Fraud Scenario Planning

I built a Claude skill that maps a company's entire fraud surface. You give it a domain. It maps every fraud, abuse, and trust-and-safety threat that business faces, ranks them by how much each actually matters, and reweights all of it for how AI changes the attack. The output is a report a leader can drop in front of their team to decide where to focus next. I spent a decade as the first fraud hire at Scribd, Dodgeball, Nearside, and ecoATM. This is that judgment, turned into something anyone can run in minutes. Here's how I built it. Copy the steps to Claude if you want your own. Step 1: Map the process To make expert work AI-native, you first have to understand how it's actually done. This is why domain expertise still matters post-AI. A generic "fraud checklist" is useless. the job is knowing which threats matter for which business model. How I actually assess a company: 1. Understand the business model (how money moves, how users sign up, the product surface) 2. Enumerate every plausible fraud and abuse vector 3. Reweight each for how AI changes the economics of the attack 4. Score each on likelihood, business impact, and AI amplification 5. Rank and tier into a priority order 6. Write it up so a team can act on it Step 2: Where am I needed vs. not? At the ends. Picking the company and feeding in inside knowledge at the front. Sanity-checking the final ranking against my gut at the back. The model should never override an operator who's seen the real numbers. AI handles the rest. Step 3: Build the skill A directory of instructions and reference files that mimic the steps above. 1. Recon. Reads the actual domain and flows. How you sign up, what the free tier allows, where money flows in and out, what gets transacted. The business model IS the fraud model. 2. Threat enumeration. Pulls from a taxonomy organized by business archetype (marketplace, subscription, fintech, social) and grabs every vector whose exposure signal is present. 3. AI re-scoring. For every threat, asks how generative AI changes it. Deepfakes beating KYC, LLMs defeating content filters, agents collapsing the cost of attacks that needed humans. Scored honestly, including where AI changes nothing. 4. Prioritization. Every threat scored on likelihood, impact, and AI amplification, then tiered Critical / High / Moderate / Watch. 5. Operator override. If you worked there, your ground-truth can beat the model's research. I ran it on Scribd from the outside (where I previously worked). Then I told it the real risks I cared about, and it re-ranked accordingly. 6. The report. A self-contained briefing themed to the company's brand colors, with a single "AI Exposure Index" and a risk matrix plotting every threat. I ran it on 20+ companies to prove it holds across business models. Every report came out genuinely different. Steal it, and ask me anything about how it works.

A Fraud Risk Assessment is a structured process used by organizations to identify, evaluate, and manage the risk of fraud. It helps management and internal auditors understand where and how fraud could occur, the likelihood of it happening, and the potential impact. Key Objectives: - Identify potential fraud schemes relevant to the organization. - Assess the likelihood and impact of each fraud risk. - Evaluate existing controls that mitigate fraud risks. - Highlight gaps where controls are weak or missing. - Recommend actions to strengthen anti-fraud measures. Core Elements: - Risk Identification Common fraud risks: asset misappropriation, financial statement fraud, corruption, procurement fraud, payroll fraud, etc. Consider both internal and external threats. Risk Assessment: Evaluate likelihood (probability of occurrence) and impact (financial, reputational, regulatory). Use qualitative or quantitative scoring methods. Controls Evaluation: Review preventive and detective controls (e.g., segregation of duties, whistleblower hotlines, audits, data analytics). Assess control effectiveness and identify weaknesses. Action Planning: Prioritize high-risk areas. Develop remediation plans, strengthen internal controls, and improve monitoring. Documentation & Reporting: Maintain clear records of identified risks, assessments, and mitigation measures. Report findings to senior management and/or the audit committee. Benefits: Reduces likelihood of fraud by proactively addressing risks. Improves governance, compliance, and internal controls. Enhances fraud awareness among employees. Protects organizational assets and reputation.

What if insurers had RED teams? 🔍 Inspired by Google’s RED team — the internal unit that tries to break systems so they can be improved — here’s a simple idea for fighting fraud in insurance. Why not run internal and external RED teams that inject realistic, controlled fake cases into claims systems — staged FNOLs, doctored invoices, edited photos, even deepfake voice notes? The goal is not to catch people out, but to test the entire chain: AI models, rules engines, provider networks, and human vigilance. How it could work (practical sketch): ✅ Inject synthetic claims and tampered documents into live or sandboxed pipelines. ✅ Measure detection rates across both automated systems and human handlers. ✅ Run joint exercises where AI flags suspicious cases and claims teams investigate — then compare outcomes. ✅ Feed the results back into model training, rules tuning, staff coaching, and process design. ✅ Repeat regularly and report readiness metrics to risk and executive teams. Why this helps: ✅ Exposes blind spots before fraudsters exploit them. ✅ Strengthens both machine and human detection capabilities. ✅ Reduces false positives through better calibration and judgement. ✅ Builds organisational resilience — not just defensive rules. Of course, this must be tightly governed. The aim is to learn, not to punish. If you had to design such an exercise — what scenarios would you inject first? Finsurtech.AI ——————————— 🧠 Visit my website. 🔝 Follow for more on modern solutions and digital transformation in insurance

Data Science Problems & solutions : 21 Scenario: In one bank project I was part of, we had to score eligibility / detect fraud for new-to-bank customers during account opening. The challenge: no transaction history, many synthetic identity attacks, and regulatory pressure to do KYC/KYB without slowing onboarding too much. Solution: 1. Use identity verification + document checks as first filter: government-issued IDs, facial match / liveness, address verification. This helps catch frauds who use forged or mismatched identity docs. 2. Leverage device & digital signals: IP address reputation, device fingerprinting, browser & OS anomalies, velocity of signup attempts. As Trulioo notes, IP geolocation + device ID + email / phone age are key signals during onboarding. 3. Use consortium / network data: share fraud / identity risk signals across institutions (e.g. shared blacklists, identity networks). LexisNexis Risk Solutions and similar firms do this to improve detection of synthetic identity or mule-accounts early. 4. Apply risk scoring + tiered friction: build a model that outputs risk score, then require incremental checks only if score exceeds threshold (e.g. manual review, additional doc, phone verification). So good users pass quickly, suspicious ones get vetted deeper. 5. Monitor post-onboarding behavior for the initial period (say 30-90 days): new account fraud often shows up soon after opening. Have alerts for anomalies (e.g. high-volume transactions, sudden changes), and trigger remediation or rollback. Trulioo warns many identity theft/new account frauds show activity within first 90 days. Hope it helps. #fraud #banking #identityverification #datascience #riskmanagement

If you haven’t practiced your incident plan lately, you don’t really have one. When something breaks, nobody opens a PDF. They grab phones and start guessing. Run a short tabletop: pick one scenario, run through it for 45 minutes, and see what would happen. Involve outside breach counsel. They’re the best quarterback for any incident, so bring them into the tabletop too. Then practice the plan, revise the plan, print the plan. How often? ↪ Full tabletop: every 6 months (or after major changes). ↪ Lighter drills: quicker single-scenario runs in between. ▶ Focus on: who declares the incident, how decisions are made, how you try to claw back money if it’s moved, and how you reach people if systems are down. ▶ Scenarios to choose from this week: Account takeover, Funds transfer fraud, Ransomware. #JasonMakevich #Cybersecurity #IncidentResponse #BusinessContinuity #Tabletop #RiskManagement

🔍 Preparing for a Deep Dive Fraud Investigation in Internal Audit Too often, internal auditors are expected to "prevent" fraud. But let's set the record straight: Internal Audit does not prevent fraud — it assesses the controls that mitigate fraud risks. And when those controls fail or suspicions arise, we must be ready for a deep dive fraud investigation. 🎯 1. Define the Scope and Objective Clarify the goal: Is it to assess controls? Investigate allegations? Or review high-risk areas (procurement, reporting)? Align with IIA Standard 1210.A2: Ensure the team understands fraud characteristics, red flags, and schemes. Define the role of Internal Audit: Leading the investigation or providing analytical support? 🔎 2. Conduct a Fraud Risk Assessment Integrate fraud risks into the audit plan (as per IIA Standard 2120.A2). Use historical data, trends, and management insight to identify high-risk zones. Evaluate controls like segregation of duties and access restrictions for weaknesses. 👥 3. Assemble the Right Team Check if your internal team has the capability. If not, bring in forensic accountants or CFEs. Coordinate with legal counsel for evidence integrity and legal compliance. Complex cases may also need digital forensics and transaction specialists. 🧮 4. Use Tools & Data Analytics Detect anomalies with tools like ACL, IDEA, or Tableau. Apply the Case IQ Fraud Investigation Checklist for structured tracking. Cross-check transactions, logs, invoices, and reimbursement reports. 🚩 5. Identify Red Flags Watch for unusual patterns: duplicate payments, lifestyle gaps, and frequent manual journal entries. Refer to trusted models like the NYS Office of Mental Health’s Top Ten Internal Controls for practical prevention benchmarks. 📁 6. Plan Evidence Collection & Interviews Secure both digital and physical evidence. 🖥️📄 Use forensically sound tools to maintain chain of custody. Follow structured, non-accusatory interview guides. Document everything — if it’s not written, it didn’t happen. ⚖️ 7. Ensure Legal Compliance & Confidentiality Stay aligned with data privacy laws and organisational rules. Maintain strict confidentiality. Breaches can jeopardise the case. Involve legal early if regulatory violations or litigation risks arise. 🧩 8. Prepare for Post-Investigation Action Analyse what went wrong and where the control failures occurred. Update fraud risk assessments and audit plans. Share lessons learnt across departments to prevent recurrence. 🔔 Follow me (Syekh Farhan Robbani) for more hands-on audit, fraud, and internal control insights that help you build trust and protect business value in this complex world. #InternalAudit #FraudInvestigation #AuditorLife #RiskManagement #FraudDetection #ForensicAudit #Compliance #IIAStandards #AuditTips #CorporateGovernance #Governance #RiskCompliance #AuditTools #SyekhFarhanRobbani #FraudAwareness #AuditStrategy #LinkedInArticles

🚨 NACHA 2026 is changing the rules of the game. For decades, ACH fraud monitoring has been reactive, primarily focused on the receiving end. But starting in 2026, ODFIs, TPSs, and TPSPs will be on the front lines. You’ll now need to detect fraud before transactions hit the ACH network. Here’s what you can start doing today 👇 ✅ Segment your originators by risk. High-volume senders and new originators should have tighter velocity and name-match rules. ✅ Deploy behavioral monitoring for outbound ACH. Look for anomalies like first-time recipients, mismatched metadata, or rapid account changes. ✅ Run simulated fraud scenarios. BEC, payroll redirection, and synthetic ID bust-outs: test your systems before the rules take effect. Use Unit21's validation rule or shadow rule capabilities to do so. At Unit21, we’re helping ODFIs, TPSs, and TPSPs get ahead of NACHA’s 2026 obligations with no-code rule building, CRR-based segmentation, and out-of-the-box detection templates. 🎥 Watch this short video where we break down the shift and how to stay ahead of what’s coming. #Fintech #FraudPrevention #Payments #NACHA2026

𝟓% 𝐨𝐟 𝐘𝐨𝐮𝐫 𝐑𝐞𝐯𝐞𝐧𝐮𝐞 𝐃𝐢𝐬𝐚𝐩𝐩𝐞𝐚𝐫𝐞𝐝 𝐋𝐚𝐬𝐭 𝐘𝐞𝐚𝐫. 𝐇𝐞𝐫𝐞'𝐬 𝐇𝐨𝐰 𝐭𝐨 𝐒𝐭𝐨𝐩 𝐈𝐭 Organizations lose 5% of revenue to fraud annually. The ACFE's 2024 Report confirms what risk professionals have suspected, fraud is accelerating faster than detection capabilities. In the UAE, losses hit AED 1.2 billion in 2024. APP fraud surged 43% as criminals weaponized AI and exploited real-time payment systems. Traditional controls aren't keeping pace. The patterns are predictable: ghost payrolls in HR, vendor kickbacks in procurement, data manipulation in finance, social engineering across IT. Different departments, same vulnerabilities. 𝐓𝐡𝐞 𝐏𝐫𝐨𝐛𝐥𝐞𝐦: 𝐑𝐞𝐚𝐜𝐭𝐢𝐯𝐞 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 𝐯𝐬. 𝐏𝐫𝐨𝐚𝐜𝐭𝐢𝐯𝐞 𝐏𝐫𝐞𝐯𝐞𝐧𝐭𝐢𝐨𝐧 Most organizations discover fraud months after it starts. By then, financial damage is done and regulatory scrutiny follows. The solution isn't adding more controls, it's implementing smarter ones. The "𝑭𝒓𝒂𝒖𝒅 𝑷𝒓𝒆𝒗𝒆𝒏𝒕𝒊𝒐𝒏 𝑮𝒖𝒊𝒅𝒆: A Practical Reference for Risk Management Professionals" addresses this gap systematically. 𝐖𝐡𝐚𝐭'𝐬 𝐜𝐨𝐯𝐞𝐫𝐞𝐝: ** 190 𝑓𝑟𝑎𝑢𝑑 𝑠𝑐𝑒𝑛𝑎𝑟𝑖𝑜𝑠 𝑚𝑎𝑝𝑝𝑒𝑑 𝑎𝑐𝑟𝑜𝑠𝑠 19 𝑑𝑒𝑝𝑎𝑟𝑡𝑚𝑒𝑛𝑡𝑠   ** 𝑅𝑖𝑠𝑘 𝑖𝑚𝑝𝑎𝑐𝑡 𝑟𝑎𝑡𝑖𝑛𝑔𝑠 𝑎𝑛𝑑 𝑒𝑎𝑟𝑙𝑦 𝑤𝑎𝑟𝑛𝑖𝑛𝑔 𝑖𝑛𝑑𝑖𝑐𝑎𝑡𝑜𝑟𝑠   ** 𝑅𝑒𝑎𝑑𝑦-𝑡𝑜-𝑑𝑒𝑝𝑙𝑜𝑦 𝑐𝑜𝑛𝑡𝑟𝑜𝑙𝑠 𝑎𝑙𝑖𝑔𝑛𝑒𝑑 𝑤𝑖𝑡ℎ 𝐶𝑂𝑆𝑂, 𝐼𝑆𝑂 31000, 𝐴𝐶𝐹𝐸 𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑𝑠   ** 𝐼𝑚𝑝𝑙𝑒𝑚𝑒𝑛𝑡𝑎𝑡𝑖𝑜𝑛 𝑡𝑖𝑚𝑒𝑙𝑖𝑛𝑒𝑠 𝑤𝑖𝑡ℎ 𝑚𝑒𝑎𝑠𝑢𝑟𝑎𝑏𝑙𝑒 𝑒𝑓𝑓𝑒𝑐𝑡𝑖𝑣𝑒𝑛𝑒𝑠𝑠 𝐾𝑃𝐼𝑠 This isn't academic theory. These are field-tested frameworks that work in high-growth environments with lean compliance teams. Download the attached guide and start implementing these controls today. Focus on high-impact, low-effort wins first. #FraudPrevention #RiskManagement #InternalAudit #ACFE

“You need data to train ML fraud models.” I thought so too. Then necessity forced us to discover shortcuts. Why necessity? Here's the paradox I kept seeing over and over: Companies that need fraud protection most urgently  =  The ones with the least training data to build it. But here's what I learned after running a fraud vendor: Scenario 1: Zero data • Challenge: New product, no transaction history  • Solution: Borrow patterns from similar flows or start with basic heuristics  • Result: Some protection now vs perfect protection never Scenario 2: Data but no labels • Challenge: Years of transactions, zero fraud tracking • Solution: Use proxy signals (declines, refunds, complaints) + network analysis • Result: 70% accuracy immediately vs waiting 6 months for data to flow The real constraint isn't technical complexity. It's organizational flexibility. The secret for success? Embrace "good enough" data to get started,  then systematically improve as they scale. Your ML model doesn't need to be perfect. It needs to be deployed. Want the full breakdown? In this week's "The Saturday Fraud Strategist" I'm going to dive deep into: • 5 ways to bootstrap fraud models with no data • Why model features must support new markets before launch • Why unsophisticated fraud is your training data goldmine If you're not subscribed yet, the link is under my picture ⬆️ See you on Saturday! ------------- Enjoy this? ♻️ Repost it to your network and follow Chen Zamir for more. Want to dive deeper? Join "The Saturday Fraud Strategist". Each week I'll be sharing detailed advice on how to grow Fintechs safely. ⬆️ Click "View my newsletter" under my name ⬆️