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Flowlet AI/ML — Fraud Detection Model Performance

Dataset: 1.2 million synthetic + real-pattern transactions spanning Jan 2022 – Dec 2023. Fraud prevalence: 3.1% (class-imbalanced; SMOTE oversampling applied during training). Train/test split: 80/20 stratified. All metrics reported on the held-out 20% test set.

Executive Summary

Model AUC-ROC Precision Recall F1 Inference (ms) Recommended
Random Forest 0.974 96.1% 94.8% 95.4% 4 Standby
XGBoost 0.981 96.7% 95.3% 96.0% 3 Standby
LightGBM 0.978 96.3% 95.0% 95.6% 2 Standby
Neural Network (LSTM) 0.969 94.4% 93.7% 94.0% 18 Standby
Ensemble (Stacking) 0.987 97.3% 96.1% 96.7% 8 ✅ Active
Baseline (Logistic Reg.) 0.921 88.4% 85.2% 86.8% <1 Baseline

The Stacking Ensemble (XGBoost + LightGBM + Random Forest meta-learner) is deployed in production and delivers the best AUC-ROC with an acceptable 8 ms inference latency.

1. Ensemble Model — Full Tearsheet

Confusion Matrix (240,000 test transactions)

                 Predicted Fraud   Predicted Legit
Actual Fraud       7,248 (TP)         289 (FN)
Actual Legit         204 (FP)      232,259 (TN)
Metric Value Formula
Precision 97.3% TP / (TP + FP)
Recall / Sensitivity 96.1% TP / (TP + FN)
Specificity 99.9% TN / (TN + FP)
F1 Score 96.7% 2·P·R / (P + R)
False Positive Rate 0.09% FP / (FP + TN)
False Negative Rate 3.83% FN / (FN + TP)
Matthews Corr. Coeff. 0.966 —

AUC-ROC Curve (key operating points)

FPR (False Positive Rate) TPR (True Positive Rate)
0.001 0.812
0.005 0.923
0.009 0.961 ← Production threshold
0.020 0.981
0.050 0.993

AUC-ROC = 0.987 — Operating at FPR = 0.09% captures 96.1% of all fraud.

Precision-Recall Curve

Threshold Precision Recall F1
0.30 88.1% 98.9% 93.2%
0.50 93.7% 97.8% 95.7%
0.62 97.3% 96.1% 96.7% ← Production
0.75 98.8% 91.2% 94.8%
0.90 99.4% 79.3% 88.2%

Average Precision (AP) = 0.979

2. Individual Model Benchmarks

2.1 XGBoost

  • AUC-ROC: 0.981
  • Training time: 4.2 minutes (CPU, 960k rows)
  • Key hyperparameters: n_estimators=500, max_depth=7, learning_rate=0.05, scale_pos_weight=31 (fraud:legit ratio)

Top 10 Feature Importances (SHAP values)

Rank Feature Mean SHAP
1 velocity_24h 0.182
2 amount_zscore 0.171
3 amount 0.148
4 new_device 0.112
5 high_risk_merchant 0.094
6 unusual_time 0.079
7 new_location 0.071
8 user_age_days 0.063
9 velocity_1h 0.051
10 transaction_count_30d 0.029

2.2 LightGBM

  • AUC-ROC: 0.978
  • Training time: 1.8 minutes (CPU)
  • Notable: Fastest inference (2 ms) — suitable for ultra-low-latency fallback.

2.3 Random Forest

  • AUC-ROC: 0.974
  • Training time: 8.1 minutes (CPU)
  • n_estimators=300, max_depth=15, class_weight="balanced"

2.4 Neural Network (Bi-LSTM)

Architecture: Input(18) → LSTM(128) → Dropout(0.3) → Dense(64, ReLU) → Dense(1, Sigmoid)

  • AUC-ROC: 0.969
  • Training time: 23 minutes (GPU T4, 50 epochs)
  • Highest recall on sequential fraud patterns (card-not-present chains), but 18 ms latency makes it secondary in the ensemble.

3. Robustness & Stability

Walk-Forward Validation

The ensemble was retrained monthly on a 12-month rolling window and evaluated on the following month's unseen data:

Period AUC Precision Recall
Jan–Dec 2022 → Jan 2023 0.984 97.1% 95.8%
Feb–Jan 2023 → Feb 2023 0.986 97.4% 96.0%
Mar–Feb 2023 → Mar 2023 0.981 96.8% 95.5%
Apr–Mar 2023 → Apr 2023 0.988 97.6% 96.4%
May–Apr 2023 → May 2023 0.985 97.2% 95.9%
Average 0.985 97.2% 95.9%

Minimal performance degradation across periods confirms stability and absence of overfitting.

Class Imbalance Sensitivity

Technique AUC F1
No balancing 0.964 93.1%
Class weights 0.978 95.4%
SMOTE 0.981 96.0%
SMOTE + class weights 0.987 96.7% ← Used in production

Transaction Cost Sensitivity

Cost per FP ($) Cost per FN ($) Expected cost per 100k txns
2 50 $1,852
2 200 $6,378
5 50 $1,918
5 200 $6,444 ← Production cost estimate

At the production threshold (0.62), the model minimises expected financial cost under the $5 FP / $200 FN loss matrix.

4. Fairness & Bias Analysis

To ensure the model does not discriminate by demographic proxies:

Subgroup FPR FNR
Transaction amount < $100 0.08% 3.91%
Transaction amount ≥ $100 0.10% 3.74%
Domestic transactions 0.09% 3.88%
International transactions 0.11% 3.61%
New accounts (< 30 days) 0.14% 4.02%
Established accounts (> 1 year) 0.07% 3.64%

FPR and FNR are consistent across all analysed subgroups (max delta: 0.07 pp FPR, 0.41 pp FNR), indicating no systematic bias.

5. Operational Metrics

Metric Value
Avg inference latency (p50) 8 ms
Inference latency (p99) 22 ms
Throughput 12,000 txns/sec (single node)
Memory footprint 310 MB
Model file size 48 MB
Retraining frequency Weekly (auto-triggered if AUC drops below 0.97)
Feature pipeline (Redis cache) p50 = 1.2 ms

6. Limitations & Caveats

  1. Concept drift: Financial fraud patterns evolve. The weekly retraining pipeline monitors AUC via a reference window; if AUC drops below 0.970 on live traffic, an alert is triggered.
  2. Novel attack vectors: The model may underperform on attack types not represented in the training distribution. The rule engine (247 hand-crafted rules) acts as a safety net.
  3. Synthetic data component: ~40% of training fraud labels were synthetically generated using a GAN. Real-world fraud rates and patterns may differ in unseen markets.
  4. Not a substitute for human review: All transactions scored 0.50–0.70 are routed to the human review queue; the model is decision-support, not fully autonomous.