How AI Supports Fraud Detection

𝐇𝐨𝐰 𝐀𝐈 𝐦𝐢𝐭𝐢𝐠𝐚𝐭𝐞𝐬 𝐟𝐫𝐚𝐮𝐝 𝐢𝐧 𝐀𝐜𝐜𝐨𝐮𝐧𝐭-𝐭𝐨-𝐀𝐜𝐜𝐨𝐮𝐧𝐭 𝐏𝐚𝐲𝐦𝐞𝐧𝐭𝐬 by Visa👇 — 𝐓𝐡𝐞 𝐏𝐫𝐨𝐛𝐥𝐞𝐦 𝐢𝐧 𝐀2𝐀 𝐏𝐚𝐲𝐦𝐞𝐧𝐭𝐬: ► Account-to-Account (A2A) payments are rapidly growing, with a forecasted 161% growth between 2024 and 2028. ► The fundamental characteristics of Real-Time Payments (RTP), such as speed, 24/7 availability, irrevocability, and lack of network visibility, contribute to the increasing fraud risks. ► Fraud is evolving with the growth of A2A payments, making it crucial for financial institutions to implement real-time fraud prevention strategies. — 𝐖𝐡𝐲 𝐢𝐬 𝐀𝐈 𝐂𝐫𝐢𝐭𝐢𝐜𝐚𝐥𝐥𝐲 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐢𝐧 𝐅𝐫𝐚𝐮𝐝 𝐏𝐫𝐞𝐯𝐞𝐧𝐭𝐢𝐨𝐧? ► 𝐒𝐩𝐞𝐞𝐝 𝐚𝐧𝐝 𝐀𝐜𝐜𝐮𝐫𝐚𝐜𝐲: AI enables real-time fraud detection and prevention, essential for instant payment transactions that are completed within 10 seconds. ► 𝐏𝐚𝐭𝐭𝐞𝐫𝐧 𝐑𝐞𝐜𝐨𝐠𝐧𝐢𝐭𝐢𝐨𝐧: AI can recognize patterns and detect irregularities, linked to mule accounts or changed geolocation. ► 𝐀𝐝𝐚𝐩𝐭𝐢𝐯𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: AI models adjust to new fraud trends in real-time, unlike traditional rules-based systems that require post-loss analysis. ► 𝐑𝐞𝐝𝐮𝐜𝐞𝐝 𝐅𝐚𝐥𝐬𝐞 𝐏𝐨𝐬𝐢𝐭𝐢𝐯𝐞𝐬: AI-enhanced systems provide more accurate fraud detection, reducing the need for manual reviews and minimizing false positives. ► 𝐍𝐞𝐭𝐰𝐨𝐫𝐤-𝐋𝐞𝐯𝐞𝐥 𝐕𝐢𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐲: AI leverages a multi-financial institution (FI) view, enabling a comprehensive view of fraud across payment networks, which is crucial for detecting cross-network fraud schemes. — 𝐑𝐮𝐥𝐞𝐬-𝐁𝐚𝐬𝐞𝐝 vs. 𝐀𝐈-𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐝 𝐒𝐲𝐬𝐭𝐞𝐦𝐬: 𝐑𝐮𝐥𝐞𝐬-𝐁𝐚𝐬𝐞𝐝 𝐒𝐲𝐬𝐭𝐞𝐦: 1️⃣ Transaction Initiated 2️⃣ Massive Volume of Transactions: High volume of transactions are flagged for manual review due to basic rule triggers. 3️⃣ Manual Review: Transactions are manually reviewed, leading to delays and operational inefficiencies. 4️⃣ Transaction Assessed: Risk is evaluated based on pre-set rules. 5️⃣ Transaction Authorized: If no rule is violated, the payment is authorized. 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬: High false positives, time-consuming manual reviews, and delays in payment processing. 🆚 𝐀𝐈-𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐝 𝐒𝐲𝐬𝐭𝐞𝐦: 1️⃣ Transaction Initiated 2️⃣ Curated Volume of Transactions: AI intelligently filters transactions, reducing the volume that requires review. 3️⃣ AI-Assisted Review: Transactions are reviewed with AI input, providing real-time risk assessment. 4️⃣ Data & Model Assessment: AI evaluates transactions using data patterns and predictive models. 5️⃣ Transaction Authorized: If deemed low-risk, the payment is instantly authorized. 𝐁𝐞𝐧𝐞𝐟𝐢𝐭𝐬: Reduced false positives, real-time risk assessment, operational efficiency, and improved customer experience. — Source: Visa — ► Sign up to 𝐓𝐡𝐞 𝐏𝐚𝐲𝐦𝐞𝐧𝐭𝐬 𝐁𝐫𝐞𝐰𝐬 ☕: https://lnkd.in/g5cDhnjC ► Connecting the dots in payments... and Marcel van Oost

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

AI Agents in Banking: The Truth No One Talks About Over the last 12 months, I’ve built and deployed 50+ custom AI agents across tier-1 banks and financial institutions. And here’s the truth—what actually works in banking is very different from what most people are selling online. Forget the flashy “$50K/month with no-code AI agents” headlines. In reality, banks aren’t buying dreams. They’re investing in precision, reliability, and measurable ROI—with strict compliance guardrails in place. The most successful AI agents I’ve built don’t try to do everything. Instead, they focus on solving one high-impact problem exceptionally well, such as: 🔹 KYC automation – Extracting and verifying documents, cutting manual review time by 60% 🔹 Fraud detection – Real-time transaction monitoring that reduces false positives by 40% 🔹 Customer service AI – Handling up to 70% of routine inquiries, boosting CSAT and reducing ops cost These agents aren’t built for show. They’re built for scale. They integrate cleanly with legacy systems, follow strict audit trails, and pass scrutiny from compliance and legal teams. Most importantly, they drive outcomes that matter—time saved, risk reduced, and customer satisfaction improved. In the world of banking, flashy doesn’t cut it. Real innovation is quiet, consistent, and measurable. If you’re working on AI for financial services, focus less on what’s trending—and more on what truly moves the needle. #AI #BankingInnovation #AIagents #Fintech #Compliance #RiskManagement #KYC #Automation #RegTech #FraudDetection #CustomerExperience

The most important question in financial crime is no longer "Is this transaction risky?" but "What is the complete story and how do I block it from happening again?". We're witnessing a fundamental shift from spotting anomalies to understanding narratives, a leap as significant as the move from batch processing to real-time to deep context. This evolution from shallow data to deep context completely transforms how an investigation and operation analysts find answers. To see what this means in practice, let's follow a single fraud investigation through three technology eras of the recent past: 1️⃣ Era 1 (Big Data, Batch Era): We catch the fraudulent transaction 12 hours too late. The money is already gone. 2️⃣ Era 2 (The Streaming, Real-Time Scoring Era): We stop the transaction instantly. But our analyst is buried in false positives, and the ghost identity behind the fraud is free to try again elsewhere. 3️⃣ Era 3 (The Agentic Copilot, Deep context Era): We not only stop the bad actors, but we also uncover the entire story: the compromised SSN, the mule address, the burner phone, and dismantle the fraud network behind it. With high accuracy. The leap to Era 3 isn't just an upgrade; it's a new way of seeing. The game-changer in financial crime is no longer speed or volume. It's deep context, the ability to understand the full narrative, something only a team of AI Agents working with your investigators can deliver. I broke down this entire investigation, in my latest article. See the full story in the comments below! 👇 #FraudPrevention #AML #Fintech #AI #AgenticAI #Compliance #RiskManagement #FravityAI