Anti-Fraud Software Solutions

Here's how Stripe detects frauds with a 99.9% accuracy in 100 milliseconds (that too by checking over 1000 parameters for one transaction) Fraud detection in online payments isn’t just about stopping bad transactions it’s about doing it fast, at scale, and without blocking legitimate users. Stripe’s fraud prevention system, Radar, evaluates 1,000+ signals within 100 milliseconds to make decisions. Here’s how it works and why it’s so effective: 1. ML Models That Learn and Scale Stripe started with simple ML models (logistic regression) but quickly scaled to hybrid architectures combining: –XGBoost for memorization (catching known patterns). –Deep Neural Networks (DNNs) for generalization (handling unseen patterns). –Key Problem: XGBoost couldn’t scale or integrate modern ML techniques like transfer learning and embeddings. –The Solution: Stripe moved to a multi-branch DNN-only architecture inspired by ResNeXt. This setup allowed it to memorize patterns while staying scalable. It reduced training times by 85%, enabling multiple experiments in a single day instead of overnight runs. 2. Learning From Real Fraud Patterns Radar doesn’t just rely on static rules, it learns from data across Stripe’s network. –Engineers analyze fraud attacks in detail, e.g., patterns of disposable emails or repeated card testing. –Features like IP clustering and velocity checks were added to detect suspicious activity. –Fraud insights are shared across the network, so lessons learned from one business protect others automatically. Example: Analyzing IP patterns helped detect high-volume attacks where fraudsters used multiple stolen cards from the same source. 3. Scaling With More Data, Not Just Smarter Models Stripe realized that more training data could unlock better performance, similar to modern LLMs like GPT models. It tested scaling datasets by 10x and 100x. Result? Performance kept improving, confirming that larger datasets and faster training cycles work better than complex rules alone. Key Insight: Bigger datasets help uncover rare fraud cases, even if they occur in only 0.1% of transactions. 4. Explaining Fraud Decisions Clearly Fraud systems often act like black boxes, leaving businesses guessing why a payment failed. Stripe built Risk Insights to provide clear explanations: –Shows features contributing to fraud scores like mismatched billing and shipping addresses. –Displays maps and transaction histories for visual context. –Enables custom rules to fine-tune fraud checks for specific business needs. Result: Businesses trust Radar’s decisions because they can see why a payment was flagged. 5. Constant Adaptation to Stay Ahead Fraud patterns evolve, so Stripe built Radar to adapt in real time: Uses transfer learning and multi-task learning to generalize better. Incorporates insights from the dark web and emerging fraud tactics. Continuously retrains models without disrupting performance.

80% of Financial Frauds Are Now Digital—Are We Prepared? The number of digital financial frauds skyrocketed in FY24, growing more than four times year-on-year. The message is clear: the battlefield of financial fraud has gone digital, and so must our defences. Relying on single-layered security measures is like locking your front door but leaving your windows wide open. Fraudsters are becoming more sophisticated, leveraging phishing, malware, and identity theft to exploit vulnerabilities across the digital ecosystem. Solution? 𝐑𝐨𝐛𝐮𝐬𝐭 𝐦𝐞𝐚𝐬𝐮𝐫𝐞𝐬 𝐭𝐡𝐚𝐭 𝐰𝐚𝐭𝐜𝐡, 𝐥𝐞𝐚𝐫𝐧, 𝐚𝐧𝐝 𝐚𝐜𝐭 𝐢𝐧 𝐫𝐞𝐚𝐥-𝐭𝐢𝐦𝐞. Here’s what a multi-layered framework looks like in action: ✅ 𝐁𝐞𝐡𝐚𝐯𝐢𝐨𝐫𝐚𝐥 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬: AI monitors real-time user behaviour—location changes, sudden high-value transactions—and triggers step-up authentication if something feels off. ✅ 𝐁𝐢𝐨𝐦𝐞𝐭𝐫𝐢𝐜 𝐀𝐮𝐭𝐡𝐞𝐧𝐭𝐢𝐜𝐚𝐭𝐢𝐨𝐧: Fingerprints and facial recognition provide nearly impossible-to-spoof ID checks, shutting down common phishing and credential attacks. ✅ 𝐃𝐲𝐧𝐚𝐦𝐢𝐜 𝐑𝐢𝐬𝐤 𝐒𝐜𝐨𝐫𝐢𝐧𝐠: Every transaction gets a risk profile. Unusual device types, odd transaction sizes, and abnormal frequencies get flagged, prompting further checks. ✅ 𝐄𝐧𝐝-𝐭𝐨-𝐄𝐧𝐝 𝐄𝐧𝐜𝐫𝐲𝐩𝐭𝐢𝐨𝐧: Even if criminals intercept data in transit, encryption ensures it’s just scrambled noise, not usable information. ✅ 𝐒𝐞𝐜𝐮𝐫𝐞 𝐀𝐏𝐈𝐬: As businesses integrate with partners, secure APIs validate incoming requests and ward off unauthorized intrusions at the integration points. 𝘙𝘦𝘮𝘦𝘮𝘣𝘦𝘳: Digital fraud isn’t going away—it’s evolving. The only way to stay ahead is to think like a fraudster while building like a strategist. How do you safeguard your digital financial operations? Share your approach in the comments below. #DigitalFraud #FinancialFraud #Cybersecurity

In Financial Services, detecting and handling fraudulent transactions is mission critical. Top institutions invest millions into AI/ML solutions to improve automated fraud detection. But there’s still a common gap: the workflows for investigating ambiguous cases often remain stuck in spreadsheets and ticketing systems—slowing review times and frustrating customers. With Databricks, organizations can build sophisticated models that automatically classify most transactions as fraudulent or legitimate. However, there's always a critical grey area of transactions that fall between these extremes—requiring hours or days of manual verification, leading to mounting operational costs and frustrated customers. Our Solutions team quickly prototyped an integrated approach based on a common Databricks reference architecture, using Superblocks for the operational workflows. Here’s the breakdown: 🔍 The Intelligence Layer (Databricks): - An isolation forest model identifies unusual patterns - An XGBoost classifier provides fraud probability scores - Models run automatically through MLflow pipelines - Predictions are stored efficiently in Delta tables 💡 The Action Layer (Superblocks):  Our application transforms these ML insights into an actionable workflow where analysts can: - Review a queue of flagged transactions with full context - Make informed decisions on potential fraud cases - Create and document investigations comprehensively - Feed decisions back to Databricks with full data governance to improve model accuracy This approach unlocks a key operational workflow and improves the model through RLHF: - Analysts can swiftly handle this tricky grey area, drastically cutting resolution times and improving customer satisfaction. - Every review action becomes fuel for even better fraud detection, creating a virtuous cycle of learning and improvement.

🚀 𝐁𝐨𝐨𝐬𝐭𝐢𝐧𝐠 𝐅𝐫𝐚𝐮𝐝 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐀𝐈 🚀 This marks the first use case of the 50 use cases in a series where we explore how AI is transforming business. Starting with Fraud detection as I have spent a decent amount of time in this field during the initial phase of my career 😁 In today’s digital world, fraudsters are evolving faster than ever, creating significant challenges for businesses. Traditional fraud detection methods like rule-based systems, statistical models, and human analysis are increasingly ineffective. High false positives, limited adaptability, and difficulty in scaling make these methods fall short. That’s where AI comes in, completely changing fraud detection with machine learning (ML), deep learning (DL), and natural language processing (NLP). AI offers real-time detection with greater accuracy, 𝒔𝒍𝒂𝒔𝒉𝒊𝒏𝒈 𝒇𝒂𝒍𝒔𝒆 𝒑𝒐𝒔𝒊𝒕𝒊𝒗𝒆𝒔 𝒃𝒚 85% 𝒂𝒏𝒅 𝒓𝒆𝒅𝒖𝒄𝒊𝒏𝒈 𝒅𝒆𝒕𝒆𝒄𝒕𝒊𝒐𝒏 𝒕𝒊𝒎𝒆 𝒃𝒚 30%. Its adaptability and scalability are essential for handling today’s complex fraud tactics. ❗ 𝐊𝐞𝐲 𝐀𝐈 𝐌𝐞𝐭𝐡𝐨𝐝𝐬 𝐢𝐧 𝐅𝐫𝐚𝐮𝐝 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧: 1️⃣ Supervised & Unsupervised Learning: Trains systems to detect both known and new fraud patterns. 2️⃣ Neural Networks, CNNs, & RNNs: These deep learning models excel at recognizing complex fraud tactics from vast data. CNNs are great for structured data, while RNNs shine in time-sensitive analysis like transaction histories. 3️⃣ Text Analytics & Sentiment Analysis: Especially useful in industries like e-commerce, analyzing text data can expose signs of fraud. 4️⃣ Regression & Time-Series Forecasting: Helps predict fraudulent activity based on historical data. ❗ 𝐓𝐞𝐜𝐡 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 Deploying AI-based solutions requires a solid tech stack, often built with 𝑷𝒚𝒕𝒉𝒐𝒏, 𝑹, 𝒐𝒓 𝑱𝒂𝒗𝒂, and leveraging frameworks like 𝑻𝒆𝒏𝒔𝒐𝒓𝑭𝒍𝒐𝒘 𝒂𝒏𝒅 𝑷𝒚𝑻𝒐𝒓𝒄𝒉. Cloud platforms like 𝑨𝑾𝑺, 𝑮𝒐𝒐𝒈𝒍𝒆 𝑪𝒍𝒐𝒖𝒅, 𝒂𝒏𝒅 𝑨𝒛𝒖𝒓𝒆 provide the scalable infrastructure necessary to support these solutions. Financially, businesses should be prepared for an initial investment, depending on system complexity. Ongoing maintenance typically costs 10% to 20% of the initial investment. ❗ 𝐓𝐡𝐞 𝐂𝐨𝐬𝐭 𝐨𝐟 𝐈𝐧𝐚𝐜𝐭𝐢𝐨𝐧 Failing to implement AI solutions can lead to significant losses—𝒐𝒏 𝒂𝒗𝒆𝒓𝒂𝒈𝒆, 5% 𝒐𝒇 𝒂 𝒄𝒐𝒎𝒑𝒂𝒏𝒚’𝒔 𝒓𝒆𝒗𝒆𝒏𝒖𝒆 𝒊𝒔 𝒍𝒐𝒔𝒕 𝒕𝒐 𝒇𝒓𝒂𝒖𝒅 𝒂𝒏𝒏𝒖𝒂𝒍𝒍𝒚. Beyond that, reputational damage and regulatory penalties can have long-lasting effects. Stay tuned for more! PS: Vaidyanath R., I would love to hear more from you on this topic hashtag #AI #FraudDetection #MachineLearning #AIForGood #TechSolutions #BusinessGrowth #AIUseCases

Every payments executive I've spoken to in the past six months has asked about one rising risk: fraud. With the rapid growth of e-commerce and proliferation of local payment methods, emerging market consumers and businesses face increasingly complex threats. Fraudsters leverage cultural nuances (such as faith-based scams) and exploit the instant nature of local payment methods and stablecoin rails. To move money safely, businesses need solutions that are contextually aware, AI-powered, and adapt to emerging fraud trends. Last month, I had the privilege of spending time with Thalia Pillay and Carla Wilby and it's incredible to see the work they are doing at Orca Fraud. In just a year and a half, they've built feature parity with some of the biggest transaction monitoring companies in the world, with deep localisation for emerging markets. The platform offers real-time monitoring of mobile wallets, crypto and stablecoin rails, cards and bank accounts, with localised rulesets, anomaly detection, and intelligence. This is a team you'll want to watch. Read more in their most recent case study here: https://lnkd.in/dVshq3w2

Long before the AI copilot hype, SEON was built on a simple idea: Let fraud teams move fast, stay in control, and win. From day one, we combined layers of transparent and adaptive machine learning with an advanced no-code rules engine, because companies shouldn’t have to choose between: 1️⃣ Hiring a full in-house team to manually build rules 2️⃣ Blindly trusting a ML black-box score they can’t explain Both come with tradeoffs: cost, control, transparency. That’s why over 2 years ago, we launched a hybrid approach, bringing the best of both worlds. SEON customers go live fast (think days, not weeks), using industry best practices plus tailored last-mile config with our team. Then AI kicks in: • Detecting anomalies • Suggesting patterns • Surfacing deployable rules your fraud team can test, tweak, and launch The result? Less firefighting, more time for fraud teams to focus on strategic, revenue-generating work. And real fraud stopped. 💡 Just in February: • 427 SEON customers tested and deployed an AI-suggested rule • 70.3% of all fraud checks (~295 million) on our platform were influenced by an AI-suggested fraud rule • $1.27B in fraud attempts stopped, in real time While others are adding flashy LLM copilots to make their UX look smarter, we’re focused on making your fraud decisions better. 👉 Want to see how? https://lnkd.in/dFxSCP-x Or check this out: https://lnkd.in/dGrPm8_s

Fraud behaviors are changing faster than merchants can keep up with. ⏩ To level out the playing field, merchants are now shifting towards using incremental learning for #fraud detection. Let's explore what incremental learning is & how it's helping combat fraud. --- Before exploring incremental learning, it's a good idea to quickly touch on the traditional approaches to fraud detection. Fraud detection involves teams analyzing #payments transaction data, identifying fraudulent activity, and defining patterns to: 💸 Minimize losses due to fraud 🛑 Prevent legitimate transactions from being flagged (false positives) 🤝 Maintain customer trust --- To make the process more efficient, fraud detection really starts with rules-based systems. These systems rely on predefined rules that flag transactions based on specific criteria known to correlate with fraud. Downsides of just using rules-based systems? By needing to update rules every time a new pattern emerges, it creates a maintenance bottleneck. As transaction volumes get higher, detecting fraud becomes more complex too. --- The next layer of defense involves working with traditional machine learning models. Machine learning (ML) provides a way for fraud teams to quickly adapt their fraud detection systems amidst rapidly evolving fraud trends. By inputting large historical datasets into a machine learning model for training, fraud detection systems are able to detect complex fraud patterns that may have otherwise gone unnoticed. ML models typically work in tandem with rules-based systems to ensure precision. Downsides of traditional ML? New behaviors/trends mean the model has to be retrained... Which is a slow process that can take weeks or months. --- So what about incremental learning? Incremental learning is a type of machine learning model that involves "continuous learning" by using real-time data. The model only needs to be trained once with historical data. As fraud trends change, the model needs to be trained solely on that new behavior. To add salt to the fraudsters' wounds, the model adapts using real-time data from transactions to incrementally learn new behaviors as they emerge. Incremental learning meets the need for real-time adaptability. --- Fraudsters don't seem to be letting their foot off the gas pedal, so it's important for any #merchant to consult with a fraud management provider (such as ACI Worldwide) to figure out where your fraud KPIs stand against industry standards. For merchants that deal with high numbers of transactions, geographies, and #consumers, incremental learning is the most effective, future-proof solution to combat rapid, constantly changing fraud. Source: Cleber Martins at ACI Worldwide wrote a fantastic article explaining how incremental learning works and where merchants benefit. #fintech #machinelearning

Let’s say you just got budget for a new fraud tool. You need to decide: Do you build a custom fraud tool, or buy an existing one? Here’s how each option looks: 𝗕𝘂𝗶𝗹𝗱 𝗮 𝗰𝘂𝘀𝘁𝗼𝗺 𝘁𝗼𝗼𝗹: A custom tool fits your business perfectly. It integrates well with your systems and handles your specific fraud challenges. But it’s expensive, takes time to build, and requires constant maintenance. 𝗕𝘂𝘆 𝗮 𝗽𝗿𝗲-𝗯𝘂𝗶𝗹𝘁 𝘁𝗼𝗼𝗹: A pre-built solution is quicker to implement. It covers common fraud scenarios and comes with support. But it may not fully fit your needs, and you might need multiple tools to cover the entire customer journey. Both have their benefits, but here’s the thing: You don’t have to choose just one. 𝗛𝗲𝗿𝗲’𝘀 𝗵𝗼𝘄 𝘁𝗼 𝘀𝘁𝗿𝗶𝗸𝗲 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗯𝗮𝗹𝗮𝗻𝗰𝗲: 𝗧𝗵𝗶𝗻𝗸 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗷𝗼𝘂𝗿𝗻𝗲𝘆: Fraud happens at different points along the way. You might need several tools to cover each stage, from sign-up to transaction. Combining custom and pre-built tools is often the best. 𝗖𝗼𝗻𝘀𝗶𝗱𝗲𝗿 𝘆𝗼𝘂𝗿 𝗯𝘂𝗱𝗴𝗲𝘁 𝗮𝗻𝗱 𝘁𝗶𝗺𝗲𝗹𝗶𝗻𝗲: Custom tools take longer but give you exactly what you need. Pre-built tools provide faster coverage, but you may need more than one to fill all the gaps. 𝗪𝗵𝗲𝗿𝗲 𝗜’𝘃𝗲 𝗹𝗮𝗻𝗱𝗲𝗱: I used to be 100% in the “buy” camp. Unless you were turning the tool into a product to sell. It seemed like the most efficient way to manage fraud. But now, I’ve shifted. The best approach is a mix of both. Using both custom and pre-built tools helps you handle specific fraud risks while staying flexible and scalable. It’s not “build vs. buy" anymore.

#FraudPrevention is one of the clearest examples of why real-time #DataStreaming is mission-critical. Companies like PayPal, Capital One, ING Bank, Grab, and Kakao Games use #ApacheKafka and #ApacheFlink to detect and stop fraud before it impacts customers. From preventing payment fraud to stopping gaming abuse in milliseconds, these companies process billions of events, correlate real-time and historical data, and act instantly. Whether it’s #banking transactions, mobility services, or #gaming telemetry, the pattern is the same: - Kafka ingests and distributes events at scale - Flink enriches, correlates, and scores events in real time - Action is taken immediately to prevent losses and protect customers with #AI and #MachineLearning models In a world where waiting even a few minutes can mean losing millions, real-time #StreamProcessing is no longer optional for fraud detection. How is your organization leveraging Kafka and Flink for real-time risk detection or prevention? https://lnkd.in/eftjdUFB

Don’t let the fraud be the villain in your success story.... As fraud tactics are evolving and becoming more sophisticated, two major risks are posed for businesses: Revenue drain: Online fraud is projected to cost businesses $40 billion by 2027 (ClearSale). Trust erosion: 83% of consumers won’t return to an e-commerce after experiencing fraud (ClearSale). However, many businesses aren’t just combating these threats—they’re also facing the burden of costly integrations with anti-fraud engines and other security measures, resulting in high operational expenses to effectively mitigate risks. To protect both your business' reputation and profitability, leverage a payment orchestrator like DEUNA. With just one integration, access unique capabilities to.... PROTECT sensitive user data through tokenization technology, which replaces this information with tokens during transactions—keeping it shielded from potential attacks or data breaches. DEUNA complies with PCI DSS standards, ensuring robust security and data protection in every transaction. DETECT threats with fraud prevention engines, which you can enable in just a few clicks. These tools don’t just catch fraud before it strikes—they recognize patterns, anticipate emerging tactics, and keep your revenue and reputation safe. ROUTE different types of transactions to gateways with more advanced detection algorithms. For example, certain gateways provide a deeper understanding of customer behavior in specific markets or regions, to identify suspicious patterns with greater accuracy. VALIDATE suspicious transactions with extra layers of authentication (like 3D-Secure). This not only shields your business from potential threats but also prevents false positives—verifying the legitimacy of certain suspicious operations—and transfers the liability of any eventual fraud to the issuing bank, which absorbs the chargeback cost. Let me know if you have encountered these problems below...