In banking, RCSA is due for a gen AI-driven rethink

<p><b>The technology can add detail and granularity to a process that is often outdated. We explain how.</b></p>

<p>Risk and control self-assessment (RCSA) remains a cornerstone of operational risk management. Yet many banks still rely on outdated, manual methods for control documentation—leading to inefficiencies, audit vulnerabilities and inconsistent practices across business units.</p> <p>Through our work with global banking clients, we hear recurring frustrations. “Our control narratives are all over the place.” “We spend too much time reviewing and rewriting controls.” “We’re always catching up with regulations—never ahead.”</p> <p>It’s time to modernize. Generative AI brings structure, speed and strategic foresight to the traditionally reactive process of control documentation.</p> <p>Here are six reasons traditional RCSA falls short—and how gen AI can help:</p> <ol> <li><b>Controls lack context.</b> Traditional risk statements are often vague and disconnected from real-world incidents or regulatory mandates. Gen AI synthesizes internal data, audit findings and compliance frameworks to generate contextualized risk narratives. For example, instead of the vague “Unauthorized loan approvals,” gen AI produces: “Risk of unauthorized loan approvals may result in financial loss and regulatory breaches under Basel III. Historical data shows X such incidents in the past year.”<br> <br> </li> <li><b>Inconsistent control narratives.</b> Control descriptions vary widely across teams, leading to confusion and rework. Gen AI applies structured models like 5W1H (Who, What, When, Where, Why, How) to standardize phrasing and improve clarity. Rather than the brief statement, “All loan approvals must be authorized and retained for 7 years,” the solution ensures completeness and regulatory alignment with a more detail rephrasing: “All loan approvals must be authorized by both Credit and Compliance Officers, with documentation retained for 7 years and reviewed quarterly by Internal Audit for Basel III adherence.”<br> <br> </li> <li><b>Bottlenecks in review cycles.</b> Manual reviews delay delivery and increase compliance risk. Because many take three to six weeks to complete, they also delay control testing and validation and magnify compliance risk. Gen AI reduces bottlenecks, slashing review time by about 40%. Through streamlined processes and real-time validation, it flags missing elements such as review cadence, evidence retention and responsible parties.<br> <br> </li> <li><b>Limited benchmarking.</b> Most firms lack visibility into how peers manage similar controls. Gen AI enables benchmark controls against internal best practices and industry standards, even offering optimization suggestions. The solution uses anonymized peer metrics from industry surveys; governance, risk and compliance (GRC) system data; and regulatory reports to compare control performance, remediation speed and audit outcomes against industry baselines.<br> <br> </li> <li><b>Testing procedures aren’t audit-ready.</b> Because testing steps can vary by team, they often lack consistency. Generative AI produces standardized, detailed test procedures, including frequency, thresholds and evidence types. For example, instead of a loan approval test step that only checks for missing signed or emailed forms—potentially overlooking storage and audit requirements—the solution offers an expanded test step: “Obtain list of loan approvals above $50,000. Confirm dual authorization. Review audit logs. Validate quarterly Internal Audit review.” As a result, it ensures complete coverage across authorization, documentation and compliance.<br> <br> </li> <li><b>Static controls in a dynamic regulatory environment</b>. Regulations evolve rapidly, but controls often remain unchanged. We’ve seen instances in which disclosure requirements are not integrated into reporting controls for four to five months. Gen AI can continuously scan regulatory feeds and industry alerts to update control language and procedures in real time.</li> </ol> <h4>A practical path forward</h4> <p>Adopting gen AI in RCSA is a practical, phased lift. Most banks already have the core data in place. The key is aligning stakeholders, piloting enhancements and integrating with existing GRC tools. With the right governance and change management, teams can shift from manual effort to intelligent automation.</p> <p>Ready to begin? Start by identifying one high-friction control process and explore how generative AI can streamline it—then scale from there.</p> <p>&nbsp;</p>