AI-Driven Insights For Fraud Risk Assessment

You’re hired as a GRC Analyst at a fast-growing fintech company that just integrated AI-powered fraud detection. The AI flags transactions as “suspicious,” but customers start complaining that their accounts are being unfairly locked. Regulators begin investigating for potential bias and unfair decision-making. How you would tackle this? 1. Assess AI Bias Risks • Start by reviewing how the AI model makes decisions. Does it disproportionately flag certain demographics or behaviors? • Check historical false positive rates—how often has the AI mistakenly flagged legitimate transactions? • Work with data science teams to audit the training data. Was it diverse and representative, or could it have inherited biases? 2. Ensure Compliance with Regulations • Look at GDPR, CPRA, and the EU AI Act—these all have requirements for fairness, transparency, and explainability in AI models. • Review internal policies to see if the company already has AI ethics guidelines in place. If not, this may be a gap that needs urgent attention. • Prepare for potential regulatory inquiries by documenting how decisions are made and if customers were given clear explanations when their transactions were flagged. 3. Improve AI Transparency & Governance • Require “explainability” features—customers should be able to understand why their transaction was flagged. • Implement human-in-the-loop review for high-risk decisions to prevent automatic account freezes. • Set up regular fairness audits on the AI system to monitor its impact and make necessary adjustments. AI can improve security, but without proper governance, it can create more problems than it solves. If you’re working towards #GRC, understanding AI-related risks will make you stand out.

Just had a call with a customer whose risk analysts are firmly stuck in the pre-AI world. Here's what they're doing manually that will be automated: (Disclaimer: I don't blame this team at all, and there are so many like them making the jump to AI workflows. We're here to help!) Their current (manual) merchant verification process: 1. Manually searching business names across multiple sources 🔍 2. Cross-checking Secretary of State registrations 📑 3. Comparing website domain creation dates with "in business since" claims 📅 4. Reviewing Google/Yelp business status and ratings ⭐ 5. Scanning for adverse media mentions 📰 6. Checking physical location via Google Maps 🏢 7. Verifying social media presence (Instagram/YouTube) 📱 8. Looking for suspicious website elements (stock images, template text) 🚩 9. Verifying the payout bank account with voided checks 🏦 10. Calculating potential credit exposure for risk assessment 💰 Every analyst does this, and I don't blame them. The problem is, it's time-consuming, inconsistent across analysts and teams, and doesn't scale 👎 What excites me is how AI agents 🧠 can transform this workflow: - Automated data collection: Connect to multiple sources simultaneously to gather all relevant data in seconds ⚡ - Pattern recognition: Flag discrepancies that matter (like a business claiming 20 years of history with a 6-month-old domain) 🧩 - Contextual intelligence: Understand industry norms (like towing companies typically having lower ratings) 🔄 - Risk summarization: Provide the "net net" with key findings and specific risk factors, not raw data dumps 📊 - Guided recommendations: "Pause payouts," "Request additional documentation," or "Approve with monitoring" based on risk patterns and the company's risk appetite 📋 - Continuous learning: Improve detection by incorporating feedback from confirmed fraud cases 📈 Transitions like this are difficult once. The outcome is a senior analyst team member for everyone on your risk team that never gets tired, and always delivers insights. Leaving the manual processes behind forever. Trust me, it's worth it 🚀

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

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