Risk and Fraud Scoring Model Factory for Financial Services

VigourSoft builds an end-to-end model pipeline on Amazon SageMaker, covering feature engineering, automated training pipelines, model registry and versioning, and deployment to production endpoints, then operates ongoing model monitoring, drift detection, and scheduled retraining as a managed service. This suits credit risk scoring, claims fraud detection, or policy renewal scoring, so the data science team can focus on new models instead of pipeline maintenance.

Scope of Work:

• Use-case and data-source assessment, feature engineering pipeline design
• SageMaker Pipelines setup for automated training, evaluation, and registration
• Model deployment automation (real-time or batch endpoints) with approval gating
• Model monitoring, drift detection, and CloudWatch alerting configuration
• Ongoing managed retraining schedule and governance reporting

Deliverables:

• Deployed SageMaker model pipeline (training, registry, deployment)
• Model monitoring and drift-detection dashboards
• Governance and model-versioning documentation
• Monthly managed-service retraining and performance report

Target Audience: Banks, insurers, and lenders with one or more risk or fraud scoring models already in production, or close to it, who need a repeatable pipeline and ongoing monitoring instead of manually retraining and redeploying models on an ad hoc basis.

Outcome Summary: Models move through a standardized SageMaker pipeline from training to production, with drift detection and monitoring running continuously and retraining happening on a managed schedule, so model performance does not quietly degrade between data science team check-ins.