Fraud Analytics & Risk Scoring Dashboard

Project Overview

This project demonstrates an end-to-end fraud analytics and risk scoring workflow, from raw transactional data through rule evaluation, account-level risk scoring, and an operational summary dashboard.

The goal of the project is to replicate how fraud and risk teams:

• Monitor suspicious activity using rules
• Classify severity (HIGH / MED)
• Aggregate risk at the account and customer level
• Surface actionable insights for fraud operations and investigations

The final output is a Fraud Operations Summary dashboard designed for analysts, risk managers, and decision-makers.

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Data Model

The project follows a star-schema-style layout with clear separation between dimensions, facts, and curated outputs.

Dimension Tables (data/raw)

• dim_customers.csv – customer identifiers and attributes
• dim_accounts.csv – account-level identifiers and metadata
• dim_merchants.csv – merchant reference data
• dim_devices.csv – device identifiers and attributes

Fact Tables (data/raw)

• fact_transactions.csv – transaction-level data including international indicators
• fact_login_sessions.csv – authentication and login session activity
• fraud_rule_hits.csv – fraud rule triggers with severity and timestamps

Curated Outputs (data/curated)

• account_risk_scores.csv – account-level risk scoring output derived from transactional and rule activity

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Fraud Rules & Severity

Fraud rules are evaluated at the transaction level and recorded in fraud_rule_hits.

Each rule hit includes:

• rule_code
• rule_severity (HIGH / MED)
• hit_ts (timestamp of rule trigger)
• Associated account_id and customer_id

Severity is used throughout the analysis to differentiate:

• High-confidence fraud signals
• Medium-risk or investigatory signals

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Risk Scoring Logic

Account-level risk is computed in the curated layer (Account_Risk_Scoring) using:

• Frequency of fraud rule hits
• Severity of triggered rules
• Transaction behavior patterns (e.g., international activity rate)
• Recency of suspicious activity

Key outputs include:

• risk_score – composite numeric risk score
• risk_tier – HIGH / MED / LOW classification
• last_txn_ts – most recent transaction timestamp
• last_hit_ts – most recent fraud rule trigger

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Summary Dashboard

The Fraud Operations Summary dashboard provides a consolidated operational view of fraud activity.

Dashboard Sections

1. Executive Fraud & Risk Overview
   High-level KPIs summarizing transaction volume, declines, rule hits, and severity distribution.

2. Fraud Rule Activity Over Time
   Rule hit volume across recent monitoring windows (24 hours, 7 days, 30 days).

3. Highest Risk Accounts (Model Output)
   Accounts ranked by composite risk score and risk tier.

4. Fraud Rule Effectiveness & Severity Breakdown
   Rule-level performance showing total hits and severity mix.

5. Account-Level Rule Activity
   Accounts generating the highest volume of fraud alerts, including recency and rule diversity.

6. Customer-Level Rule Activity
   Aggregated fraud rule activity across customers for exposure analysis.

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Screenshots

The following screenshots illustrate key dashboard views:

• summary_kpis.png
• top_risk_accounts.png
• rule_effectiveness.png
• account_rule_activity.png
• customer_rule_activity.png
• fraud_rule_activity_over_time_.png

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SQL Structure

The SQL folder reflects a production-style analytics pipeline:

sql/ ├── 00_schema/ │ └── 001_create_tables.sql ├── 10_staging/ │ └── stg_transactions.sql ├── 20_rules/ │ └── rule_severity_logic.sql ├── 30_scoring/ │ └── account_risk_scoring.sql ├── 40_reporting/ │ └── fraud_summary.sql

This structure separates schema creation, staging logic, rule logic, scoring, and reporting.

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Tools & Skills Demonstrated

• Fraud analytics & rule-based detection
• Risk scoring and tiering
• Data modeling (fact / dimension design)
• Google Sheets for analytical prototyping
• SQL for schema, scoring, and reporting logic
• Dashboard design for fraud operations
• Data export and project packaging for analytics portfolios

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How to Use This Project

1. Review raw and curated datasets in the data/ directory
2. Examine scoring and reporting logic in the sql/ directory
3. Open the dashboard file in dashboards/ to explore fraud insights
4. Reference screenshots for a quick visual overview

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Notes

This project is designed as a portfolio demonstration of fraud analytics concepts and workflows.
All data is synthetic and intended for educational and professional showcase purposes only.