AI Solutions For Reducing False Positives In Fraud Detection

🚨 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐏𝐚𝐲𝐦𝐞𝐧𝐭𝐬 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 𝐢𝐧 𝐌𝐨𝐭𝐢𝐨𝐧 — 𝐅𝐫𝐚𝐮𝐝 𝐏𝐫𝐞𝐯𝐞𝐧𝐭𝐢𝐨𝐧 by DEUNA Traditional, static fraud rules often fall short — tightening controls so much that they block good customers, or leaving gaps that allow fraud to slip through. Agentic intelligence changes this paradigm. By leveraging historic transaction data and strategic signals (PSPs, payment methods, geographies, behavioral trends), it dynamically recommends risk controls tailored to each scenario. — 𝐃𝐞𝐞𝐩 𝐃𝐚𝐭𝐚 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 Historic transaction patterns and behavioral signals are integrated with granular specifics like BIN, card franchise, and geography. This allows the system to distinguish between legitimate customers and potential fraud with precision. → The Walt Disney Company leverages historical subscription behavior data to differentiate genuine recurring payments from suspicious account takeovers, reducing false declines. — 𝐋𝐨𝐰 𝐑𝐢𝐬𝐤 𝐯𝐬 𝐇𝐢𝐠𝐡 𝐑𝐢𝐬𝐤 𝐓𝐫𝐚𝐧𝐬𝐚𝐜𝐭𝐢𝐨𝐧𝐬 Low-risk transactions flow seamlessly with minimal friction, boosting conversion and improving customer satisfaction. High-risk transactions are dynamically routed through targeted fraud prevention layers — activating the most relevant PSPs and antifraud providers at the right moment. → Uber adapts fraud checks by geography, applying stronger measures in regions with high fraud incidence while keeping repeat riders’ payments frictionless. — 𝐏𝐫𝐨𝐯𝐢𝐝𝐞𝐫 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐅𝐫𝐚𝐮𝐝 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 Risk scoring is factored into provider and PSP selection to balance approval rates, cost efficiency, and security. → Airbnb leverages intelligence to dynamically adjust fraud controls by market and traveler profile — applying stronger authentication in high-risk regions or for first-time guests, while allowing frictionless payments for trusted, repeat customers. — 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐞𝐝 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐚𝐭 𝐒𝐜𝐚𝐥𝐞 Fraud tools are embedded directly into the orchestration layer, enabling smarter allocation: fraud detection where it is most impactful, and seamless flows where customers have already proven trustworthy. → Worldline merchants leverage adaptive authentication, activating 3DS selectively when intelligence identifies elevated risk — enabling smoother experiences for low-risk customers. — The Result → Intelligent Growth with Protection ✅ Higher approval rates without compromising safety ✅ Smarter allocation of fraud tools where they matter most ✅ Frictionless checkout experiences for trusted customers — This is proactive fraud prevention in motion — moving beyond rigid rules into an era of intelligent orchestration, where every payment decision optimizes both security and customer satisfaction at scale. — Source: DEUNA ► Subscribe to The Payments Brews: https://lnkd.in/g5cDhnjC ► Connecting the dots in payments... | Marcel van Oost

I don’t say this lightly.  Our new release of the Sigma V4 Fraud Engine is GAME CHANGING for companies losing millions of dollars annually from digital account opening fraud.  I’m talking to the banks, fintechs, marketplaces, governments, gaming companies…  Pay attention. Here’s the performance data on Sigma Identity V4: 🔹 Capturing up to 99% of identity fraud in the riskiest 5% of users, compared to just 37% by competitors at the same review rate 🔹 Reducing false positives by more than 40% over Socure's Sigma ID v3 🔹 Delivering an average 20x ROI for customer's from increased revenue/false positive reduction, fraud loss reduction, and lower manual reviews How did we do it? 10 years of making huge investments across 3 key areas: 1️⃣ Digital Signal creates a robust digital fingerprint of each customer, inclusive of devices and their OS, browser languages, geolocations, and relationship to multiple identities. 2️⃣ Entity Profiler allows us to see an identity from its inception in the digital economy, assessing every historical transactional, digital and relational data point to make up-to-the-second risk decisions. 3️⃣ Integrated Anomaly Detection is a new model that assesses identity behavioral pattern differences at the company, industry, and financial network level and allows us to identify thousands of risk-indicating variables. Let’s use an analogy.  Think of fighting identity fraud like playing a giant game of 'Spot the Difference' where most of the images are identical copies of a normal, everyday scene. The fraudulent activity is like one subtle, but crucial difference hidden in one of these images. It's hard to find because it blends in so well. However, with the right tools, this one different detail lights up or gets highlighted, making it easy to spot. This saves the fraud analysts, who are like players in this game, a lot of time and effort as they don't have to scrutinize every single part of the picture to find the anomaly #fraud #ai #banks #fintech

The old way to fight fraud was to ask: “Does this look like normal user behavior?” The problem: normal is relative. Some people shop at 3am every night. Some bounce between 15 countries in a month. Run those patterns against a demographic model, and you DROWN in false positives. We found the breakthrough was shifting from consortium models to identity models. From “people like you” → to “you vs. you.” Not: “Is this normal for customers like Josh?” But: “Is this normal for Josh?” That one shift changes everything: - Fewer false positives. - Stronger security. Fraud isn’t about normal. It’s about YOUR normal. That approach reduces false positives while tightening security. We stop asking whether a transaction looks normal in general and start asking whether it looks normal for this person.

Too many fraud solutions focus just on account opening. But risk evolves across the full user journey. Here's how we build the full picture at Sardine for dynamic scoring 👇 👉 When a user signs up, we create a baseline score based on identity, device, email, behavior signals 👉 As they transact, we update the score dynamically based on activity like login patterns, transaction details, behavior changes 👉 We build a holistic profile combining telco, email, device, merchant and more data into their risk score 👉 Machine learning models continuously monitor and flag anomalies to the baseline 👉 Granular data + models train on user's unique activity = precise risk scoring as they grow with your product Unlike legacy fraud tools, we don't just screen applicants. We provide ongoing monitoring across onboarding, transactions, account changes and more. This full picture reduces false positives and keeps fraud low across the user lifecycle.