Artificial intelligence is moving fast, but compliance is not. In financial services, where regulations are strict and scrutiny is rising, outdated manual compliance processes threaten to slow innovation.
According to Experian recent research, 95% of financial institutions report a rise in AI model risk management requirements over the past few years. Yet, compliance is still treated as a manual exercise. On average, 29 people per institution are involved in model documentation, and in larger companies, the number can exceed 50.
The result: Compliance consumes up to a third of teams’ time – time that could otherwise fuel innovation and growth.
The Tightening Grip of Regulation
Regulations impacting financial services are on the rise worldwide. In the U.S., supervisory guidance such as SR 11-7 has long shaped model risk management. Brazil has introduced frameworks that mandate rigorous oversight of risk models. The U.K.’s Prudential Regulation Authority issued its own principles in 2023, requiring banks to treat model risk as a discipline in its own right.
And industry leaders are feeling the pressure. Nearly all (95%) financial institutions report an increase in the number of regulations, and 45% cite geographic variations in compliance as a key challenge.
At the same time, these leaders are facing sharper regulatory scrutiny. Nearly four out of five financial institutions say regulators now raise supervisory concerns more frequently than they did a year ago. And in such a strict environment, noncompliance can be devastating in terms of fines as well as reputational damage and lost trust. Over half of global institutions report that reputational risk from compliance failures is among their greatest challenges.
The High Cost of Manual Processes
Increasing regulatory pressure around AI model compliance wouldn’t be such a problem if compliance leaders had the tools to adjust. However, too many organizations are bogged down by compliance workflows built for a slower era.
In banking, 60% of institutions still rely on manual compliance. For example, compliance staff might spend weeks compiling model validation documents line by line, cross-checking every assumption and data source by hand. These labor-intensive processes result in longer approval timelines, costly rework and delayed product launches, among other bottlenecks.
Financial services leaders are beginning to recognize that automation is the only way to keep up with regulations. In fact, 87% of financial institutions plan to adopt automated model documentation in the next two years.
Why? The obvious answer is that automation can help organizations meet regulatory requirements with greater speed and precision. However, the benefits go deeper. Automation can help banks strengthen their governance, enhance transparency and free up employee time for higher-value work. Importantly, there is also a direct connection between automation and innovation: Organizations that are confident in their compliance posture can bring new products and services to market faster, with less risk of costly setbacks.
Compliance Automation as an Innovation Engine
For finance leaders, the challenge is clear: Transform compliance from a drag into a driver of innovation via automated, end-to-end AI model compliance.
Full automation refers to streamlining and automating as many steps of the model lifecycle as possible, including:
- Data preparation and ingestion: Automating pipelines for pulling, cleaning and transforming data.
- Model development and training: Automated workflows for tuning, retraining and versioning.
- Documentation generation: Automatically producing regulatory and internal documents that capture methodology, assumptions, validation tests and performance metrics.
- Validation and testing: Running predefined validation checks, stress tests and benchmarking automatically, with minimal manual intervention.
- Deployment and monitoring: Automated promotion of models into production, coupled with monitoring tools that continuously track drift, bias and performance.
Here’s how finance leaders can get started:
- Establish a robust data infrastructure, setting the stage for seamless end-to-end automation in the model documentation process.
- Strengthen model governance to ensure consistency and transparency across the entire lifecycle.
- Integrate responsible AI frameworks to align innovation with ethical and regulatory standards.
- Enhance explainability so that both regulators and consumers can understand decisions made by AI and advanced models.
- Modernize operations for resilience so that compliance processes can keep pace with rapid regulatory change.
- Establish clear workflows with assigned roles and responsibilities so modelers and validators can work independently to reinforce the validator’s requirements to challenge the model independently.
- Think holistically, as piecemeal or hybrid approaches often leave gaps and inconsistencies that don’t translate into real progress.
In the bygone era of slow model development and static regulatory regimes, manual compliance made sense. However, in 2025 and beyond, clinging to manual methods is inefficient at best and a strategic liability at worst.
Finance leaders must reframe compliance. Compliance doesn’t have to be a headache; it can be a source of resilience, transparency and trust. More importantly, end-to-end automation can redirect resources toward the innovation that consumers increasingly expect.
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