Title: Student Placement & Analytics Dashboard Design

Description: Colleges sit on goldmines of student data — but most of it lives in spreadsheets nobody reads.

I design custom student placement dashboards built around your institution's actual data, giving decision-makers a clear, filterable view across gender, branch, and city tier — all in one place.

What you get:

Custom dashboard built on your data

KPI overview — placement rate, CGPA, student count, backlogs

Breakdown views by gender, branch, city tier

Interactive filters for deeper analysis

Clean, presentation-ready design

Perfect for colleges and universities that want to present placement data professionally — to students, parents, and accreditation bodies.

After Purchase: Hey, thanks for your order! Here's what happens next:

I'll reach out within 24 hours to kick things off

Share your student dataset with me and any specific metrics you want highlighted

I'll design your custom dashboard and share it for feedback

You get 2 rounds of revisions to make sure it's perfect

Typical turnaround: 3–5 business days

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