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Understanding the AI and statistical models powering FinSeek
FinSeek uses an ensemble approach with a 2-of-3 voting system. A transaction is flagged as fraud only when at least 2 models agree, dramatically reducing false positives.
Final_Flag = (Model1_Vote + Model2_Vote + Model3_Vote) ≥ 2Performance: Achieves <5 false positives and >130 true positives on test data
Optimized for minimal false positives - only flags when very confident
Where:
Ensemble of decision trees that vote to detect anomalous patterns
Where:
Random Forest builds multiple decision trees on different samples. Each tree learns different patterns. By voting together, they catch fraud that might slip past a single model.
Example: If 7 out of 10 trees say a $10,000 3am transfer is fraud, the ensemble flags it with 70% confidence.
Custom Threshold: Optimized for the 2-of-3 ensemble voting system
Optimized to catch maximum fraud - aggressive detection
Where:
Low Threshold: Set aggressively to maximize recall (catch more fraud)
Each transaction type has a learned coefficient that adjusts the fraud score. Positive values increase fraud likelihood, negative values decrease it.
Wire transfers and account-to-account movements
+5.0585
coefficient
ATM withdrawals and cash extraction
+3.0961
coefficient
Standard debit card purchases
-0.5993
coefficient
Bill payments and recurring charges
-9.5075
coefficient
Deposits and incoming transfers
0.0000
coefficient
How to read this: These coefficients are learned from historical fraud patterns. For example, TRANSFER transactions add +5.0585 to the fraud score, making them more likely to be flagged, while PAYMENT transactions subtract -9.5075, indicating they're typically legitimate.
Where:
R² measures how well the line fits historical data (0-1, higher = better fit)
Actual Implementation: Fits a straight line through historical quarterly fraud amounts. Extrapolates this trend forward with seasonal and growth adjustments (2.5% QoQ growth, Q4 +15% seasonal factor).
Where:
Low α (0.1-0.3): More weight on past → Stable, less reactive
High α (0.7-0.9): More weight on recent → Responsive to changes
Default: α = 0.2 provides steady predictions with some responsiveness
Actual Implementation: Applies exponential smoothing to historical fraud amounts, giving more weight to recent quarters while still considering historical patterns.
Short MA (3 periods): Captures recent changes
Long MA (6 periods): Shows overall trend
Positive Trend: Short > Long → Fraud increasing
Negative Trend: Short < Long → Fraud decreasing
Actual Implementation: Calculates both 3-quarter and 6-quarter moving averages. Projects forward by adding the detected trend to the recent average.