Financial Services transformation. Built for SR 11-7, not slideware.

Banking AI consulting, written to your model risk policy

Financial services AI consulting is the work of designing, building, validating, and monitoring AI systems for credit, fraud, AML, pricing, and operations inside a bank, asset manager, or insurer, governed to the regulations that examiners actually cite. The job is not to land a pilot. The job is to land a production system inside your model risk management framework with the validation paper already filed. Rockmere runs that work across credit decisioning, fraud investigations, AML alert review, capital markets back-office, and retail banking customer operations.

The frameworks we design to from day one include SR 11-7 (Federal Reserve), OCC 2011-12, OCC Bulletin 2013-29 on third-party risk, the CCAR stress framework, SOX 404 controls, GLBA / Reg P privacy, and GDPR where European data crosses the perimeter.

Most AI work in banks fails the same way: a working pilot in a sandbox VPC, an exec demo that lands, then six months of model risk meetings, vendor risk reviews, and SOX walk-throughs the original team never scoped. The system never reaches production. Financial services is not a different industry. It is a different physics. Every artifact gets audited, every model gets validated, every change gets a control mapping. We build for that on day one, so go-live day is not the day the real work starts.

What is SR 11-7, and how do we design for it?

SR 11-7 is the Federal Reserve’s supervisory guidance on model risk management, requiring banks to validate, monitor, and challenge any model that informs a business decision. OCC 2011-12 is the OCC’s matching guidance. Together they govern AI used in credit, fraud, AML, and pricing, and they are where most bank AI projects stall.

We treat SR 11-7 and OCC 2011-12 as build inputs, not as documentation passes. Every AI we land inside a bank carries:

• A model card and validation plan in week one, with a challenger model design by week four
• Pre-coordinated handoff to your second line of defense so validation does not become a six-month re-do
• Independent monitoring of conceptual soundness, ongoing performance, and outcome analysis under SR 11-7 paragraphs IV through VI
• OCC Bulletin 2013-29 third-party risk paper trail your TPRM team will accept, with foundation-model vendor concentration risk explicitly addressed
• CCAR-friendly model lineage when the AI touches stress-testing inputs or capital adequacy
• SOX 404 control mapping when the AI sits inside a financial reporting workflow
• GLBA, Reg P, Reg E, Reg Z, and UDAAP coverage for any AI touching consumer outcomes
• GDPR mapping for European customer data, with data residency held inside your VPC

We build inside your VPC, with your KMS, with your IAM. PHI, NPI, and PII never leave the perimeter. The retrieval design draws on our enterprise RAG consulting practice for policy lookups, regulator citations, and audit-evidence retrieval.

Where AI moves the P&L

Banking AI pays off in five places: fraud investigations, AML alert review, credit and underwriting, capital markets back-office, and retail customer operations. We focus the work where the math moves:

• Fraud investigations: handle-time and false-positive reduction, with disposition reasoning auditable per claim
• AML alert review: tier-1 disposition assist with SAR-quality narrative drafts that compliance signs
• Credit and underwriting: decisioning copilots with explainability documented to the model risk standard
• CCAR stress-test scenarios: production-grade challenger models and supporting documentation
• Capital markets back-office: reconciliation, exception triage, and risk reporting automation
• Retail customer operations: KYC onboarding flow, wire investigation triage, mortgage origination

Recent work: a fraud-investigation copilot that cut tier-2 handle time 38% and cleared full model risk management review in 11 weeks. The full write-up is in the Bank Fraud Investigation Copilot case study.

Services we run in financial services

Financial services AI consulting at Rockmere typically pairs three or four services on the same engagement:

• AI Transformation for fraud, AML, credit, and operations AI under SR 11-7 discipline
• Enterprise RAG consulting for policy-grounded retrieval, examiner-question answering, and SAR narrative drafting
• SAFe® consulting for Agile Release Trains in banks where the central PMO has given up on Agile twice, with every SAFe® artifact pre-mapped to the audit evidence the second and third lines of defense will require
• Enterprise Agile coaching for the team-level cadence under that scaffolding
• Lean operations consulting for KYC, wire investigation, mortgage origination, and branch ops value streams, where the cost savings frequently fund the AI work
• Talent solutions for embedded senior practitioners on long-cycle bank programs

Our SR 11-7 documentation patterns and senior practitioner credentials are re-verified quarterly on the credentials page.

Case study: bank fraud investigation copilot

A Tier-1 US bank needed faster fraud investigation handle time without weakening SAR quality or examiner posture. The team landed a fraud-investigation copilot that cut tier-2 handle time 38%, with full model risk management review cleared in 11 weeks and the OCC Bulletin 2013-29 third-party paper trail filed. The full write-up is in the Bank Fraud Investigation Copilot case study.

What we do not do in financial services

• Investment advice. We are not a registered investment adviser. We do not recommend securities or build models that constitute advice.
• Pure quant trading systems. Specialized firms own that vertical. We integrate adjacent (risk reporting, operations, compliance). The strategy book stays with the desks.
• Core banking platform replacements. Multi-year, multi-hundred-million programs need a systems integrator 50x our size. We work alongside those programs. We do not lead them.
• Regulatory legal opinions. Compliance and outside counsel own those. We work next to them.
• Anything that pushes a model past your MRM. No shortcuts.

What success looks like

By the end of a financial services AI consulting engagement you have:

1. A production AI system operating under your MRM, vendor risk, and audit frameworks, with the paper trail already filed
2. A delivery cadence the second and third lines of defense have signed off on
3. Documented model cards, validation reports, and control mappings ready for the next OCC or Fed exam
4. An internal team that can extend the system without us, including the regulatory-handling muscle

→ Browse all Financial Services case studies or talk to a Financial Services lead.

What we keep solving here

Outcomes you can measure

• 100% SR 11-7 documentation package signed by second-line
• < 2wk MRM validation cycle for our pilot pattern
• 38% investigator handle-time reduction on the SIU floor
• Zero production deployments without OCC 2011-12 alignment

What you leave with

• SR 11-7 model risk documentation package
• OCC 2011-12 alignment evidence inside the model inventory entry
• Model card and evaluation report cleared by second-line validators
• Fairness, performance, override-rate monitoring dashboards
• Quarterly MRM review schedule with named owners