Controls-by-Design: Turning Regulatory Readiness Into a Competitive Advantage

Enterprise leaders are shifting from retroactive compliance to a controls-by-design architecture to maintain a competitive edge amid accelerating technology cycles and evolving regulations.

Integrating Regulatory Frameworks into Technology Design

Financial institutions are increasingly utilizing regulatory frameworks, such as the Digital Operational Resilience Act (DORA) and the EU AI Act, as core design principles. This approach prevents the need to retrofit compliance into existing systems after they are deployed, according to the CIO report.

The EU AI Act requires transparency for high-risk AI systems, including automated credit scoring. One implementation strategy involves building interactive features directly into digital banking applications that allow customers to simulate how specific adjustments might improve their approval odds, transforming a legal obligation into a tool for customer trust.

In the payments sector, the rise of FedNow and stablecoins has reduced settlement windows from days to seconds. Because these transactions are often irrevocable, the CIO report notes that brands must embed behavioral monitoring, AI-driven fraud detection, and account verification directly into the transaction architecture. This design choice aligns with new Nacha rules regarding ACH fraud.

Cross-Functional Alignment and AI Governance

Effective regulatory readiness requires breaking down silos between product, engineering, operations, risk, and compliance teams. The CIO report states that shared accountability and continuous feedback loops allow organizations to adapt to regulation more consistently than when governance acts as a standalone checkpoint.

This collaboration is critical for managing agentic and generative AI, where innovation has outpaced formal regulation. The Federal Reserve’s SR 26-2 guidance on AI for banks establishes expectations for model risk management but allows institutions to determine their own governance for generative AI.

According to the report, a shared view of accountability is necessary because different teams hold different essential perspectives: product teams manage the customer experience, engineering teams handle model deployment, and risk teams oversee governance expectations.

Strategic Partnerships in Risk Management

Due to the complexity of modern fraud threats and the investment required for AI tuning, some organizations are adopting a build-buy-partner strategy. This approach is used to reduce implementation risk in highly regulated environments where proven results are prioritized over internal experimentation.

As an example, CSG Forte partnered with IBM to launch PaymentsProtection.ai. The collaboration integrated IBM’s AI capabilities, real-time monitoring, and financial risk management expertise to avoid spending years recreating existing tools.

The CIO report indicates that this specific partnership resulted in a 50% to 70% reduction in fraud losses and a decrease in false positives for customers.

The brands that lead have embraced regulatory readiness as an advantage to better inform technology architecture, operating models and partner strategy.
CIO