Interac Fights Fraud with AI and Human Expertise

In the fast-moving world of digital finance, trends can change overnight. One week, the focus is on AI-powered fraud detection. The next, it’s a new wave

Finovox Expands into the UK to Combat Rising Document Fraud ⠀ 📎 Finovox, a European leader in document fraud detection, has announced its expansion into the United Kingdom, building on its established presence in France, Luxembourg, Belgium, and Spain. This move comes amid a sharp rise in fraud across Europe, driven by the rapid digitalisation of processes and the accelerating use of artificial intelligence. ⠀ 📎 According to a European study conducted by Finovox in partnership with Selvitys, the UK ranks third among European countries most affected, with 11% of respondents admitting to having falsified a document. The UK market stands out for its advanced digitalisation of fraudulent practices, with nearly a quarter of respondents using generative AI tools. To counter this, Finovox is deploying its AI-based technology to support UK organisations in securing their procedures by analyzing the authenticity of documents in a matter of seconds. ⠀ 💬 “Document fraud now represents a significant cost for UK businesses and, more broadly, for the economy. As fraud becomes increasingly sophisticated and facilitated by artificial intelligence, organisations still lack the right tools to respond effectively. At Finovox, our ambition is to protect companies and organisations against this growing risk by providing technology capable of detecting fraud quickly and reliably.”  – Marc De Beaucorps, Co-Founder and CEO, Finovox ⠀ 📌 Learn more: https://lnkd.in/dpQkTc57 ⠀ #FinancialIT #Finovox #Fintech #FraudPrevention #AI #Cybersecurity #DocumentFraud

Experian, a global data analytics company, has gone live with Transaction Forensics, a new AI-driven solution developed alongside Resistant AI - Fraud Detection AI to tackle fraud and financial crime in real time across UK financial services. Read here: https://lnkd.in/en48sVfj Paul Weathersby Martin Rehak

Fraud detection is not control. It’s evidence that control already failed. — Most fintech systems still follow the same pattern: transaction executes → fraud is detected → damage is contained We’ve normalized this. Built entire teams, tools, and KPIs around it. But step back for a second: Why was the transaction even possible? — If your system needs to decide after execution whether something is valid, then invalid states are part of your design. And that’s where the real risk lives. — The strongest financial systems don’t rely on detection. They constrain: → what transactions can be constructed → what identities can be represented → what flows can be composed Before anything touches execution. — Because once money moves, you’re no longer enforcing rules. You’re negotiating recovery. — This is where most fraud strategies break: They optimize speed of detection, instead of reducing the size of the possible attack surface. — The shift is simple, but uncomfortable: From: fraud detection systems To: fraud impossibility architectures — Not: “how fast can we catch it?” But: “why could it exist at all?”

𝐅𝐫𝐚𝐮𝐝 𝐢𝐧 𝐩𝐚𝐲𝐦𝐞𝐧𝐭𝐬 𝐢𝐬𝐧’𝐭 𝐣𝐮𝐬𝐭 𝐚 𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐢𝐬𝐬𝐮𝐞 — 𝐢𝐭’𝐬 𝐚 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐫𝐢𝐬𝐤 🔒💸 With AI and machine learning, fraud detection is shifting from rigid rules to real-time, predictive prevention. 🤖📊 𝐈𝐧 𝐨𝐮𝐫 𝐥𝐚𝐭𝐞𝐬𝐭 𝐛𝐥𝐨𝐠, 𝐰𝐞 𝐞𝐱𝐩𝐥𝐚𝐢𝐧 𝐡𝐨𝐰 𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐭 𝐟𝐫𝐚𝐮𝐝 𝐝𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 𝐜𝐚𝐧: 🔷Stop suspicious transactions before they happen⚡ 🔷Reduce false declines and save revenue 📉 🔷Keep customer trust without friction 🛡️ 𝐑𝐞𝐚𝐝 𝐭𝐡𝐞 𝐟𝐮𝐥𝐥 𝐛𝐥𝐨𝐠 → https://lnkd.in/gkeUQgY4 #FraudDetection #AIinPayments #MachineLearning #DigitalPayments #PaymentSecurity #Fintech #RiskManagement #ToucanPayments

11 Best Financial Fraud Detection Software (2026 Guide) Financial fraud detection software has evolved from a defensive cost center into a strategic growth lever for modern fintechs. By leveraging real-time telemetry and advanced machine learning models, this software analyzes high-velocity transaction streams and behavioral patterns to neutralize threats—such as synthetic identity fraud and account takeovers—before they impact the bottom line. Current industry benchmarks indicate that leading financial fraud detection software can reduce false positives by up to 75% through automated rules-based engines and agentic AI integration....

Fraud is no longer a bank problem. It’s a network problem. The IMF recently made a clear point : 👉 Banks need to share data to keep up with AI-driven fraud. And the numbers explain why: - Global fraud losses estimated $480B+ - US fraud losses already $10B+ and rising - AI is accelerating scams (deepfakes, synthetic identities, automation) Today’s challenge: - Fraud patterns span multiple institutions - Attackers operate across the ecosystem - But defenses are still siloed What this means Fraud prevention has to evolve from: - Institution-level → Network-aware - Batch checks → Real-time decisions - Isolated signals → Shared intelligence The hard part Data sharing runs into: - Privacy & regulation - Trust between banks - Data standardization Fraud is scaling like a network. Defenses still operate like silos. That gap is where the real risk - and opportunity - is. 📎 Reading : https://lnkd.in/e65xiv_i https://lnkd.in/eRH96Bm7 #Payments #Fraud #Cybersecurity #AI #EnterpriseArchitecture #Fintech #BankingTechnology #Risk #FraudPrevention #TechStrategy

The "Cat and Mouse" game of financial crime just got a lot faster. The announcement from Commonwealth Bank on Fraud AI agent showcases that the next era of security isn't just about better detection but it’s about "Autonomous Architecture." We are moving from AI that flags problems to AI that builds the solutions. The bank isn't just using AI to find scams but they have deployed an agent that autonomously generates the rules needed to stop new threats. It analyzes the context and writes the defensive code. By turning scam detection into an agentic workflow, the bank has updated 75% of its fraud rules via AI. The result? A 20% drop in fraud losses in just six months. Also, this isn't just 'set and forget' but every machine-proposed rule is reviewed by a human analyst. The AI provides the scale and speed, while the human provides the accountability. #AgenticAI #FinTech #CommBank #FraudPrevention #CyberSecurity #FutureOfFinance #BankingInnovation2026 #DigitalTransformation #AIGovernance #MachineLearning https://lnkd.in/dPs_Nk8G

📰 Great to see the Financial Times for spotlighting fraud, using data and intelligence from our latest Fraudscape report 📊 Fraud has never been more widespread and continues to evolve rapidly, particularly through the use of AI-enabled technology. This article reinforces why collective action and cross-sector collaboration matters, and how industry is fighting back. 🔒 As our Cifas CEO Mike Haley further underlines, sharing data and intelligence is critical to protecting people and organisations from harm. 🤝 Here’s more from journalist, Chris Newlands: https://lnkd.in/eTHJd3RX #Fraud #FraudAwareness #FraudPrevention #FraudProtection #FraudAlert #FraudEmergency #FraudData #DataSharing

Nobody talks about the real reason banks are losing the fraud war — even with record AI investment. It's not weak detection models. It's not slow regulators. It's not under-funded security teams. It's that for the first time in banking history, the attacker and the defender are running the same tech stack. Here's what actually happens: Step 1: Bank deploys AI to detect synthetic identities. ↓ Step 2: Fraudsters use the same models to generate them. ↓ Step 3: Every detection upgrade trains the next attack. ↓ Step 4: The bank's "AI advantage" lasts about 90 days. The fix is surprisingly simple: Instead of competing on model sophistication: → Compete on data the fraudster cannot access — behavioural patterns, device fingerprints, cross-institution signal. Instead of bolting a standalone "AI fraud platform" onto your stack: → Embed detection natively across the entire payment lifecycle — onboarding, authorisation, switching, post-event — so no single model is the gate. Instead of leaving fraud rules to quarterly data-science cycles: → Put business-owned rules, pre-tested on real transaction history, alongside ML scoring — so your fraud team reacts in days, not quarters. I've sat in enough fraud reviews to know the pattern: the metric that's improving is almost never the vector that's about to hurt you. Card fraud trending down means nothing if synthetic identity onboarding or authorised push payment scams are the next wave — and they usually are. Strip away the AI budget, the vendor logos and the dashboards, and the question that actually matters is simple: what does your bank know about its customers that the fraudster doesn't? What's the real reason your bank is still losing ground on fraud?