The Role of AI in Fraud Resolution

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

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

🕵🏽♀️+🤖= 💰 🛑Artificial Intelligence helps feds stop $1 billion in fraud. Law enforcement has a new tool in their financial crime fighting toolbox: AI. But not the kind you may be thinking about. Instead of relying on generative AI, the likes of ChatGPT or Gemini, authorities at U.S. Department of the Treasury are relying upon machine learning, the subset of #ai that excels at analyzing vast amounts of data and assisting investigators in making decisions based on what is learned. According to a new report by CNN, AI helped Treasury to sift through massive amounts of data and recover $1 billion worth of check fraud in fiscal year 2024. The Treasury Department delivers about 1.4 billion payments valued at nearly $7 trillion to 100 million people. It’s responsible for sending out everything from Social Security and Medicaid payments to federal worker paychecks, tax refunds and stimulus checks. This makes the Treasury a prime target for fraudsters who are using AI to commit these crimes. Treasury officials cite that “𝒂 𝒉𝒖𝒎𝒂𝒏 𝒊𝒔 𝒂𝒍𝒘𝒂𝒚𝒔 𝒊𝒏 𝒕𝒉𝒆 𝒍𝒐𝒐𝒑,” which is a must for any AI fraud and money laundering detection and investigative software solution to be effective. Global law enforcement and financial crime investigators can utilize AI powered tools like FinAware, to manage, organize, and analyze large amounts of financial data in a fraction of the time. These tools are leading to faster identification of assets and recoveries of fraudulently stolen money. AI powered investigative tools allow supervisors to reallocate resources to the investigations, while eliminating hours, days, and weeks of tedious data management. Prosecutors benefit from non-generative AI tools by being able to quickly identify evidence and build jury appealing graphs and charts. And it’s not only bank statements, FinAware is able to read, organize and analyze massive amounts of off-chain #cryptocurrency exchange data, including coin swaps and trading counterparties. 👀Want to see what AI looks like to a financial crime investigator? 📺https://lnkd.in/ejaehtih ❓ Do you believe law enforcement can keep up with the AI race with fraudsters, scammers and money launderers? https://lnkd.in/eq6GEViz

I wrote a piece recently about how I think of AI: "Adapt Now or Fall Behind". The core of it is that fraudsters are leveraging AI really well already. This is leading to an unprecedented increase in scams, making it difficult for most to differentiate between what is real or not. At Unit21, we’ve already seen data showing that 40% of transactions that were blocked were due to scams 🤯 We need to lean into AI to fight back. The way businesses use AI will determine whether we win this war. AI has countless applications, from asking ChatGPT about data trends to deploying AI agents for Level 1 fraud/AML reviews or entity research. At Unit21, we focused on the risk-reward question: Where are the lowest risk and highest reward? We realized that 80% of the work in a fraud or AML alert is just gathering information, not decision-making. So, we prioritized optimizing that process. It’s explainable, keeps humans in the loop for the final decision, and significantly reduces manual work by 80% with minimal risk. The key is to start with the lowest-risk, highest-reward tasks and scale up over time. As an industry, AI adoption must also align with regulatory approval. The goal isn’t to have AI make the final decision—that’s unacceptable. We have to leverage AI to automate processes with full explainability. If your current technology doesn’t offer AI-driven solutions, it’s time to make an exit plan. Thrilled to be leading the charge on AI. We need to lean in. We must WIN this fight against fraud 💪 #ai #winning #fraudfighters #complianceheroes