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

We've executed AI copilots inside Tier-1 banks, scaled Agile delivery across capital markets, and rebuilt claims operations end-to-end. Every artifact survives an audit because we treat the examiner as a first-class user.

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Industries we focus on6Median engagement length11 weeksDecks without a build path0Named partner on every engagement1
03 / 06
01

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 runs on a different physics from any other industry. 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.

02

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.

03

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:

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.

04

Services we run in financial services

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

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

05

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.

06

What we do not do in financial services

07

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.

How the engagement runs
01
Weeks 1 to 2
SR 11-7 scope
MRM committee identified. Model risk tier set. Documentation template aligned to OCC 2011-12. No surprises at validation time.
02
Weeks 3 to 8
Pilot with second-line in the loop
Real customer-facing data. Evaluation harness instrumented from commit one. Second-line model risk pairs with engineering weekly.
03
Weeks 9 to 12
MRM validation
Documentation package walks through validation. Findings closed. Production approval signed.
04
Beyond 12
Ongoing monitoring
Performance, fairness, override rate tracked monthly. The bank owns the model inventory entry.
In the work

What we keep solving here

Model risk is the long pole, and most AI vendors ignore it
SR 11-7 (Fed) and OCC 2011-12 require validation, monitoring, and challenger models for any AI used in credit, fraud, AML, or pricing decisions. We treat model risk management as a build requirement, not a documentation afterthought.
Agile transformations in regulated banks die in audit
Quarterly planning collides with change advisory boards. PI Planning conflicts with SOX evidence retention. We've solved both. Auditable PI cadences, traceability matrices that satisfy GLBA and OCC examiners, and Definition of Done templates pre-mapped to your audit framework.
Vendor concentration risk caps your AI stack
OCC Bulletin 2013-29 forces you to manage third-party model risk. We design AI architectures that don't lock you to one foundation-model vendor, and we produce the third-party risk paper trail your TPRM team will actually accept.
Customer data residency is non-negotiable
GLBA, state breach notification laws, and your own privacy commitments mean training data can't leave the perimeter. We build inside your VPC, with your KMS, and document what was processed where.
Measured

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
Measured

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
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Running a program like this in Financial Services?

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Clear answers to your questions.
Do you have SR 11-7 / model risk experience?
Yes. Our AI Transformation engagements in banking execute with model documentation aligned to your MRM standards, a challenger model design, and a validation handoff to your second line of defense. We've worked with several Tier-1 and Tier-2 US bank MRM teams.
Can you work inside our VPC / Azure tenant / on-prem environment?
Yes. Almost every financial services engagement runs that way. We do not require data to leave your environment. We use your KMS, your IAM, your network. Our consultants pass standard background checks and we sign whatever vendor risk paperwork your TPRM team requires.
How do you handle SAFe® in a regulated, audit-driven environment?
We map every SAFe® artifact to an audit evidence requirement before launch. PI Objectives become quarterly attestations. Definition of Done includes the SOX/SOC control checks. Iteration reviews include compliance signoff. Audit becomes a byproduct of the operating cadence, not a separate workstream.
Are you a registered investment adviser or licensed broker-dealer?
No. We do not provide investment advice. We are a technology and transformation consultancy. Engagements involving trading systems, advisory platforms, or licensed activity are scoped around your compliance officer's guidance. We don't operate the licensed parts of your business.
What's your typical engagement size and cost in FS?
Pilot AI engagements run 3 to 6 consultants for 8 to 12 weeks. ART launches run 4 to 6 consultants for 12 to 16 weeks. Larger transformations scale to 8 to 15 person pods across multiple value streams over 9 to 18 months. Pricing is fixed-fee or T&M depending on scope clarity.

Stuck on a specific scenario in this industry?

We've been at the table for the audit conversation. Let's compare notes.

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