FeaturesUse CasesBlogAPI ReferenceWhy CorePlexMLPricing
Start Free
Financial Services

Fraud detection, credit scoring, and regulatory compliance at scale

Build production-grade ML models for fraud detection, credit risk scoring, AML transaction monitoring, and market risk modeling — all with built-in privacy compliance and audit trails.

The Challenges

Fraud Pattern Evolution

Fraudsters constantly adapt techniques, requiring models that retrain automatically as patterns shift.

Regulatory Compliance

PCI-DSS, SOX, and AML regulations demand auditable ML pipelines with full data lineage tracking.

Real-Time Scoring

Transaction decisions must happen in milliseconds while maintaining high accuracy and low false-positive rates.

Sensitive Data Handling

Credit card numbers, SSNs, and financial records require rigorous masking and encryption before model training.

How CorePlexML Helps

AutoML

AutoML Fraud Models

Train 50+ algorithms simultaneously with automatic feature engineering and stacked ensembles tuned for imbalanced fraud datasets.

Learn more
MLOps

Drift-Triggered Retraining

MLOps monitors prediction distributions and automatically retrains when fraud patterns shift, with canary deployments for safe rollouts.

Learn more
Privacy Suite

PCI-DSS Compliance

Privacy Suite detects and masks 72+ PII types including PANs and CVVs before data enters the training pipeline.

Learn more
SynthGen

Synthetic Transaction Data

Generate realistic synthetic financial records with SynthGen to augment rare fraud patterns without exposing real customer data.

Learn more

SDK Example

example.py
from coreplexml import CorePlexMLClient

client = CorePlexMLClient(
    base_url="https://api.coreplexml.io",
    api_key="sk_your_api_key"
)

# Scan dataset for PCI-DSS compliance
scan = client.privacy.scan(
    dataset_version_id="dsv_transactions_q1",
    compliance_profile="PCI_DSS"
)

# Train fraud detection model
experiment = client.experiments.create(
    project_id="proj_fraud",
    dataset_version_id="dsv_transactions_q1",
    target_column="is_fraud",
    max_models=30,
    max_runtime_secs=600
)

# Deploy with canary strategy
deployment = client.deployments.create(
    project_id="proj_fraud",
    model_id=experiment["leader_model_id"],
    strategy="canary",
    traffic_percentage=10
)

Expected Impact

99.2%
Fraud Detection Rate
67%
False Positive Reduction
<15 min
Model Retraining Time
85% faster
Compliance Audit Prep

Ready to get started?

Try CorePlexML free — no credit card required. Train your first model in under 10 minutes.

Start FreeRead the Docs