What's blocking your financial AI team?
Your fraud and credit AI looks like it's working. You don't know what it's getting wrong.
Surface failures in production traces before they reach customers or regulators, including wrong decisions, missed fraud patterns, and outputs that would fail regulatory review.
Compliance and risk review is required, but it happens in spreadsheets and over Slack.
Route the right traces to the right reviewers with shared context and rubrics. Feedback comes back structured and recorded, not scattered across inboxes.
Regulatory audits require a traceable record of AI decisions and reviews. Most teams don't have one.
Every failure found, review completed, and fix validated is recorded in a single audit-ready trail. When regulators ask, the answer is already documented.
What DataFramer does for financial AI teams
Find what your models are getting wrong
Surface failures in fraud detection, credit decisioning, and AML systems that standard metrics miss: decisions that look correct until a compliance officer or customer challenges them.
Know where in the workflow it broke
When a failure surfaces, narrow down whether it came from the model, the features, the threshold logic, or the upstream data. In complex decisioning systems, the failure is often several steps back from where it shows up.
Structure compliance and risk review
Route specific cases to fraud analysts, compliance officers, or credit risk reviewers with the context they need. Reviews happen through a structured workflow with shared rubrics, and every judgment gets recorded.
Calibrate automated scoring against expert judgment
Automated evaluators need to reflect what your compliance and legal teams actually consider acceptable. DataFramer uses reviewed examples to keep automated scoring aligned with real expert judgment rather than drifting over time.
Validate model changes before they ship
When a model or threshold changes, test it against failures that have already been reviewed and validated. Model risk frameworks require proof that new versions don't break prior validated behavior.
Use cases
Fraud Model Monitoring
Find when your fraud detection starts missing patterns before customers or regulators surface them
Credit Decision Review
Route borderline credit decisions to risk reviewers with full trace context and document the outcome for regulatory records
AML Alert Review
Give BSA officers structured context for reviewing alerts rather than raw transaction data and gut feel
SR 11-7 Model Validation
Build a documented record of failure discovery, expert review, and validated fixes that satisfies independent model validation requirements
Complaint-Driven Investigation
When a customer disputes a decision, trace the AI reasoning quickly and document what the review found
Cross-Model Quality Monitoring
Track failure trends and quality scores across fraud, credit, and AML models in one place
Find what your financial AI is getting wrong.
Connect your production traces and see the failures your metrics are missing.
Common questions from financial AI teams
How does DataFramer fit into our model risk management process?
DataFramer gives model risk teams a structured way to find failures, route cases to independent reviewers, and document the review process. It builds the audit trail that SR 11-7 and similar frameworks require: failure discovery, expert review, and validated fixes recorded in one place.
Can compliance and risk reviewers use DataFramer without being AI engineers?
Yes. Reviewers get specific traces with shared context and clear questions to answer. They don't need to understand the model internals. Their judgment gets recorded in a form that flows directly into the model improvement process.
What financial AI systems does DataFramer work with?
DataFramer works with fraud detection systems, credit decisioning models, AML alert systems, and any other financial AI that produces traces. It integrates with LangFuse, LangSmith, and other observability tools.
How does DataFramer create an audit trail for regulators?
Every failure found, review completed, decision made, and fix validated is recorded in DataFramer. When regulators ask for documentation of your model oversight process, the record is already there.
Does DataFramer deploy on-premise for data that can't leave our environment?
Yes. DataFramer deploys inside your own cloud or on-prem infrastructure. Production traces and model outputs stay within your governance boundary.
How does DataFramer help with models that affect credit decisions covered by FCRA or ECOA?
DataFramer helps teams find and document AI decisions that look borderline or inconsistent, route them to compliance review, and maintain a record of how each case was reviewed and resolved. That documentation supports the explainability and oversight requirements these regulations create.