Credit and Fraud Risk Models
Credit scoring, early-warning, and fraud detection models are built with documented features, challenger testing, and explainability, so model risk teams can validate them against SR 11-7 expectations.
Industries
Banking & Financial Services
Sharper Risk Insight With the Audit Trail Built In
For banks, credit unions, lenders, and investment firms, we build the data platforms, risk models, and customer analytics that regulators and business leaders both need to trust. Lineage, controls, and documentation are designed in from day one, not added before an exam.
What We Deliver
Where We Make the Difference
Financial institutions hold rich data, but it sits in core banking, loan origination, card, and CRM systems built decades apart. Regulatory reporting consumes analyst time, and new models face long validation cycles. We build a governed data foundation first, so reporting, risk, and customer analytics draw from the same audited numbers.
Regulatory Reporting Automation
Call reports, capital and liquidity calculations, and stress testing data come from governed pipelines with lineage back to source, reducing manual reconciliation and giving examiners a clear trail.
Customer Analytics and Retention
A unified customer view across deposits, lending, cards, and digital channels feeds churn prediction and next-best-offer models that relationship managers and marketing teams can act on.
Core Data Modernization
API layers and change data capture move data out of legacy core systems into a modern cloud platform, opening it to analytics and new applications without a risky core replacement.
Our Approach
How We Deliver Banking & Financial Services
Step 1: Start With Regulatory Context
We review your regulatory obligations, examination findings, data landscape, and strategic priorities to find the investments that serve compliance and growth together.
Step 2: Design for Lineage and Audit
The data architecture, from lakehouse to regulatory data marts, is designed with lineage, access controls, and reconciliation checks from the very first pipeline.
Step 3: Build With Validation in Mind
Risk models, regulatory reports, and customer analytics are developed with test evidence and documentation at every stage, ready for your model risk and internal audit reviews.
Step 4: Operate Under Governance
Deployed solutions run with model monitoring, data quality alerts, and change controls that keep them within policy as data, products, and regulations shift.
Can you work within our model risk management policy?
Yes. We document data sources, features, assumptions, testing, and limitations in the format your model risk team uses, and we build challenger models and monitoring so validation has what it needs. Your validators stay independent, and we support their review.
Do we need to replace our core banking system to modernize our data?
No. We extract data from the core through APIs, change data capture, or batch files and land it in a modern platform, leaving the core in place. That lets you modernize analytics and reporting without waiting for a core conversion.
How do you handle security for sensitive customer data?
We build on your approved cloud platform with encryption, private networking, role-based access, and logging aligned to your security policies and FFIEC guidance. Sensitive customer data is masked or tokenized in development and test environments.
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