Key Insights:
- Just 38% of bank risk executives are satisfied with risk management, pointing to siloed controls and fragmented legacy systems.
- CFIs can improve risk management by embedding real-time monitoring and integrated KYC/AML into core banking workflows.
- Unifying risk data with predictive analytics helps CFIs shift from static reports to dynamic early-warning signals.
BASE jumping, mountaineering, cave diving, bull riding and big-wave surfing routinely appear on lists of the world's most dangerous sports — a reminder that some risks are immediate, visible and impossible to ignore. What those sports also share, despite their reputation, is a culture of deliberate risk discipline. Free solo climber Alex Honnold famously journals every move on a route hundreds of times before attempting it ropeless. BASE jumpers run pre-jump equipment checklists. Big-wave surfers study swell forecasts days in advance and station safety swimmers in the water. The danger is unmistakable; and so, by design, is the management of it.
Thankfully, banking risk is rarely that dramatic. It does not arrive with a collapsing wave or a missed foothold; instead, it can accumulate quietly through disconnected data, manual workarounds, inconsistent controls and decisions made without a full view of the institution’s exposure. For community financial institutions (CFIs), where resources are finite and risk responsibilities often span lean teams, embedding risk awareness across the business is less about avoiding adrenaline-fueled danger than ensuring every line of business recognizes, owns and acts on the risks it creates.
Despite the $60 billion banks spent on IT systems supporting risk management in 2024, Accenture’s 2026 banking trends report found that only 38% of risk executives were satisfied with the rest of the organization’s ability to adopt a risk mindset. The gap is especially clear as banks try to roll out new payment capabilities, digital products, and AI tools: 76% say they still have work to do before they can support “smart money” capabilities. That work can involve connecting older systems through APIs and middleware; or, in some cases, replacing core systems altogether. And with 81% of risk executives expecting their risks to become more interconnected over the next two years, banks cannot afford to bring risk teams in only after key product or technology choices have been made.
Three Shifts to Implement Risk Management Across the Company
The gap is not simply a technology-spending problem. It is an operating-model problem: risk controls, data and accountability often sit apart from the customer-facing and revenue-generating decisions where they matter most. For CFIs, the objective is to make risk management part of how the institution opens accounts, underwrites credit, processes payments and changes products, not a separate exercise conducted after the fact. Three practical shifts can help turn that objective into a bank-wide discipline.
1. Embed risk management in everyday processes.
This can involve adding real-time monitoring in the core banking system, moving from quarterly, manual portfolio reviews to continuous monitoring embedded in loan origination and servicing. Automated flags should fly when concentrations, risk grades, or collateral values drift outside the CFI's policy limits.
Embedded risk management can also include integrated KYC/AML. Consider replacing fragmented onboarding flows (branch vs. digital vs. BSA) with a single, risk-scored journey that routes higher-risk customers through enhanced due diligence. Lower-risk customers pass through streamlined checks.
CFIs can also encode lending and deposit risk policies into the systems they use for originating loans and opening digital accounts. Any product change automatically triggers pre-defined risk rules and approval paths. Technology can surface an exception, but business-line leaders still need clear ownership for responding to it, escalating material issues and documenting the decision.
Embedded risk management can also include integrated KYC/AML. Consider replacing fragmented onboarding flows (branch vs. digital vs. BSA) with a single, risk-scored journey that routes higher-risk customers through enhanced due diligence. Lower-risk customers pass through streamlined checks.
CFIs can also encode lending and deposit risk policies into the systems they use for originating loans and opening digital accounts. Any product change automatically triggers pre-defined risk rules and approval paths. Technology can surface an exception, but business-line leaders still need clear ownership for responding to it, escalating material issues and documenting the decision.
2. Unify risk data and apply predictive analytics.
This moves a CFI from static, backward-looking reports to dynamic risk signals that inform decisions at the point of sale or underwriting. By combining core loan data, payment behavior and external indicators such as local economic metrics, bankers can build earlier warnings of credit deterioration. The goal is to identify borrowers or portfolio segments whose risk profiles are trending negatively before missed payments emerge, giving lenders time to conduct outreach, reassess underwriting assumptions or consider restructuring options.
Stress testing can add another forward-looking layer. For example, PCBB’s Stress Testing FIT helps CFIs evaluate portfolio-level and, where needed, loan-level exposure under different economic scenarios, such as rising unemployment, interest-rate changes, declining property values or sector-specific pressure. The resulting analysis can help management assess potential effects on earnings, capital and concentrations, and help them document the assumptions and decisions behind its response. Pattern-recognition models applied to card and ACH transactions can also identify activity that is unusual for particular customer segments. That can help reduce fraud losses and focus manual investigations on the alerts that warrant closer review.
Stress testing can add another forward-looking layer. For example, PCBB’s Stress Testing FIT helps CFIs evaluate portfolio-level and, where needed, loan-level exposure under different economic scenarios, such as rising unemployment, interest-rate changes, declining property values or sector-specific pressure. The resulting analysis can help management assess potential effects on earnings, capital and concentrations, and help them document the assumptions and decisions behind its response. Pattern-recognition models applied to card and ACH transactions can also identify activity that is unusual for particular customer segments. That can help reduce fraud losses and focus manual investigations on the alerts that warrant closer review.
3. Modernize architecture, governance and decision-making.
Pay particular attention to pain points with legacy technology: multiple vendors, stagnant software supply chains, and incomplete views of risk.
Build or adopt a risk data hub that consolidates feeds from the core, the loan origination system, digital banking, and regtech tools, so that risk officers and business leaders share a single set of dashboards and metrics. By upgrading or wrapping legacy cores with modular services, CFIs can make it easier to implement adaptive controls: automated limits, alerts, and workflows that adjust as risk conditions change, rather than using static rules locked in siloed systems.
The bank’s governance structures, such as enterprise risk councils, can use the same analytics platform and data. This ensures that risk, business, and technology leaders make decisions that are based on a common, current picture.
Build or adopt a risk data hub that consolidates feeds from the core, the loan origination system, digital banking, and regtech tools, so that risk officers and business leaders share a single set of dashboards and metrics. By upgrading or wrapping legacy cores with modular services, CFIs can make it easier to implement adaptive controls: automated limits, alerts, and workflows that adjust as risk conditions change, rather than using static rules locked in siloed systems.
The bank’s governance structures, such as enterprise risk councils, can use the same analytics platform and data. This ensures that risk, business, and technology leaders make decisions that are based on a common, current picture.
“Risk management design goes beyond technology — it drives performance and resilience. Despite heavy investment, failures still lead to losses and penalties. Modernizing risk is now a strategic imperative, shaping capital allocation and transformation. Banks that build speed and foresight will lead the next era of risk leadership,” the Accenture report states.
Indeed, 81% of risk executives expect their organizations to face more interconnected risks over the next two years. For CFIs, the response is not simply more technology. It is a more coordinated operating model in which business leaders, risk teams, and technology functions share timely data, clear accountability, and a common view of the institution’s exposure.
