Risk Inventory at a Mid-sized Financial Institution: Case Study and Framework

Launches Stalled by Unknown Risks

Many large financial institutions struggle because they lack a consolidated risk inventory.

Product teams work from fragmented lists and ad hoc spreadsheets.

The gap cost launch time, triggered regulator inquiries, and produced repeated audit findings in this case.

Below is a practical Risk Inventory Framework and a timeline showing how a fractional CCO embedded with product and operations produced an audit‑ready inventory mapped by product and process.

Background and Enterprise Challenge

The subject was a mid‑sized bank with payments, lending, deposits, and multiple third‑party integrations across 30+ states. Each product team kept local risk notes in Notion and spreadsheets. Nothing served as a single source of truth.

Policy documents lived in Notion templates. Jira tracked tickets but rarely linked controls to evidence. The result: inconsistent control definitions, unclear owners, and repeat regulator flags.

One payments release was paused after a state examiner found missing disclosures. That pause became the organizing incident for this work. The product team scrambled. Legal couldn't confirm state filing status. Engineers waited on instructions.

Regulators expect crisp, auditable artifacts. We mapped evidence packs to CFPB Supervision & Examination Manual and used the OCC Comptroller’s Handbook on Risk Management to design governance. 

Stakeholders included product, engineering, legal, internal audit, operations, and compliance. Tension flared when product pushed releases and legal sought licensing clarity. Budget constraints and urgency made the fractional model preferable to hiring a full‑time CCO.

Quick takeaway: Start with a single, high‑risk product slice and prove the pattern.

Risk Inventory Framework Overview

Step 1:  Scope definition and taxonomy

List products, services, and core processes inside the inventory boundary. Classify risks by domain: consumer compliance, payments, AML, cybersecurity, licensing. Align the taxonomy with an enterprise standard such as COSO for defensibility. For cybersecurity controls, map to NIST Cybersecurity Framework (CSF) categories as needed. Add a jurisdiction column so state versus federal exposure is visible.

Example: Map the payments disclosure risk to a product called "ACH Gateway" and a process called "consumer disclosure flow." Assign a jurisdiction tag for each state where the feature will launch.

Why this matters: If you can't answer "who owns this risk and where's the evidence?" you can't defend a launch.

Step 2: Data model and control mapping

Build a compact model: Risk ID, product, process, control description, owner, evidence pointer, residual risk score. Use a control matrix template to speed adoption control matrix template.

Map each control to specific regulations and evidence types: Policy, transaction logs, system screenshots, control test results, or SOC reports. Use AICPA guidance when SOC evidence is relevant AICPA SOC guidance. Pull sample metadata from existing Jira and Notion to seed fields. No need to boil the ocean; you can make the framework more granular in future iterations.

Practical tip: Add a one‑line evidence standard for common artifacts (e.g., "transaction log + test script + owner attestation").

Step 3: Governance and ownership model

Assign a single owner to each control. Publish escalation paths and review cadences: monthly for high‑risk, quarterly for moderate, yearly for low. Create an approvals matrix with product, legal, operations, and the fractional CCO signatories. Tie governance to audit readiness and your 50‑state licensing plan; reference NMLS — state licensing resources.

Practical tip: Require a control owner before a release ticket moves from staging to production.

One‑sentence takeaway: ownership stops last-minute firefights.

Implementation Timeline: Phase-by-phase Rollout

Phase 1: Discovery workshops and intake

Run targeted, timeboxed workshops with product, engineering, legal, operations, and account managers. Use facilitated templates and capture regulator‑flagged incidents and prior audit findings.

A fractional CCO led the sessions to keep momentum and lend credibility. Short, scripted prompts help. Ask teams: “What could go wrong here? Who will answer regulator questions?”

Stakeholder voice: A product manager said, “We had no single place to point an examiner.” That line framed the first week of work.

Action steps:

• Run two 90‑minute workshops per product slice.
• Capture past audit findings as seed items.
• Export metadata from Notion and Jira for initial population.

Phase 2: Build the inventory and validate

Populate the data model from workshop outputs and validate entries with subject matter experts. Run a rapid sampling exercise of 20 controls to calibrate scoring and pull evidence. Convert priority items into Jira tickets with owners and acceptance criteria.

Use AuditBoard controls testing resources to shape control testing and evidence workflows.

Practical example: Pick 20 high‑impact controls, pull matching evidence, and run tests to confirm evidence quality. If tests fail, open remediation tickets tied to sprint stories.

Deliverable example: A validated mini inventory of 20 controls with evidence links and closure criteria. That deliverable became the first regulator‑ready evidence pack.

Phase 3: Remediation roadmap and sprint integration

Prioritize remediation by residual risk and launch impact to produce a 90‑day plan. Translate top items into sprint‑ready engineering tasks with clear test steps. Track KPIs: remediation velocity, percent of controls with evidence, and first‑pass test rate.

A fractional CCO coordinated approvals, drafted regulator‑ready artifacts, and supported licensing filings. This made exam responses and state submissions faster.

Practical tip: Tie remediation tickets to sprint acceptance criteria. Require evidence links and owner sign‑off as part of the Definition of Done.

Outcomes and Measurable Improvements

Reduced launch delays and faster decisions

After implementation, average launch holds for medium‑risk features dropped from eight weeks to about three weeks. Clear control ownership and prebuilt evidence templates removed ambiguity. The fractional CCO shortened decision loops by aligning product and legal on a single source of truth.

Short human detail: Engineers stopped waiting for vague instructions. Releases moved with confidence.

Improved audit and licensing readiness

The inventory produced control matrices and evidence packs used directly in a licensing submission. One filing that previously took six weeks was ready in two because artifacts were assembled ahead of time. We leaned on AICPA control testing checklists during validation.

Operationalized governance and fewer repeat findings

Ownership clarity rose to over 90% of controls assigned. Overdue remediation counts fell by roughly 60%, and first‑pass test success increased. These metrics aligned to benchmark guidance in risk assurance reports when setting targets.

One line summary: the program turned fragmented notes into repeatable artifacts auditors accept.

Lessons learned and practical advice

Common obstacles were stakeholder inertia, poor data hygiene, and ambiguous ownership. Mitigate these by starting with a high‑risk product slice, timeboxing workshops, and enforcing one owner per control.

Quick wins:

• Seed the inventory with past audit findings.
• Link controls to Jira tickets with owners and acceptance criteria.
• Run quarterly regulator‑readiness drills.

For AML and payments risk, use FinCEN risk assessment guidance when assessing money‑movement exposures. For IT controls and vendor risk, consult FFIEC IT Examination Handbook and SEC/FINRA vendor risk guidance to ensure evidence completeness.

Quick Checklist: First 30 Days

• Run two 90‑minute discovery workshops per product slice.
• Seed the inventory with 20 high‑impact controls and gather evidence.
• Assign owners and open Jira remediation tickets.
• Draft one regulator‑ready evidence pack for a live product.
• Schedule monthly high‑risk reviews and quarterly governance checkpoints.

Conclusion and Next Step

A structured risk inventory by product and process turns compliance into a predictable input for launches. Embedding a fractional CCO accelerated decisions, produced regulator‑ready artifacts, and made multistate licensing more manageable.

Next step: run a four‑week discovery workshop to produce a validated inventory slice and a 90‑day remediation plan.

FAQs

Q: How do you scale the inventory across 50 states?
A: Map each product to state filing requirements via NMLS. Maintain a jurisdiction column and assign licensing owners per state. Automate periodic checks against NMLS to flag changes.

Q: How should residual risk be scored?
A: Use a simple likelihood x impact rubric, aligned to COSO or Deloitte guidance. Add a qualitative overlay for regulatory sensitivity.

Q: Full‑time CCO vs fractional CCO?
A: A full‑time CCO gives continuous institutional memory. A fractional CCO delivers senior leadership faster and at lower fixed cost. Fractional support is ideal for rapid program design, licensing support, or exam readiness without adding fixed headcount.

Q: How do I integrate with Jira, Notion, AuditBoard?
A: Link control records to Jira tickets for remediation. Store policies in Notion and point evidence links back to the inventory. Use AuditBoard templates for testing and evidence management.

Q: What review cadence is recommended?
A: Monthly for high‑risk controls, quarterly for medium, and annual for low. Trigger ad hoc reviews for major product changes or regulator inquiries.

Q: What artifacts do auditors and examiners want?
A: Control matrices, evidence packs (logs, test scripts, signed policies), remediation tickets with closure evidence, and a governance approvals matrix. Use AICPA SOC criteria for evidence structure when service organization controls matter.

Q: Which KPIs to report after 90 days?
A: Percent of controls with owners, remediation velocity (closed/open), percent of controls with sufficient evidence, and first‑pass test success. Compare to industry benchmarks from firms like PwC and Deloitte to set targets.