Fraud Risk Consulting

As a SOC Analyst, it's tempting to rely on VirusTotal as the Ultimate Solution for spotting threats, but attackers know how to stay ahead. Here's a real-world example that demonstrates why behavioral detection matters more than static signatures: When analyzing binaries like Mimikatz, you might spot a string like "mimikatz_doLocal" being flagged as Malicious. However, attackers can easily evade this detection by tweaking the source code: 1- Changing strings: Replace "mimikatz_doLocal" with "anythingkatz_doLocal". 2- Renaming commands: Instead of "sekurlsa::logonpasswords," attackers use "securelsa::loginpasswordz." 3- Renaming prompts and executables: Change "mimikatz.exe" to "mimidogz.exe" and alter the application's interface to say "mimidogz." After recompiling, these small changes can bypass the AV and VirusTotal checks. Even if one part of the binary is flagged (like an error string), attackers will iterate until it’s clean. What Should SOC Analysts Do? - Focus on Behaviors: Tools like Mimikatz perform specific malicious actions (e.g., dumping LSASS memory). Behavioral detection makes it harder for attackers to evade. - Use Advanced Tools: Rely on EDR/XDR solutions that analyze patterns like process injection, suspicious memory reads, or credential dumping. - Contextualize Threats: Don't stop at VirusTotal scores. Investigate anomalies in logs, traffic patterns, and system behaviors. - Proactive Threat Hunting: Regularly hunt for renamed binaries, odd command usage, and unusual process trees in your environment. - Train Your Mindset: Always ask, "What is this file trying to achieve?" rather than, "What is its VirusTotal score?" Remember, attackers evolve their tactics to exploit over-reliance on static detections. To truly defend your organization, think like an attacker and hunt for what they do, not just the tools they use. #SOCAnalyst #ThreatHunting #DetectionTips #CyberSecurity

Your Procurement Cycle is a Minefield of Risks. Are You Walking Blind? Procurement Excellence | 17 JAN 2026 - Procurement always navigates hidden risks that can derail projects, inflate costs, and tarnish reputations. Ignoring them? That’s the real risk. Here are 7 CRITICAL risks lurking in your procurement cycle + how to defuse them: #1. Performance Risk ↳Suppliers underdelivering on quality/timelines. ↳Fix: Clear KPIs. Penalty clauses. Regular performance reviews. #2.Specification Risk ↳Vague requirements lead to wrong deliverables. ↳Fix:Collaborate with stakeholders upfront & freeze specs before sourcing. #3. Supplier Financial Risk ↳Bankrupt suppliers = halted operations. ↳Fix:Run credit checks, diversify suppliers, demand financial disclosures. #4. Reputation Risk (ESG) ↳Child labor or pollution in supply chain = brand crisis. ↳Fix: Supplier ESG screenings. Audits. Sustainability clauses. #5. Price Volatility Risk ↳Market swings crush budgets. ↳Fix: Fixed-price contracts. Hedging strategies. Cost-indexed clauses. #6. Fraud & Corruption Risk ↳Kickbacks, fake invoicing, collusion. ↳Fix: Segregate duties. Whistleblower policies. AI-powered anomaly detection. #7. Contract Leakage Risk ↳Unused discounts, auto-renewals, scope creep. ↳Fix:Centralized contract repository. Milestone alerts. Spend analytics. #Bonus I: Over-Reliance Risk ↳One supplier holds 80% of your spend. ↳Fix: Strategic supplier diversification. #Bonus II: Cybersecurity Risk ↳Suppliers accessing your systems >>data breaches. ↳Fix:Vendor security assessments. Zero-trust architecture. #Bonus III: Supply Disruption Risk ↳Natural disasters, geopolitics or supplier failures. ↳Fix: Dual sourcing, Safety stock & Real-time supply chain monitoring. Risk Mitigation Playbook: ✅ Proactive: Map risks at EVERY stage ✅ Use AI for predictive analytics, blockchain for traceability. ✅ Train & empower teams to spot red flags early. ✅ Collaborate & partner with Legal, Finance, Operations. Risk-aware procurement NOT about avoiding suppliers Procurement can’t own risk alone! Build resilient, ethical & agile supply chains that drive sustainable value. What risks keep YOU up at night? ♻️ Share to help someone in your network. ➕️ Follow Frederick for more content like this. #ProcurementExcellence #RiskManagement #Leadership

Subscription fraud is often invisible - but its impact is significant. Fake free trials and recurring payment abuse rarely appear fraudulent at the start. They typically mimic legitimate user behavior, making detection challenging. Common fraud patterns in subscription businesses • Multiple accounts created by the same user • Use of temporary emails and shared or stolen cards • Abnormal usage during trial periods • Intentional chargebacks after extensive consumption Business impact • Revenue leakage • Increased chargeback ratios • Payment gateway penalties • Distorted growth and retention metrics • Higher customer acquisition costs How fraud is detected effectively • Device and IP intelligence • Behavioral signal analysis • Payment reuse and failure patterns • Usage anomalies during trials and renewals Prevention strategies that scale • Limit free trials per device and payment method • Apply step-up verification for high-risk users • Monitor usage prior to renewals • Block bots and high-risk IP ranges • Leverage AI models to identify evolving fraud patterns Outcomes of a strong fraud strategy • Reduced fake users • Lower chargebacks • Accurate business metrics • Protected recurring revenue • Improved trust with genuine customers Fraud prevention is not friction. It is a safeguard for legitimate users and sustainable growth.

Third-Party Risk: The Hidden Cybersecurity Battlefield in Modern Supply Chains In our interconnected digital ecosystem, your security posture is only as strong as your weakest vendor. Modern enterprises rely on 100s of third-party vendors, creating an exponentially expanding attack surface. Supply chain attacks have become the preferred vector for sophisticated threat actors. Instead of targeting well-defended enterprises directly, attackers exploit vulnerabilities in trusted vendors to simultaneously breach hundreds of downstream organizations. Game-Changing Examples SolarWinds (2020): Compromised software updates affected 18,000+ customers including Fortune 500 companies and government agencies, demonstrating how a single vendor breach cascades across entire sectors. MOVEit (2023): A single vulnerability led to data breaches affecting over 600 organizations globally, showcasing the massive scale of modern supply chain impacts. Why Third-Party Risk Monitoring is Critical Continuous Visibility: Traditional annual assessments are insufficient. Organizations need real-time monitoring of vendor security posture, breach notifications, and compliance status changes. Risk Amplification: When attackers target managed service providers or software vendors, the impact multiplies across all their clients. One compromised vendor can expose thousands of organizations simultaneously. Regulatory Liability: With GDPR, CCPA, and emerging supply chain regulations, organizations face increasing liability for third-party security failures. Proactive monitoring demonstrates due diligence. Building Effective Defense Continuous Assessment: Implement real-time vendor risk scoring across your entire ecosystem Zero Trust Extension: Apply least-privilege access controls to all third-party connections Incident Response Integration: Ensure your IR plans account for vendor breaches with clear communication protocols Contractual Protection: Update vendor agreements with security requirements and liability provisions The Bottom Line Organizations can no longer treat vendor risk as procurement afterthought. The question isn't whether your supply chain will be targeted — it's whether you'll detect and respond effectively when it happens. The strongest security programs extend beyond organizational boundaries to create defensible ecosystems, not just defensible enterprises. #ThirdPartyRisk #TRPM #SupplyChainAttack #CyberSecurity

Fraud is one of the biggest hidden costs in #MobilityServices like #RideHailing, #FoodDelivery, and #MicroMobility. From GPS spoofing to fake accounts and payment abuse, modern fraud schemes exploit the very real-time nature that makes these services convenient. Traditional #Frauddetection methods often rely on batch processing and manual rule-based systems. They act too late, missing fast-moving and complex fraud patterns. Leaders like #Uber, #Grab, and #Lyft are changing the game by using real-time data streaming with #ApacheKafka and #ApacheFlink to detect and stop #Fraud as it happens. Here is how: #DataStreaming with Apache Kafka continuously streams data from payments, GPS, and user interactions to enable immediate decision-making. Apache Flink processes and correlates these events in real time, applying #AI and machine learning models to spot anomalies and block suspicious activity instantly. This shift from reactive to proactive fraud detection is protecting millions in revenue while keeping user trust intact. Real-world examples show the business impact: - FREE NOW (Lyft) uses #KafkaStreams to analyze trip routes and detect fake rides in real time. - Grab built its AI-powered fraud engine GrabDefence with Kafka and Flink, cutting fraud losses from 1.6% to 0.2%. - Uber’s Project RADAR combines Kafka and #MachineLearning models with human analysts to handle chargeback and payment fraud globally. The lesson is clear: Fraud in mobility services is a real-time problem that requires real-time solutions. A #DataStreamingPlatform provides the scalability, reliability, and intelligence needed to detect and prevent fraud before it happens. This is not only a technical upgrade but a strategic advantage for every mobility provider competing in an AI-driven digital economy. More details: https://lnkd.in/eZ7q_6M2 How do you see real-time streaming and AI changing the way mobility and delivery platforms protect their businesses from fraud?

1. 30 Common Insurance Frauds in India The image categorizes the most prevalent types of fraud across various insurance segments: Motor Insurance Frauds Staged or fake accidents Inflated repair bills Fake injury claims Multiple claims for the same loss Health Insurance Frauds Fake hospitalizations Concealing pre-existing diseases Malingering (pretending illness) Claiming for non-covered treatments Policy and Distribution Frauds Policy misrepresentation Misuse of add-on covers Bogus agents or intermediaries Premium diversion by agents Documentation Frauds Forged prescriptions and bills Identity theft Claims filed after the insured's death Corporate and Specialized Frauds Agricultural insurance manipulation Warehouse stock inflation Employer-employee collusion Reinsurance fraud Data breach exploitation 🛡️ 2. Best Mitigation Tactics The infographic highlights key controls insurers should implement: ✔️ Strong KYC and customer onboarding ✔️ Robust underwriting and risk assessment ✔️ Fraud risk scoring systems ✔️ Real-time verification with hospitals, RTOs, UIDAI, GSTN, etc. ✔️ GPS, video, and image validation ✔️ Hospital and garage audits ✔️ Behavioural analytics and anomaly detection ✔️ Staff training and awareness programs ✔️ Whistleblower mechanisms ✔️ Clear policy wording and customer education ✔️ Periodic review of high-risk claims ⚖️ 3. Regulatory Framework in India The image references important anti-fraud regulations: IRDAI Regulations (2017) Insurers must establish board-approved Fraud Risk Management (FRM) policies. IRDAI Master Circular on FRM Requires insurers to adopt technology-driven fraud prevention practices and submit annual reports. Anti-Fraud Guidelines Focus on: Data analytics Fraud monitoring Governance and reporting Insurance Act, 1938 (Section 45) Fraudulent claims can attract penalties, fines, and imprisonment. Insurance Association of India (IAI) Provides standard investigation and reporting frameworks. 📊 4. Magnitude of Insurance Fraud in India The infographic estimates: ₹20,000–₹30,000 crore lost annually due to insurance fraud. Motor insurance contributes nearly 70% of fraudulent claims. Health insurance fraud is increasing by approximately 20–30% annually. Crop insurance fraud significantly impacts government expenditure. Fraud ultimately increases premiums for honest policyholders. 🤖 5. AI-Powered Fraud Detection Tools The image emphasizes the growing role of technology: AI and Machine Learning Predict suspicious claims. Detect unusual claim patterns. NLP (Natural Language Processing) Identifies forged or manipulated documents. Computer Vision Analyses accident photos and medical images. Network Analytics Detects fraud rings and collusion networks. Predictive Analytics Forecasts emerging fraud trends. Robotic Process Automation (RPA) Automates verification and data checks. Voice Analytics

𝗨𝘀𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗮𝗻𝗱 𝗔𝗜 𝘁𝗼 𝗖𝗼𝗺𝗯𝗮𝘁 𝗜𝗻𝘀𝘁𝗮𝗻𝘁 𝗣𝗮𝘆𝗺𝗲𝗻𝘁𝘀 𝗙𝗿𝗮𝘂𝗱 The rise of instant payments has made AI-powered fraud detection a necessity. Unlike traditional rules-based systems, AI can spot subtle behavioral patterns across vast datasets in real time—vital for detecting complex, fast-moving fraud. Yet, as AI becomes central to fraud prevention, its responsible and transparent use is just as important. Consumers must be protected not only from fraud but also from the unintended harm of biased or opaque AI models. The stakes are high: an estimated 42.5% of fraud attempts now use AI, and nearly a third are successful. Criminals are evolving too, leveraging deepfakes and generative AI to bypass controls. The global market for deepfake detection is projected to grow 42% annually, from €4.73B in 2023 to €13.5B by 2026. Businesses are responding—three-quarters plan to adopt AI-driven fraud prevention tools—but fewer than a quarter have begun implementation, exposing a gap between awareness and action. At its core, AI’s strength lies in pattern recognition—automatically identifying relationships and anomalies in data. Just as a human analyst might, AI detects shifts such as unusual geolocation, new devices, or behavioral changes. In money-laundering cases, for example, mule accounts often move funds in chains; AI’s ability to view the network as a whole helps uncover these linked transactions. Fraud doesn’t appear in isolation—it often comes in waves and trends. Machine-learning models can evolve as new behaviors emerge, unlike static rules-based systems that require post-loss analysis to update their logic. This adaptability is especially crucial in an era of instant payments, where funds move within seconds. 𝗜𝗻𝘀𝘁𝗮𝗻𝘁 𝗣𝗮𝘆𝗺𝗲𝗻𝘁𝘀 𝗙𝗿𝗮𝘂𝗱 𝗣𝗿𝗲𝘃𝗲𝗻𝘁𝗶𝗼𝗻: 𝗧𝗵𝗲 𝗡𝗲𝗲𝗱 𝗳𝗼𝗿 𝗦𝗽𝗲𝗲𝗱 Speed is the main challenge. Instant payments typically settle within 10 seconds, leaving almost no time for manual fraud checks. While some transactions can be delayed if flagged as suspicious, decisions must be made instantly. Rules-based systems struggle here—they tend to generate too many false positives, draining resources and delaying legitimate payments. In contrast, AI-enhanced systems evaluate transactions in real time, combining models and rules to minimize friction. This enables fraud teams to focus their attention on the truly risky cases. Ultimately, AI doesn’t replace human judgment—it amplifies it. By providing real-time intelligence and adapting to new fraud patterns, AI helps businesses strike the balance between security and customer experience. As instant payments continue to expand globally, this balance will define the winners in the next phase of fraud prevention Source Visa #fintech #ai

Here are 9 Transaction Red Flags Every AML Fraud Analyst Must Spot (With Real-World Examples). Financial criminals are smart—but you can be smarter. A single overlooked transaction can lead to millions laundered, regulatory fines, and reputational damage. As AML/KYC professionals, your vigilance is the last line of defense. Here are 10 red flags—with real-world examples—to help you catch illicit activity before it escalates: 🚩 Red Flag #1: Rapid Beneficiary Activity 📌 What to Watch: A newly added beneficiary receives large, immediate payments (especially if the account was recently opened). 🔍 Example: A dormant corporate account suddenly adds a foreign beneficiary and transfers $250,000 within hours—with no prior business history. 🚩 Red Flag #2: Vague or Generic Payment Descriptions 📌 What to Watch: Transactions labeled "consulting fees," "services rendered," or "miscellaneous"—without supporting invoices or contracts. 🔍 Example: A client sends $50,000 to a high-risk jurisdiction with the note "business expenses." When questioned, they can’t provide documentation. 🚩 Red Flag #3: Round-Number Transactions (Smurfing/Structuring) 📌 What to Watch: Repeated transfers of $9,500, $14,900, or other amounts just below reporting thresholds. 🔍 Example: A customer makes 12 cash deposits of $9,800 across different branches in one week. 🚩 Red Flag #4: Layering Through Multiple Jurisdictions 📌 What to Watch: Funds moving through 3+ countries, especially high-risk ones (e.g., Cyprus, UAE, Seychelles). 🔍 Example: A payment from Germany → Panama → Cayman Islands → Mauritius with no clear business rationale. 🚩 Red Flag #5: Sudden Behavioral Shifts 📌 What to Watch: A low-risk customer abruptly increases transaction volume, changes beneficiaries, or switches industries. 🔍 Example: A small retail business that normally processes $5K/month suddenly sends $200K to a crypto exchange. 🚩 Red Flag #6: Reluctance to Provide Source of Funds (SoF) 📌 What to Watch: A customer delays, refuses, or provides inconsistent explanations for large transactions. 🔍 Example: A PEP (Politically Exposed Person) claims a $1M deposit is from "savings" but can’t explain the origin. 🚩 Red Flag #7: Micro-Splitting to Avoid Detection 📌 What to Watch: Multiple transactions just below AML thresholds (e.g., $9,999 instead of $10,000). 🔍 Example: A corporate account sends 15 wires of $9,950 to the same beneficiary in one day. 🚩 Red Flag #8: Mismatched Business Activity 📌 What to Watch: Transactions that don’t align with the customer’s profile (e.g., a bakery dealing in offshore oil trades). 🔍 Example: A freelance graphic designer receives $500K from a mining company in Zimbabwe. 🚩 Red Flag #9: Overuse of Cash or Cryptocurrency 📌 What to Watch: High-value cash deposits/withdrawals. 🔍 Example: A customer deposits $200K in cash monthly but claims to run an "e-commerce store" with no online presence.

⚡ How Banks Detect Card Fraud in Under 100 ms Every time you tap your card, an incredible amount of analysis happens before your transaction is approved—usually in less than 100 milliseconds. Let's see what happens behind the scenes. 💳 Step 1: Transaction Initiated You tap your card at a POS terminal. An ISO 8583 authorization request is created and sent through: POS Terminal ↓ Acquirer Bank ↓ Visa / Mastercard ↓ Issuer Bank The issuer now has only a few milliseconds to decide whether the transaction is genuine. 🧠 Step 2: Fraud Engine Takes Over Before checking your account balance, the issuer's Fraud Detection Engine evaluates the transaction using hundreds of rules and AI models. It analyzes signals such as: 📍 Location Check Is the transaction happening in a location consistent with your recent activity? Example: A purchase in London just minutes after one in Delhi is suspicious. 💰 Transaction Amount Is the amount unusual for this cardholder? ⚡ Velocity Check Have there been multiple transactions within a very short time? Example: 5 purchases in 2 minutes. 🏪 Merchant Category (MCC) Does the merchant type match your normal spending behavior? 📱 Device & Channel Is this a trusted device or payment channel? 📊 Behavioral Analysis Does this transaction fit your historical spending pattern? 🚫 Blacklist & Watchlists Is the card, merchant, IP address, or device already flagged? 🤖 Step 3: AI Generates a Risk Score All these checks are combined to calculate a risk score. Risk Score < 30 ↓ Approve ✅ Risk Score 30–70 ↓ Step-up Authentication (OTP / 3DS) Risk Score > 70 ↓ Decline ❌ This decision is made in just a few milliseconds. ⏱️ Example Timeline 0 ms → Card tapped 20 ms → Authorization reaches issuer 45 ms → Fraud engine evaluates risk 75 ms → Decision made 95 ms → Response reaches POS The customer only notices a brief "Processing..." message, while the bank has already analyzed hundreds of data points. 🛡️ Why It Matters Modern fraud detection isn't based on a single rule. Banks use a combination of: Rule-based engines Machine Learning models Real-time behavioral analytics Device fingerprinting Historical transaction patterns to stop fraudulent transactions before money leaves the account. 💡 Key Takeaway Banks don't just check your balance—they evaluate every transaction against hundreds of risk signals in under 100 milliseconds before deciding whether to approve or decline it. Every time you tap your card, an AI-powered fraud engine races against the clock—analyzing hundreds of signals and making a decision in under 100 milliseconds. That's the invisible technology protecting billions of transactions every day.

Master Third-Party Risk Management (TPRM) in 12 Steps 🛡️ Your organization’s security is only as strong as its weakest vendor. Onboarding a third-party tool without thorough risk assessment is like locking your front door while handing out key copies to strangers. Here is a practical, 12-step framework to evaluate vendors, mitigate risk, and make smarter business decisions: Phase 1: Identification & Categorization Vendor Onboarding Capture critical vendor metadata from day one (business owner, contract value, service scope). Vendor Criticality Assessment Determine their operational impact. Do they access internal networks or process customer data? Categorize critical vendors early. Data Classification Map out what sensitivity level of data they handle: Public, Internal, Confidential, or Restricted. Phase 2: Risk & Controls Evaluation Inherent Risk Assessment Evaluate raw risk exposure across Data Privacy, Cyber Security, Compliance, and Operations before factoring in existing security controls. Security Questionnaire Send tailored security questions covering key domains: Information Security (ISMS), Access Management (MFA), Network Security, Patching, and Incident Response. Evidence Collection Never rely strictly on "Yes" answers—always demand proof. Collect SOC 2 Type II reports, ISO 27001 certificates, penetration test summaries, and BCP/DR plans. Phase 3: Deep Dive & Scoring Document Review Validate that claimed controls match provided evidence (e.g., verifying MFA implementation via identity provider dashboard screenshots). Risk Identification Highlight specific security gaps or missing controls (e.g., lack of MFA or missing Disaster Recovery testing). Risk Rating Assign an objective score based on total residual risk to place vendors into clear bands: Low, Medium, High, or Critical. Phase 4: Decision & Governance Remediation Plan Outline mandatory corrective actions, owners, and strict SLA timelines (e.g., require MFA setup within 30 days). Risk Acceptance If a vendor cannot remediate immediately, require formal sign-off from the Business Owner, InfoSec Lead, and Risk Committee. Final Recommendation Deliver a clear mandate: Approved, Approved with Conditions, or Rejected. Key Takeaways for Security Leaders: Verify, don't trust: Always demand tangible evidence. Automate where possible: Leverage TPRM platforms (like ServiceNow, MetricStream, or Aravo) to handle questionnaires and risk scoring automatically. Continuous Monitoring: TPRM doesn't stop at onboarding. Regular reviews, re-assessments, and real-time monitoring are essential for continuous compliance. What framework or tools does your team rely on for Third-Party Risk Management? Share your thoughts below! 👇 #Cybersecurity #TPRM #RiskManagement #GovernanceRiskCompliance #InformationSecurity #VendorRisk