Loss Prevention Strategies

Detecting fraud is no longer just about manual checks; advanced analytics and AI-driven insights allow companies to anticipate risks before they escalate. This shift minimizes financial loss and fosters a data-driven culture of transparency and trust. Behavioral analytics transforms fraud detection by leveraging data patterns, machine learning, and NLP to identify suspicious activities. Unlike traditional rule-based approaches, this method adapts dynamically, learning from transactional and contextual data to detect anomalies. For example, an insurance claim from an unusual location or an inconsistent medical history can trigger alerts. Machine learning refines these insights, reducing false positives while improving accuracy. Ethical considerations remain critical, ensuring privacy and fairness in automated decisions. By integrating analytics into business processes, organizations strengthen fraud prevention, optimize investigations, and protect consumers from financial exploitation. #AI #Insurance #InsurTech #DigitalTransformation

Still trying to manage your ever-increasing alert flow by hiring more analysts? That’s much like adding buckets to deal with a leaking roof. Invest in detection engineering and automation engineering to reduce the alert flow and prevent alert fatigue and unhappy analysts. Here are some best practices: - Apply an automation-first strategy: handle and/or accelerate all alerts through automation - Continuously tune and optimize detection rules - Let analysts and detection / automation engineers work closely together to increase the effectiveness of engineering efforts - Establish metrics for rule quality to identify candidates for tuning and automation - Test against defined quality criteria before putting any detection rules live - Increase the fidelity of your rules by alerting on more specific criteria - Aggregate and analyse batches of noisy alerts daily or weekly, instead of handling them individually in real-time - Consider your ideal ratio between analysts and engineers. Start out with 50-50, then decide what would best suit your needs - Make risk-based decisions on added value of rules compared to time investment, and drop time-consuming rules with little added value if they cannot be tuned properly This is by no means an easy thing to do. But by focussing on engineering and detection quality, you can transition to a state where you control of the alert flow instead of the other way around, so that analysts can focus on the alerts that truly matter. #soc #securityoperations #securityanalysis #detectionengineering #automationfirst

AML Case Study: Unusual Customer Behavior – No Transactional Activity Context: A bank’s AML system generates an alert for a long-time customer, Ms. Y, not due to financial transactions but due to behavioral and documentation anomalies. The alert is triggered based on inconsistencies in identity verification and unusual account access patterns. Scenario: Ms. Y, a private banking client, has maintained a dormant account for years with no recent transactions. However, the following red flags prompt an internal review: Multiple Login Attempts from Different Locations: Unsuccessful login attempts are detected from three different countries within a short period. Inconsistent KYC Information: During an account update request, the newly submitted identification documents differ from those previously provided. Third-Party Inquiry: A person claiming to be Ms. Y’s “legal representative” calls the bank requesting changes to account details but fails security verification. Investigation Steps: Identity Verification Review Cross-checked new ID documents against original records. Contacted the customer directly using the on-file communication channels. Device & IP Analysis Confirmed that login attempts originated from high-risk jurisdictions known for cyber fraud. Identified a mismatch in registered and recently used devices. Customer Interaction & Due Diligence Ms. Y was contacted via a secure channel, and she confirmed she had not attempted to access the account or authorize changes. Reported potential identity theft to compliance teams for further escalation. Outcome & Actions Taken: The account was flagged and temporarily frozen to prevent unauthorized access. Enhanced due diligence (EDD) was applied, requiring in-person verification before any account modifications. The case was escalated to law enforcement for potential fraud and identity theft. Key Takeaways: AML is not solely about financial transactions—behavioral anomalies can be strong indicators of financial crime. Continuous monitoring of customer activity, login behaviors, and identity verification is crucial for fraud prevention. Collaboration between compliance, fraud teams, and law enforcement helps mitigate risks effectively. How can financial institutions detect and mitigate AML risks when no transactions are involved? What key behavioral red flags should investigators look for? #AML #FinancialCrime #RiskManagement #FraudDetection #Compliance #KYC #DueDiligence #AMLInvestigations #SuspiciousActivity #FinancialSecurity

Anti-shoplifting systems in shopping malls use smart cameras with computer vision to detect theft in real time. These cameras, equipped with edge AI processors, continuously analyze video feeds to track customer movements, hand gestures, and item interactions without storing full footage for privacy. The system employs pose estimation (e.g., OpenPose, MediaPipe) to monitor body posture and detect suspicious actions like reaching into pockets, lifting clothing, or placing unpaid items in bags or carts. Object detection models (YOLOv8, EfficientDet) identify products, while re-identification algorithms track the same person across multiple cameras. Anomaly detection flags unusual behavior, such as lingering near high-value items or placing goods inside clothing. When a high-confidence theft event is detected (e.g., item disappears from hand into pocket without scanning), the system instantly alerts security via mobile devices with video clips and timestamps. Key tools enhancing functionality include:~~~~ - NVIDIA Jetson or Intel Movidius for on-camera processing - DeepStream SDK for real-time multi-stream analysis - Behavioral analytics platforms (e.g., Vaak, Veesion) - Integration with POS systems to cross-check scanned vs. carried items. This reduces false alarms through multi-factor verification and protects privacy by processing data locally and deleting footage within seconds unless flagged.

I never thought it would happen to me. One day, I noticed a spike in chargebacks. I knew something was wrong, but I didn’t know what. I started by investigating the types of fraud we were experiencing. From fake accounts to transaction fraud, it was overwhelming. Here’s how to detect and prevent fraud at every stage of the customer journey: Stage 1: Data Collection Data is your first line of defense. • Gather as much user data as possible. • Track device information, IP addresses, and user behavior. • Monitor changes in user activity. Understanding user patterns helps in identifying anomalies early. Stage 2: Basic Risk Scoring Identify low-hanging fruit. • Use simple rules to score transactions. • Look for mismatched billing and shipping addresses. • Flag unusual purchasing behaviors. This stage catches the most obvious fraud attempts. Stage 3: Dynamic Friction Balance security and user experience. • Implement step-up authentication for suspicious activities. • Use dynamic risk based routing • Introduce verification processes at critical points. Dynamic friction helps reduce fraud without hurting conversion rates. Stage 4: Advanced Analytics Deep dive into data for insights. • Use machine learning to detect patterns. • Analyze transaction histories and behaviors. • Integrate third-party data sources for enhanced detection. Advanced analytics provide a comprehensive view of potential threats. Stage 5: Continuous Optimization Stay ahead of evolving threats. • Regularly update your fraud detection rules. • A/B Test and refine your strategies. • Stay informed about new fraud techniques and trends. Continuous testing ensures your not two steps behind fraudsters. A comprehensive fraud strategy requires a layered approach.

In the fast-paced world of cybersecurity, alert storms can overwhelm Security Operations Centres (SOCs), causing analyst fatigue and increasing the risk of critical threats slipping through unnoticed. Managing these storms effectively is crucial to maintaining operational stability and protecting sensitive data. 5 WAYS TO AVOID ALERT STORMS IN SECURITY OPERATION CENTRE (SOC) 1. UNIFY THREAT MONITORING Fragmented security tools generate isolated alerts, leading to duplicate notifications and poor threat correlation. By unifying threat monitoring across systems, you can: • Centralise all alerts from firewalls, SIEMs, EDR and other tools in a single platform. • Streamline threat visibility to identify patterns across multiple attack vectors. • Reduce manual effort and improve incident prioritisation. Example: Use a well-integrated SIEM solution to ingest and correlate logs from multiple sources, reducing noise from disparate systems. 2. FINE-TUNE DETECTION RULES Default detection rules often generate excessive false positives. Analysts can avoid unnecessary alerts by fine-tuning detection mechanisms to: • Set specific thresholds based on the environment and use case. • Reduce false positives by excluding benign behaviour patterns. • Update rules regularly to reflect evolving threats. Tip: Regularly review and customise detection rules in your SIEM or EDR tool based on your organisation’s risk profile. 3. GROUP ALERTS INTELLIGENTLY Alert storms often occur when multiple alerts are triggered for a single incident. Intelligent grouping helps analysts focus on the bigger picture by: • Aggregating alerts related to the same event or threat. • Using correlation rules to identify connections between logs and alerts. • Reducing the number of tickets created for similar incidents. Example: Implement alert deduplication and correlation logic in your SOC tools to group login attempts from the same source IP into a single incident. 4. PRACTICE GOOD ALERT HYGIENE Poorly managed alerts can clog the system, overwhelming analysts. Practising alert hygiene ensures that: • Old, irrelevant or low-priority alerts are reviewed and resolved promptly. • Alerts with no actionable outcomes are tuned or suppressed. • Historical alert data is archived but accessible for compliance and review. Tip: Conduct regular alert reviews to identify noisy rules and disable alerts that do not add value. 5. AUTOMATE REPETITIVE TASKS Manual alert triaging during a storm is time-consuming and error-prone. Automation can help SOC teams handle large volumes efficiently by: • Automating triage processes for known low-risk events. • Using SOAR tools to investigate and respond to alerts without human intervention. • Deploying playbooks for common incidents to reduce response time. Example: Configure your SOAR tool to automatically resolve low-risk phishing alerts by blocking the sender and tagging the email for further review. For more details, please refer to the attached PDF.

INTERNATIONAL SECURITY BEHAVIORAL ANALYSIS COURSE For Security Officers, Supervisors, and Hotel Security Professionals This course includes four important professional modules: 15 Types of Suspicious Hotel Guest Behaviors (Every Senior Security Officer Must Recognize) 1. Loitering Without Purpose A person stays in the lobby, corridors, or parking areas without a clear reason for a long time. Possible risk: Theft Surveillance Unauthorized access 2. Avoiding CCTV Cameras The person deliberately tries to: Walk around cameras Cover their face Stand in blind spots Possible risk: Planning illegal activity. 3. Refusing to Provide Identification The guest refuses to show ID during check-in or verification. Possible risk: Fraud Identity concealment Criminal activity. 4. Entering Restricted Areas Attempting to access: Staff corridors Security rooms Maintenance areas Emergency exits Possible risk: Sabotage Theft Intelligence gathering. 5. Frequent Room Changes The person requests many room changes without logical reasons. Possible risk: Avoiding tracking Illegal activities. 6. Unusual Interest in Security Operations The person asks questions like: Where are the cameras? How many guards are on duty? What time do guards change shifts? Possible risk: Security testing. 7. Carrying Suspicious Items Items that appear unusual: Large bags without reason Hidden objects Equipment not related to travel Possible risk: Smuggling Weapon concealment. 8. Nervous or Aggressive Reactions When security politely approaches them, they become: Defensive Angry Extremely nervous. Possible risk: Hiding something illegal. 9. Repeated Visits Without Booking The person enters the hotel many times but never checks in. Possible risk: Surveillance Target scouting. 10. Attempting to Follow Guests Trying to follow guests into: Elevators Guest floors Private areas Possible risk: Theft Harassment. 11. Using Multiple Names The person registers with different names or identities. Possible risk: Fraud Criminal evasion. 12. Excessive Privacy Behavior Examples: Closing curtains constantly Avoiding housekeeping Refusing any staff contact. Possible risk: Illegal activity inside the room. 13. Suspicious Phone Activity Examples: Constant secret phone calls Taking photos of security areas. Possible risk: Information gathering. 14. Aggressive Behavior Toward Staff Shouting, threatening, or intimidating employees. Possible risk: Potential violence. 15. Sudden Exit After Observation Leaving immediately when security observes them. Possible risk: Criminal intent detected. DKB Ramu GC Kamal Senior Security Officer #SecurityLeadership #SecurityProfessionals #SituationalAwareness #RiskManagement #OperationalExcellence #PeopleProtection #StayVigilant #SecurityOps #IncidentResponse #DkbRamugckamal #RamuGCKamal #NepalToGlobal #TarakholaBaglungNepal #PanyuboteKanxa #Cocodemerhotel #Blackparrotsuites 🇸🇨🇳🇵🙏🇸🇨🇳🇵

🚨 Insights from HSE Offshore Inspection Reports (2019-2024) 🚨 At Salus Technical, we’ve been examining HSE offshore inspection letters over the past five years. Usually, these are kept private and are missed opportunities to learn. This post highlights some key findings related to alarm management offshore: 🔹 High Numbers of Standing Alarms An inspection found over 1200 standing alarms, many redundant or out-of-service. Action Required: Conduct a comprehensive audit of all current alarms. Decommission unnecessary alarms and establish a recurring review cycle every six months to maintain alarm system integrity. 🔹 Alarm Floods and Nuisance Alarms An inspection revealed peaks of up to 282 alarms in a 10-minute span, overwhelming operators. Action Required: Revise the alarm rationalisation strategy to reduce obsolete and excessive alarms, enhancing system efficiency. 🔹 Lack of Alarm Prioritisation and Poor HMI Design No effective prioritisation was found on an inspection, risking oversight during critical periods. Action Required: Overhaul HMI designs and prioritisation to support better operator response and usability. 🔹 Inadequate Alarm Management and Monitoring Persistent standing alarms with insufficient monitoring were noted. Action Required: Implement and track KPIs for alarm management to foster continuous improvement. 🔹 Alarm System Usability Issues Found inconsistent prioritisation and deficient alarm information, reducing effective operator responses. Action Required: Update alarm management philosophy to include Human Factors design criteria, enhancing operator situational awareness. 🤔 What other topics would you be interested in hearing about? Let me know in the comments! #processsafety #alarmmanagement #hseletters #salustechnical #foi

That is an insightful post; thank you for elevating this conversation. From a Cyberpsychology and Forensic Cyberpsychology standpoint, human-centered risk is fundamentally a behavioral challenge before it is a technical one. Controls and security awareness training remain vital "hygiene," but they address only the how of an attack. To outpace the threat, it's crucial to delve into the why, including cognitive biases, emotional triggers, and social dynamics that drive individuals to become inadvertent or deliberate threat actors. In practice, this means enhancing traditional SOC telemetry with what my field refers to as behavioral threat intelligence (BTI). By integrating digital forensics artifacts (logins, file movements, anomaly scores) with empirically validated behavioral markers, we can surface intent before it manifests as harm. Models such as the Adversary Behavior Analysis Model (ABAM) and the Cyber Forensics Behavioral Analysis" (CFBA) framework operationalize this fusion, enabling security teams to: - Profile motivation (grievance, ideology, profit, curiosity) rather than relying solely on role‑based access assumptions. - Detect cognitive fatigue or moral disengagement in employees, early indicators of risky click paths, and policy violations. - Map social engineering pressure points by analyzing how attackers exploit trust dynamics inside supply‑chain and hiring workflows. It's essential to tailor interventions (such as coaching, peer support, or investigative escalation) proportionate to both the technical severity and psychological drivers. This personalized approach is key to effectively managing cybersecurity risks. When we treat human risk as a continuum of behavioral signals rather than a binary of compliant versus malicious, we create response playbooks that are preventative, proportionate, and humane. The outcome is a workforce that is not merely "aware" but actively engaged in its cyber resilience. That culture, more than any single control, is what closes today's widening gap between threat velocity and organizational readiness. #Cyberpsychology #ForensicCyberpsychology #BehavioralThreatIntelligence #HumanCentricSecurity #CognitiveSecurity #InsiderThreats #HumanRisk #CyberBehavioralScience #SecurityAwareness #IntentBasedDefense #CyberResilience #SecurityCulture #ThreatModeling #DigitalForensics #CybersecurityLeadership #NeurodiversityInSecurity #CyberDeception #AdaptiveDefense #DarkTriadAnalysis #BehavioralAnalytics Landon W. Prof. Mary Aiken

Alert fatigue undermines SOC effectiveness by overwhelming analysts with noise. To reduce false positives and optimize detection coverage, implement a structured, metric-driven tuning cycle: 1. Unique Analytic Identification - Ensure every detection rule carries a globally unique identifier. Embed this ID and the analyst’s final disposition (True Positive / False Positive) in each alert record. 2. Weekly Accuracy Reporting - Retrieve all resolved alerts on a weekly cadence. - Group records by alert ID to determine total firings per analytic. - Within each group, calculate the ratio and count of true versus false positives. - Produce comparative charts (e.g., stacked bars) to highlight high-volume and low-accuracy alerts. 3. Impact-Driven Prioritization - High Volume + Low Accuracy Example: Alert C fires 125 times but yields only 20 true positives (84% FP rate). Action: Refine detection logic, introduce additional context enrichment (threat intelligence feeds, user-/asset-based whitelisting), or consider rule deactivation if not business-critical. - High Volume + High Accuracy Example: Alert A fires 200 times at 90% true-positive rate. Action: Investigate upstream preventive controls (network segmentation, endpoint hardening) to reduce true detections at the source. - Low Volume + High Accuracy Example: Alert D fires 10 times with 100% accuracy. Action: Validate that tuning has not inadvertently introduced false negatives; maintain existing configuration. 4. Supplementary Metrics for Continuous Improvement - Mean Time to Triage (MTTT): Monitor triage latency to identify process bottlenecks. - False Negative Identification: Correlate incident post-mortems with missing alerts to uncover blind spots. - Automation Potential: Leverage enrichment playbooks and SOAR workflows to auto-close low-risk false positives or accelerate context gathering. 5. Institutionalizing the Tuning Lifecycle - Weekly SOC Briefings: Present alert-accuracy dashboards and tuning progress to stakeholders. - Quarterly Reviews: Reassess critical use cases, adjust thresholds based on evolving threat patterns, and validate rule efficacy against recent adversary behaviors. - Tuning Standard Operating Procedure: Maintain a living document that captures best-practice tuning techniques (e.g., threshold calibration, enrichment integration, correlation rule templates). By embracing this structured tuning methodology, SOCs can systematically reduce false-positive noise, accelerate genuine incident identification, and allocate analyst capacity toward proactive threat hunting rather than reactive noise management.