01
+37%
fraud catch rate
Real-time fraud detection
Streaming ML pipelines for card-not-present, ACH, wire, and synthetic-identity fraud. Two-tier ensembles with sub-50ms hot path, full audit logging, and analyst-facing case management.
AI & Machine Learning × Financial Services
AI for Financial Services.
Production-grade fraud detection, KYC orchestration, credit decisioning, AML, and capital-markets AI, engineered against PCI-DSS, SOX, FFIEC, and SR 11-7 model risk management. Audit logs and regulator-ready evidence by design.
- PCI-DSS Level 1
- Aligned
- SR 11-7 (MRM)
- Engineered
- SOX (S-OX 404)
- Aligned
- FFIEC
- Supported
The reality
Financial services AI stalls on model risk management, audit, and explainability , not on model quality.
The pattern across banks, lenders, and capital-markets firms: a model that performs in backtest, an MRM function that hasn't seen the documentation, an audit trail that captures inputs but not decisions, an SAR / STR generation flow that nobody can defend in front of an examiner, and a deployment that stalls in the second-line review for six months. The model isn't the problem; the operational discipline around it is.
Prosigns ships financial-services AI with model risk management as a first-class engineering concern. Every model lands with SR 11-7-aligned documentation: development methodology, performance metrics, validation results, and ongoing monitoring. Every decision logs the model version, feature snapshot, decision threshold, and downstream action. Audit trails survive examiner inquiry; second-line review pulls evidence in days, not weeks. The AI that ships in financial services is the AI that survives the regulator visit.
Where it ships
6 workloads, scoped to financial services.
Concrete applications where ai & machine learning unlocks measurable value inside financial services delivery constraints.
- 02
−47%
onboarding time
Multi-agent KYC orchestration
Identity verification, document classification, sanctions screening, and risk scoring orchestrated as a multi-agent system with explicit human-in-the-loop checkpoints.
- 03
Credit decisioning
Underwriting models with Reg B / ECOA-aware design, adverse action reasoning, and the explainability documentation regulators expect.
- 04
AML and transaction monitoring
AI-augmented AML with explicit alert reasoning, examiner-defensible alert tracing, and SAR / STR generation grounded in the underlying evidence.
- 05
Capital markets AI
Trading signal research, execution quality analytics, and post-trade reconciliation AI with the operational discipline market-making and prop trading require.
- 06
Document intelligence
Loan document extraction, contract intelligence, and regulatory filing analysis. Structured output with confidence scoring, human-in-the-loop review queues, and full audit lineage.
How we engage
4 phases, named in the SOW.
Each phase has a deliverable, an owner, and an acceptance criterion calibrated to financial services delivery.
- 01
Model risk frame and regulatory mapping
Discovery starts with the regulatory frame and second-line expectations: SR 11-7 alignment, OCC / FRB / FDIC examination patterns, state-level supervision requirements. Architecture decisions land against the regulatory frame in writing.
- 02
MRM-aligned engineering
SR 11-7-aligned documentation produced as a side-effect of building. Development methodology, performance metrics, validation results, and ongoing monitoring captured continuously. Second-line review co-authoring rather than retrofit.
- 03
Audit-defensible operations
Every prediction logs model version, feature snapshot, decision threshold, downstream action, and user identity. Drift monitoring against the eval dataset; explicit retraining cadence. Audit pulls run in days, not weeks.
- 04
Examiner-ready operating cadence
Continuous evidence collection for the next exam, not the one before. Quarterly model risk reviews with second line, monthly drift and performance triage, and IR plan rehearsed quarterly with named regulator-notification timeline.
Capabilities
What’s in scope.
- Real-time fraud detection: streaming ML, two-tier ensembles, analyst tooling
- KYC orchestration: multi-agent identity, sanctions, document, risk scoring
- Credit decisioning: Reg B / ECOA-aware models with adverse action reasoning
- AML and transaction monitoring with examiner-defensible alert tracing
- Document intelligence: extraction, classification, contract analysis
- Capital markets AI: signal research, execution analytics, post-trade
- SR 11-7 documentation produced continuously, not assembled at audit time
- Audit-trail tooling that survives OCC / FRB / FDIC / state-level examination
Stack
Tools we use in production.
- Foundation models
- Anthropic ClaudeOpenAI GPTAWS BedrockAzure OpenAI
- ML platform
- SageMakerVertex AIDatabricksMLflowWeights & Biases
- Streaming
- AWS KinesisApache KafkaApache FlinkConfluent
- Decisioning
- LightGBMXGBoostPyTorchCustom rule engines
- Identity & document
- OnfidoSumsubMitekCustom OCR pipelines
- Compliance & audit
- VantaDrataCustom evidence pipelinesSplunk
Compliance overlay
Frameworks we engineer to.
Every financial services engagement carries the evidence collection that procurement and audit teams expect on day one.
- 01
SR 11-7 Model Risk Management
Full SR 11-7 documentation produced as a side-effect of building: development methodology, conceptual soundness, performance metrics, validation results, ongoing monitoring, and inventory management. Second-line review co-authored rather than retrofit.
- 02
PCI-DSS Level 1
Cardholder-data scope minimized through tokenization, network segmentation, and explicit data-flow boundaries. Continuous-monitoring evidence pipeline supports QSA-led assessments without scrambling.
- 03
SOX (Sarbanes-Oxley) 404
Models with material financial impact (CECL, ALLL, fraud-loss reserves) ship with SOX-aligned change management, evidence, and the audit trail external auditors expect.
- 04
FFIEC IT examination handbook
FFIEC-aligned IT controls integrated with model deployment: change management, access management, business continuity, and information security.
- 05
Reg B / ECOA / Fair Lending
Credit-decisioning models engineered against ECOA / Reg B with adverse action reasoning, prohibited-basis variable handling, disparate-impact testing, and the documentation fair-lending exam reviews require.
Selected work
Quantified outcomes, not adjectives.
- 01Financial Services
+37%
fraud catch rateReal-time fraud detection for a US regional bank.
Replaced a rules-based engine with a streaming ML pipeline on AWS. Reduced false positives 42% while raising true catches. SR 11-7-aligned governance frame, regulator-ready audit logs.
9 months
- 02Financial Services
−47%
onboarding timeMulti-agent KYC and onboarding orchestrator for a US digital bank.
Five-agent workflow with explicit human-in-the-loop checkpoints. Full audit trail meets BSA / AML examination requirements. SAR / STR generation grounded in underlying evidence.
8 months
Where this fits
Open the parent practice or industry hub.
Common questions
Asked before the first call.
- 01
How do you handle SR 11-7 model risk management?
SR 11-7 documentation produced as a side-effect of building, not as a deliverable assembled before second-line review. Development methodology, performance metrics, validation results, and ongoing monitoring captured continuously. We co-author with the second-line MRM function rather than handing off a black box.
- 02
Can your models stand up to a regulator visit?
Yes: that's the design. Every prediction logs model version, feature snapshot, decision threshold, downstream action, and user identity. Drift monitoring runs continuously. Audit pulls run in days, not weeks. We've supported clients through OCC, FRB, FDIC, NYDFS, and state-level examinations.
- 03
How do you handle adverse action and ECOA?
Credit models engineered against ECOA / Reg B from architecture, not retrofitted. Adverse action reasoning generated from the model rather than overlaid post-hoc. Prohibited-basis variable handling enforced at the data layer. Disparate-impact testing in CI on every model change.
- 04
Can you operate the AI estate after launch?
Yes: through Managed Services. Model performance and drift monitoring, retraining cadence, second-line review support, and the operating discipline financial-services AI requires. Or we hand off to your team with the runbook and a 90-day shadow period.
- 05
What about generative AI and LLMs in financial services?
Production LLM deployment with SR 11-7-applicable governance, BAA-covered model endpoints (AWS Bedrock under HIPAA-eligible accounts where customer-data overlap exists), refusal patterns for compliance-sensitive content, and audit logging that captures inputs / outputs / citations / model version. We tell you when LLM is the wrong tool.
- 06
What does a financial-services AI engagement cost?
Discovery and risk modeling: 4–6 weeks, $80K–$200K. Production AI deployment with MRM frame: 6–12 months, $600K–$2M. Multi-use-case enterprise programs: $2M–$8M+. Managed Services: $50K–$250K monthly retainer. Brackets published honestly so visitors self-qualify before the first call.
Talk to us
Bring us a hard AI & Machine Learning problem. We’ll bring a senior Financial Services engineer.
A senior engineer plus the CORTEX department lead joins the first call, both with prior financial services delivery experience.