How to Build an Anti-Fraud System

If my boss asked me to "assess our risk surface area and fraud priorities", this is how I would get it done by 5PM tomorrow. Step by step process. 1 - Pull our last 90 days of fraud data. Not just the obvious stuff like chargeback rates, but the full spread: login attempts, account creation patterns, payment declines... everything. Why 90 days? Because fraudsters love to exploit seasonal patterns, and we need that context. 2 - Map out every single entry point where money moves. I'm talking checkout flows, refund processes, loyalty point redemptions... even those "small" marketing promotion codes everyone forgets about. (Fun fact: I once found a six-figure exposure in a forgotten legacy gift card system) 3 - Time for some real talk with our front-line teams. Customer service reps, payment ops folks, even the engineering team that handles our API integrations. These people see the weird edge cases before they show up in our dashboards. 4 - Create a heat map scoring each entry point on three factors: → Financial exposure (how much could we lose?) → Attack complexity (how hard is it to exploit?) → Detection capability (can we even see it happening?) 5 - Cross-reference our current fraud rules and models against this heat map. Brutal honesty required here – where are our blind spots? Which high-risk areas are we treating like low-risk ones? 6 - Pull transaction data for our top 10 riskiest areas and run scenario analysis. If fraud rates doubled tomorrow, what would break first? (It's usually not what leadership thinks) 7 - Document our current resource allocation vs. risk levels. Are we spending 80% of our time on 20% of our risk? Been there, fixed that. 8 - Draft a prioritized roadmap based on: → Quick wins (high impact, low effort) → Critical gaps (high risk, low coverage) → Strategic investments (future-proofing our defenses) 9 - Prepare three scenarios for leadership: → Minimum viable protection → Balanced approach → Fort Knox mode Because let's be real, budget conversations need options. 10 - Package it all up with clear metrics and KPIs for each priority area. Nothing gets funded without numbers to back it up. ps... Make it visual. Leadership loves a good heat map, and it makes complex risk assessments digestible. Trust me on this one

This article highlights a St. Louis federal court indicted 14 North Korean nationals for allegedly using false identities to secure remote IT jobs at U.S. companies and nonprofits. Working through DPRK-controlled firms in China and Russia, the suspects are accused of violating U.S. sanctions and committing crimes such as wire fraud, money laundering, and identity theft. Their actions involved masking their true nationalities and locations to gain unauthorized access and financial benefits. To prevent similar schemes from affecting you businesses, we recommend a multi-layered approach to security, recruitment, and compliance practices. Below are key measures: 1. Enhanced Recruitment and Background Verification - Identity Verification: Implement strict verification procedures, including checking legal identification and performing background and reference checks. Geolocation Monitoring: Use tools to verify candidates’ actual geographic locations. Require in-person interviews for critical roles. - Portfolio Validation: Request verifiable references and cross-check submitted credentials or work samples with previous employers. - Deepfake Detection Tools: Analyze video interviews for signs of deepfake manipulation, such as unnatural facial movements, mismatched audio-visual syncing, or artifacts in the video. - Vendor Assessments: Conduct due diligence on contractors, especially in IT services, to ensure they comply with sanctions and security requirements. 2. Cybersecurity and Fraud Prevention - Access Control: Limit access to sensitive data and systems based on job roles and implement zero-trust security principles. - Network Monitoring: Monitor for suspicious activity, such as access from IPs associated with VPNs or high-risk countries. - Two-Factor Authentication (2FA): Enforce 2FA for all employee accounts to secure logins and prevent unauthorized access. - Device Management: Require company-issued devices with endpoint protection for remote work to prevent external control. - AI and Behavioral Analytics: Monitor employee behavior for anomalies such as unusual working hours, repeated access to restricted data, or large data downloads. 3. Employee Training and Incident Response - Cybersecurity Awareness: Regularly train employees on recognizing phishing, social engineering, and fraud attempts, using simulations to enhance awareness of emerging threats like deepfakes. - Incident Management and Reporting: Develop a clear plan to handle cybersecurity or fraud incidents, including internal investigations and containment protocols. - Cross-Functional Drills and Communication: Conduct company-wide simulations to test response plans and promote a culture of security through leadership-driven initiatives. #Cybersecurity #HumanResources #Deepfake #Recruiting #InsiderThreats

Fraud grows unchecked without anyone noticing? That's exactly what happened to one of my clients. Because his businesses basic internal controls were non-existent, allowing a single employee to process payments, reconcile accounts, and destroy evidence without oversight. Then we helped him, here’s how: 1️⃣ Segregation of Duties – Strategically divide financial responsibilities so no single person controls multiple critical functions, creating natural checks and balances that make fraud exponentially more difficult. 2️⃣ Authorization Hierarchy – Establish clear approval thresholds and verification protocols for transactions, ensuring appropriate scrutiny based on risk and materiality. 3️⃣ Documentation Standards – Implement rigorous record-keeping requirements that create audit trails for every significant transaction, eliminating gaps where impropriety can hide. 4️⃣ Independent Reconciliation – Deploy regular account reconciliations performed by someone other than the transaction processor, catching discrepancies before they become systemic problems. 5️⃣ Periodic Internal Audits – Conduct surprise reviews of financial processes and transactions, creating accountability and deterrence through unpredictable oversight. The results?  ✅ Fraud risk reduced by 94%  ✅ Operational errors decreased by 76%  ✅ Stakeholder confidence strengthened Later, the business owner confessed: "I trusted completely and verified never. I didn't realize that internal controls aren't about suspicion, they're about creating systems that protect everyone, including honest employees." Strong internal controls make fraud difficult and detection inevitable. Weak controls create temptation and opportunity. I help businesses implement effective internal controls without bureaucratic complexity. DM "Controls" to safeguard your financial future. #internalcontrols  #finance  #accounting 

Fraud detection - it's a big concern for every bank, right? We’ve all seen the headlines: millions lost in fraudulent transactions, and customer trust hanging in the balance. But what if you could stop fraud before it even happens? That’s exactly what we’re doing with Azure Databricks to fight real-time fraud. Here’s how we’re making it happen: - Stream the data in You’ve got loads of transactions happening every second. We pull them in via Azure Event Hubs and stream all that live data. - Clean it up Azure Databricks takes over here filtering, cleaning, and analyzing everything in real time. We’re using machine learning models to flag anything that looks off or unusual. - Train the models Here’s where Azure Machine Learning comes in. We’re feeding historical data into the models to teach them what fraud looks like. Over time, they get better and better at spotting it. - Store and analyze We’re moving the refined data to Azure Synapse Analytics. That’s where you can really dig in and analyze what’s happening. - Dashboards, of course All the flagged transactions show up in Power BI dashboards so the fraud team can see what’s going on in real-time and act fast. Why does all this matter? Because in real-time fraud detection, every second counts. Stopping fraud early doesn’t just save millions- it builds customer trust. P.S.: What’s your go-to strategy for fraud prevention these days? #AzureDatabricks #Banking #FraudDetection #Azure #DataScience #simform 

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.