time anomaly detection and transaction blocking. Example: -HSBC deployed AI on its EA backbone to flag suspicious transactions as they occur. -Result: 35% faster fraud detection, saving $300M annually.   4. Automating Repetitive Processes to Free Up Teams Problem: Employees waste significant time on manual, repetitive tasks (e.g., invoice processing, IT service requests). Solution: EA standardizes and maps processes, enabling Intelligent Automation (e.g., RPA, NLP, Computer Vision) to take over these tasks end-to-end. Example: -Coca-Cola used EA and AI to automate 80% of its invoice processing. -Result: 10,000+ hours/year saved for finance teams, allowing them to focus on strategic analysis.   Continue in 1st, 2nd and 3rd Comments     Transform Partner – Your Strategic Champion for Digital Transformation   Image Source: Salesforce

Been losing sleep lately thinking about AI in fraud prevention... Not because I'm worried about it replacing us. Because I can't stop thinking about what's possible. After months of diving deep into AI and fraud operations, I've discovered something that changed my mind: The more automated our systems become, the more valuable human judgment becomes. Strange paradox, right? Here's what's keeping me up at night → Imagine an AI decisioning copilot that: • Finds hidden patterns across 10,000+ cases in seconds • Writes investigation summaries automatically • Suggests next actions based on what actually worked • Routes cases to exactly the right expert But that's just the surface. It starts to come together with root cause analysis: • AI connecting invisible dots across months of data • Fraud patterns clustering themselves • Attacks visualized in real-time • Risk signals surfacing BEFORE the spike hits Think about that for a second. What if your team could get ahead of fraud instead of chasing it? Now here's where it gets really interesting to me... All of this feeds into risk decisioning at scale: • Rules that evolve with new patterns • Policies that adjust without breaking • Risk scores you can actually explain • Systems that get smarter every single day From spotting patterns, to understanding root causes, to making decisions at scale - the whole fraud prevention game is about to change. Think about what I just broke down... When your AI copilot handles the pattern detection... And root cause analysis happens automatically... Your team can focus on what matters most: making smarter risk decisions. I've seen enough "AI solutions" to be skeptical. But this feels different. And here's what I really think... We're not building AI to replace fraud teams. We're building it to reveal their true value. The companies that understand this will win. Not because they have better AI – everyone will have that soon enough. They'll win because they understand where human intelligence matters most. What if the greatest value of AI isn't in what it can do, but in what it lets humans do? That's the question that keeps me up at night. ...I guess we'll see

In Nigeria’s banking sector, AI adoption is accelerating with tangible results. Seven major banks – including Zenith, Fidelity, and others – have deployed AI chatbots to serve customers 24/7, handling inquiries and transactions seamlessly. AI-driven fraud detection is also paying off: In Q1 2023, banks lost about ₦472 million to fraud, but by Q3 2024, even as attempted fraud skyrocketed to ₦115.9 billion, actual losses dropped to just ₦10.1 billion – a testament to smarter, proactive security measures. Moreover, AI is unlocking credit access. By analyzing alternative data (mobile transactions, utility payments, etc.), AI-powered credit scoring is helping to bridge Nigeria’s ₦24.2 trillion SME financing gap, enabling instant micro-loans for individuals without traditional credit histories. These snapshots illustrate how AI is transforming Nigerian banking, enhancing customer service, security, and financial inclusion all at once.

Insurance fraud is no longer just a cost problem. It’s becoming a strategic credibility problem. Deloitte’s Financial Services Industry Predictions 2025 makes a compelling case that fraud—embedded in roughly 1 in 10 P&C claims—is eroding trust across the insurance value chain, with costs ultimately borne by consumers through higher premiums. What stands out is not just the scale of the issue, but the implication: pricing alone can’t fix fraud anymore. As customer attrition rises and tolerance for premium increases falls, insurers are being pushed toward a different response—one rooted in intelligence, not inflation. Deloitte points to AI‑powered, multimodal fraud detection as a turning point. By integrating text, images, audio, video, geospatial data, and IoT signals across the claims lifecycle, insurers can move beyond reactive, rules‑based controls toward real‑time, predictive fraud prevention. The strategic upside is significant: Soft fraud, which represents ~60% of incidents and is notoriously difficult to prove, becomes more detectable when patterns are analyzed across multiple data sources. At scale, Deloitte estimates AI‑driven approaches could unlock US$80B–US$160B in savings by 2032, while reducing false positives and investigator burnout. But the most important insight may be this: AI is not replacing human judgment—it’s reshaping it. The insurers most likely to win are those that pair advanced analytics with strong governance, regulatory alignment, and skilled investigative talent, turning fraud prevention into a source of long‑term resilience rather than short‑term cost control. The fight against insurance fraud is quickly becoming a test of how effectively firms can blend technology, trust, and human insight. https://lnkd.in/eNvS3E97