Top Operational Risk Management Challenges for Banks in 2026

8 Top Operational Risk Management Challenges

The table below maps each of the top operational risk management challenges to the Basel II event type it primarily exercises, and the regulatory anchor a US examiner is most likely to reference. Read on to delve into each challenge in more detail.

Challenge 1: Third-party and concentration risk

Vendor stacks now carry an outsized share of operational risk losses in US banking. Core providers, cloud platforms, payment processors, model vendors, and fintech partners sit inside services examiners treat as part of the institution itself.

The June 2023 interagency guidance on third-party relationships, issued jointly by the OCC, Federal Reserve, and FDIC, explicitly applies bank-level risk management expectations to those relationships and rescinded prior agency-specific guidance, replacing them with a single interagency standard.

The concentration of risk is the main challenge presented here. An institution can hold dozens of vendor contracts but find that two or three providers underpin the majority of important business services. Examinations of mid-sized banks now expect:

• Tiered vendor inventories
• Exit playbooks tested under realistic timelines
• Contract clauses on sub-outsourcing

The second-line action is less about new vendor questionnaires and more about reconciling the vendor inventory to the important-business-services map. See the third-party risk management program explainer for the full operating model.

Challenge 2: Cyber events and incident response

The Computer-Security Incident Notification Rule requires banking organisations to notify their primary federal regulator no later than 36 hours after determining that a notification incident has occurred.

The operational risk management challenge is integration. Cyber incident playbooks often sit with information security while the operational risk program owns loss event data and board reporting. In mature institutions, incident triage feeds the operational loss event database.

Challenge 3: AI and model risk in 2026

Generative and predictive AI now sits inside customer-facing decisions. SR 11-7 model risk management guidance remains the primary US anchor for how those models are governed, and examiners have extended its principles to AI systems.

The challenge has three layers:

• Explainability: Can the institution describe how an AI-influenced decision was made?
• Drift: Is the model’s performance still within validated bounds?
• Vendor model dependency

Practical second-line action looks like a model inventory that includes vendor models, a validation calendar that scales testing depth to model materiality, and KRIs that detect drift before customers do.

Challenge 4: Process and change management failures

Loss event data from industry consortia such as ORX consistently shows that change periods (core conversions, system upgrades, mergers and acquisitions, regulatory remediation programs) concentrate operational losses. The Execution, Delivery, and Process Management category in the Basel taxonomy captures most of the losses, but the underlying driver is change governance.

The challenge for second-line teams is timing. Change risk needs to be assessed before the change is implemented. Mature institutions embed a change-risk gate into the project management office. Any change above a defined materiality threshold cannot proceed to implementation without sign-off from operational risk on the:

• Impact assessment
• Test plan
• Rollback procedure

Challenge 5: Fraud (internal and external)

Fraud sits in two Basel categories (Internal Fraud and External Fraud), but the operating reality has converged. Authorised push payment fraud, synthetic identity, account takeover, and internal collusion in account openings now share investigative pathways and data sources.

The challenge is to aggregate fraud-loss data into the operational risk loss event database, share typologies across functions, and avoid the situation where the same scheme is detected, investigated, and reported three times by three teams.

Challenge 6: RCSA fatigue and control testing depth

The annual risk and control self-assessment (RCSA) is the workhorse of the operational risk program. Examiners increasingly probe the depth of the underlying control testing rather than the cadence of the RCSA.

The challenge for second-line teams is honest scoring. RCSA results that show the same heat map year after year, with no residual risk shifts and no control failures discovered, signal a process that has stopped doing its job. The remediation pattern is risk-informed sampling and tying the RCSA cycle to the operational loss event data so the two reinforce each other.

Challenge 7: KRI design and data quality

Key risk indicators are the early-warning layer of the operational risk program. The first failure mode is taxonomy: indicators get confused with KPIs, and the dashboard becomes a mix of risk signals and operational metrics with no clear thresholds. The second failure mode is data lineage, where the indicator looks credible until the auditor examines the underlying number’s origin and recency.

Examiner expectations under OCC Heightened Standards are explicit that risk reporting must be timely, accurate, and at a level of detail that supports board oversight. In practical terms, that means:

• A documented KRI taxonomy
• Thresholds tied to risk appetite
• A defined data source for each indicator

Challenge 8: Operational resilience and recovery time objectives

The 2020 Sound Practices to Strengthen Operational Resilience, issued jointly by the OCC, Federal Reserve, and FDIC, formally describes operational resilience standards for the largest banking organisations but the principles have shaped supervisory expectations across the broader industry.

The core idea is that an institution should identify its important business services, map the people, processes, technology, and third parties supporting each service, set tolerance levels for disruption, and demonstrate the ability to recover within those tolerances under severe but plausible scenarios.

The challenge for risk teams is mapping. Not enough institutions can produce a complete map of the technology, vendor, and process dependencies that sit behind a single service. The second-line action is to start with the top three services by customer impact, complete the dependency map, and run a tabletop scenario that tests whether the institution can hold within tolerance when one dependency fails.

How Institutions Address Top Operational Risk Challenges

The institutions that handle the 2026 operational risk surface well share three operating patterns, which are:

• An integrated risk taxonomy linking RCSA, KRIs, loss events, issues, and third-party risk ratings
• A single source of truth for risk data
• Board reporting that connects risk to strategy and services

Operational risk modules, like the one integrated into platforms like Predict360 (an example of AI-driven risk management software) provide a connected workflow across RCSA, KRI, loss event, issue, and third-party data, which consolidates the spreadsheet reconciliation work that historically consumed second-line capacity.

Frequently Asked Questions