Build a comprehensive fraud monitoring dashboard that enables payments teams to track transaction health, identify fraud patterns, and manage chargeback disputes across merchant categories, channels, and device types. The solution provides real-time visibility into fraud rates, approval metrics, and representment outcomes to reduce financial losses and improve merchant confidence.
This project uses a star schema architecture to efficiently model transaction, fraud alert, and chargeback data with dimensional attributes for detailed analysis.
Core transaction events with outcomes
Fraud alerts & decisions
Dispute outcomes
Customer profiles
Device attributes
Merchant info
DAX calculations
Time hierarchy
Transaction channels
High-level business metrics tracking transaction approval health, fraud exposure, and chargeback impact across all channels and merchants.
| Top Merchant | Loss Amount | Category | Risk Tier |
|---|---|---|---|
| HighRollr Gaming | $0.4K | Gaming | High |
| SkyJet Travel | $0.25K | Travel | Medium |
| Streaming Plus | $0.18K | Digital | Low |
Key Insight: Gaming is the largest loss driver category, accounting for 44% of total losses. The top merchant (HighRollr Gaming) contributes $0.4K in losses, indicating concentrated risk in high-value, high-risk merchants.
Detailed fraud analysis by channel, authentication method, device type, and fraud score bands. Enables identification of high-risk transaction patterns and authentication effectiveness.
| Fraud Score Band | Transaction Count | Loss Amount | Fraud Confirmed % |
|---|---|---|---|
| 0-20 (Low) | 8,420 | $0.9K | 2.1% |
| 20-40 (Medium) | 3,210 | $1.2K | 5.3% |
| 40-59 (High) | 1,890 | $3.4K | 18.7% |
| 60+ (Critical) | 420 | $2.1K | 45.2% |
Key Insights: CNP channel shows 26% higher fraud rate than CP, suggesting authentication weaknesses in card-not-present transactions. 3DS authentication significantly reduces fraud exposure. Fraud Score Band 40-59 shows the highest loss concentration, with 18.7% of transactions confirmed fraudulent.
Chargeback volume, loss impact, reason codes, and representment success rates. Tracks merchant disputes and recovery outcomes.
| Reason Code | Chargebacks | Loss | Win Rate |
|---|---|---|---|
| NoAuth | 128 | $3.1K | 42% |
| Cancelled | 94 | $2.3K | 60% |
| Duplicate | 78 | $1.9K | 55% |
| Other | 49 | $1.3K | 38% |
Key Insights: 349 total chargebacks resulted in $8.6K net loss. 56.73% representment rate indicates room for improvement in dispute contestation. Gaming category leads in chargeback losses ($4.2K). Win rates vary significantly by reason code: Cancelled disputes show 60% win rate while NoAuth disputes only achieve 42%.
Gaming merchants account for the highest fraud and chargeback losses (44% of total), suggesting category-specific risk patterns and need for tailored controls.
Card-not-present transactions show 26% higher fraud rate than card-present. Authentication strength varies by channel, favoring in-person transactions.
3DS authentication significantly reduces fraud exposure (26% reduction). Merchants not implementing 3DS face elevated fraud risk in CNP environments.
56.73% representment rate suggests 43% of chargebacks are not being contested. Improved documentation and evidence collection could recover significant losses.
Mobile devices show 23% higher fraud rate (0.32%) vs. desktop (0.26%). Device fingerprinting and behavioral analysis could improve mobile fraud detection.
Fraud Score Band 40-59 has the highest loss density (18.7% fraud confirmation). Fine-tuning decision thresholds in this band could optimize revenue-risk tradeoff.
Key calculations built with DAX to power dashboard interactivity and analytical insights across all dimensions.
Alert Rate =
DIVIDE(
COUNT(fact_alert[alert_id]),
COUNT(fact_transaction[txn_id]),
0
)
-- Tracks percentage of transactions triggering fraud alerts
-- Used in Executive Overview and Trend Analysis
Approval Rate =
DIVIDE(
COUNTROWS(
FILTER(fact_transaction, fact_transaction[auth_result] = "approved")
),
COUNTROWS(fact_transaction),
0
)
-- Measures successful transaction authorization rate
-- Key health metric for payment processing
Chargeback Win Rate =
DIVIDE(
COUNTROWS(
FILTER(fact_chargeback, fact_chargeback[cb_outcome] = "won")
),
COUNTROWS(fact_chargeback),
0
)
-- Tracks representment success rate across reason codes
-- Enables reason code-specific strategy optimization
Fraud Rate =
DIVIDE(
COUNTROWS(
FILTER(fact_transaction, fact_transaction[fraud_confirmed] = TRUE())
),
COUNTROWS(fact_transaction),
0
)
-- Sliceable by customer segment, device type, merchant category, channel
-- Enables drill-down analysis to identify high-risk patterns