Adversarial Testing for Fraud Detection Models
Upload your model, probe its decision boundaries, and generate targeted training data to eliminate blind spots before adversaries find them.
Model Blind Spots Are a Revenue Risk
Fraud detection models degrade as adversaries learn their boundaries
Training Data Bias
Models trained on historical fraud see only past attack patterns. Novel fraud vectors exploit the distributional gaps your training data never covered.
Decision Boundary Blind Spots
Fraud classifiers have thin regions where confidence drops sharply. Adversaries craft transactions that land precisely in these uncertain zones.
Adversarial Drift
Fraudsters adapt. A model with 99.5% precision today may miss 40% of next quarter's attacks as schemes evolve to exploit learned decision boundaries.
Limited Negative Samples
Real fraud is rare — typically 0.1% to 0.5% of transactions. Oversampling techniques like SMOTE don't capture the structural logic of novel fraud schemes.
How It Works
Upload ONNX, probe boundaries, get results — three API calls
Upload Your Model
Export your fraud detection model as ONNX. VynFi supports classification and anomaly detection architectures — XGBoost, LightGBM, neural networks, isolation forests.
Probe Decision Boundaries
DataSynth 3.0 generates synthetic transactions along your model's decision boundary using gradient-guided perturbation. Identifies regions of low confidence and high misclassification risk.
Get Adversarial Results
Receive a boundary analysis report with identified blind spots, confidence distributions, and a targeted augmentation dataset to retrain and harden your model.
Adversarial Capabilities
Perturbation Probing
Systematic feature-space exploration that generates transactions just inside and outside your model's decision boundary. Each perturbation respects financial constraints — amounts balance, dates are sequential, entity relationships hold.
Boundary Analysis
Quantitative mapping of your model's decision surface. Identifies the feature combinations where the classifier is most uncertain, ranked by exploitation likelihood and financial impact.
Targeted Augmentation
Generates labeled training data concentrated in your model's weak regions. Each augmentation batch is structurally valid and causally coherent — not just statistical noise injected around existing samples.
Evasion Simulation
Simulates adversarial actors who iteratively modify transaction attributes to evade detection. Tests your model against progressive evasion strategies: amount splitting, temporal spreading, and entity substitution.
Python SDK Example
import vynfi
client = vynfi.Client(api_key="vf_live_...")
# Upload your fraud detection model
model = client.adversarial.upload_model(
path="fraud_detector.onnx",
model_type="binary_classifier",
feature_schema="transaction_v2",
)
# Probe decision boundaries
probe = client.adversarial.probe(
model_id=model.id,
perturbation_budget=0.15,
n_probes=10_000,
constraint_set="financial_transactions",
)
print(f"Blind spots found: {probe.n_blind_spots}")
print(f"Avg boundary confidence: {probe.avg_confidence:.3f}")
# Generate augmentation data for weak regions
augmentation = client.adversarial.augment(
probe_id=probe.id,
target_regions=probe.top_blind_spots(k=5),
samples_per_region=2_000,
)
augmentation.download("augmentation_data.parquet")Find blind spots before adversaries do
Probe your fraud detection model against structurally valid adversarial data. 5,000 free credits to start.