Fraud doesn’t always look suspicious, until you analyse the patterns. I recently worked on a Vehicle Insurance Claim Fraud Analysis case study where the goal was simple: 👉 identify hidden risk signals and process gaps that allow fraudulent claims to slip through. 🔍 The problem Insurance teams deal with thousands of claims, but: • Fraud is often buried inside normal-looking transactions • Manual checks miss systemic weaknesses, not just bad actors • High-risk signals are rarely quantified clearly So I approached this as a real-world fraud analyst, not just a dashboard exercise. 🛠️ What I did (end-to-end) Using Excel analysis, I: • Cleaned and structured claim-level data • Defined fraud-focused KPIs, not vanity metrics • Built an interactive dashboard with slicers to simulate decision-making 📊 Core KPIs tracked • Fraud Rate (%) • Total Fraud Cases • Average Driver Risk Rating (Fraud) • Police Report Gap (%) - fraud claims filed without police reports 🚨 Key insights discovered • Fraud concentration varied strongly by vehicle price band, not just volume • Certain vehicle categories consistently showed higher fraud exposure • An alarming 97%+ of fraudulent claims lacked police reports, highlighting a major verification gap • Driver risk ratings provided early signals that could support preventive controls These insights aren’t theoretical, they point directly to where insurers are leaking money. 💡 Business impact (how this helps) This type of analysis helps organisations: • Flag high-risk claims earlier • Strengthen fraud prevention rules • Improve compliance and documentation checks • Reduce payout losses without increasing manual workload 🧠 Tools & skills applied Excel (Advanced Pivot Analysis) Fraud & Risk Analytics Dashboard Storytelling Business Intelligence Thinking I’m building and sharing real fraud analytics case studies as I continue to grow in this space, focusing on credit card, insurance, and transaction fraud. If you’re a: • Fraud / Risk Manager • Insurance or FinTech team • Founder needing data-driven fraud insights I’m open to conversations and collaboration. #FraudAnalytics #FraudDetection #RiskAnalytics #BusinessIntelligence #ExcelDashboard #DataAnalytics #BuildInPublic

wow this is very insightful

Healthcare Revenue Recovery Analysis — Scheduling Optimization Dashboard

A financial-impact analysis modeling how scheduling optimization could recover lost revenue for a healthcare provider losing income to no-shows and unfilled appointment slots.

I broke down a $4.2M annual revenue gap into specific recovery levers, SMS reminders, predictive scheduling, patient outreach, waitlist fill, and provider-schedule optimization and visualized each one's contribution in a waterfall chart to show exactly where the money could be recovered.

Key findings: • ~$2.9M (69% of the gap) identified as recoverable • 340% projected ROI with an 8-month payback period • Operational efficiency modeled to rise from 72% to 91% • Cost savings mapped across overtime reduction ($340K), idle-capacity recovery ($890K), and patient-acquisition offset ($220K)

Built as a clear, executive-ready dashboard that turns operational data into a single revenue story leadership can act on. Projections were modeled from operational simulation scenarios and historical no-show patterns.