Leveraging artificial intelligence (AI) in the investment industry has delivered efficiency and insights. It’s also become a tool for compliance monitoring. For it to be effective and consistent, AI governance for compliance is critical.
This technology can make a substantial difference in how firms achieve and maintain compliance. However, there’s still a need for controls, documentation, and human oversight. Where these things and AI converge can transform how organizations manage compliance, delivering transparency and accuracy.
So, how does it work, and how do firms strategically implement AI governance compliance? Here is an in-depth look.
Key Takeaways
- AI has become an essential tool for investment firms to monitor compliance, but it requires a strong governance foundation to be effective and accurate.
- AI governance for compliance enables firms to establish controls, monitor AI activity, and maintain greater visibility into potential risks.
- Compliance monitoring should be a shared responsibility between AI tools and human review.
Why AI Governance Matters for Compliance Monitoring
Developing responsible AI governance has implications for organizations. Just adding AI tools to a tech stack doesn’t mean it will deliver the expected results. AI needs guardrails in the compliance realm, starting with a structured approach.
Research indicates that 80% of businesses see AI explainability, ethics, bias, and trust as the most prominent obstacles to greater adoption of the technology. Addressing these challenges requires an AI governance framework.
The types of risk it can support in compliance include:
- Unclear decision-making: AI without purpose and governance may surface insights that are incomplete or without context. Thus, confidence in the ability to use it to guide decisions weakens.
- Inaccurate outputs: AI governance compliance starts with trusted, accessible data. AI-generated outputs are only as dependable as the data available to the system, making data quality, completeness, and governance foundational to effective compliance monitoring.If the data has integrity or completeness issues, users can’t truly trust outputs.
- Limited visibility into the production of AI-generated results: This describes the explainability conundrum. Firms must fully understand how AI produces results regarding compliance. Otherwise, it’s just a black box.
Overcoming these challenges starts with building controls.
Building Controls Around AI Compliance Monitoring
The first step in developing AI governance for compliance is creating the right controls. When building AI for compliance workflows, the leading criteria include:
- User permissions: How do those who need these insights access the information, and does it offer what they need to do their jobs effectively?
- Approval processes: If AI is operating in decision trees, firms must define rules about how this works to ensure accuracy and compliance.
- Usage guidelines: Users need to be aware of the limitations and opportunities of using AI for compliance monitoring. A set of standards around usage is imperative.
- AI-generated output monitoring: When AI models identify potential compliance issues, humans still need to investigate. That’s much simpler to do with a governance framework.
- Model governance: Firms should have visibility and control over which AI models are available for specific users and workflows, rather than relying on a single model or provider across every use case.
These controls are not set in stone. They’ll need regular review and updates as the regulatory landscape evolves. Once firms build controls, there are other considerations in AI compliance monitoring.
Maintaining Audit Trails for AI-Driven Compliance Decisions
A substantial part of being compliant with regulations is documentation because firms always need to be audit-ready. Without an audit trail within an AI compliance reporting tool, organizations would be woefully unprepared.
An effective audit trail should provide visibility into how AI is being used across the organization, including user activity, queries, model usage, and other relevant interactions. This gives firms a centralized record of AI activity and helps support transparency, accountability, and internal oversight. Doing so ensures consistency across processes, so that there’s evidence of applying regulatory rules holistically.
With modern investment management software that includes compliance monitoring, firms can easily track inputs across all processes. These can involve outputs and changes. This also provides a full history of activities, supporting transparency and accountability.
The Role of Human Review in AI Governance
AI augments human actions; it does not replace them. Human review in AI compliance governance is crucial to achieving the overall goals of using the technology.
AI can accelerate compliance monitoring and help improve consistency, but it cannot replace the expertise of humans. There are several key ways that humans and AI intersect. First, there’s the need to observe review processes. How is AI making decisions, and how accurate are they?
Second, firms should designate the escalation steps once AI flags a compliance concern. These rules dictate how a human would examine the alert.
Finally, humans are the final decision-makers in terms of validating AI recommendations. They should have a workflow to verify the response before the system takes any action. This is the last checkpoint. Bypassing it could result in a lot of false positives and unnecessary reworks.
Balancing AI Automation With Compliance Oversight
Investment management software with AI compliance monitoring capabilities enables automation and oversight to work hand in hand.
AI excels at creating efficient processes, reducing a lot of the manual, repetitive work currently required. However, users shouldn’t put AI on cruise control to be the sole compliance engine. Firms still need human-centered controls.
Balancing automation and oversight does not have to be cumbersome. The right technology tools usher in this new approach to compliance. AI and human intelligence working together deliver the best results for firms in terms of efficiency and cost-effectiveness.
Creating an AI Governance Framework for Compliance Workflows
How do organizations begin the journey of interweaving AI into compliance monitoring? There are practical steps to take to establish a framework, including:
- Defining policies regarding AI’s use in compliance monitoring
- Assigning responsibilities so everyone knows their role
- Monitoring performance of how well AI tools are identifying true compliance risks
- Reviewing AI processes regularly and tweaking them as necessary
Follow these standards when constructing a framework. Doing so supports a firm’s adoption of AI for compliance monitoring.
Choosing Technology That Supports Governed AI Adoption
Organizations have an opportunity to use AI compliance monitoring to ensure more efficient and accurate processes. AI is a powerful tool, but it does not operate in a vacuum. Human oversight and guidance guardrails are essential to achieving audit readiness and ensuring continued compliance.
Strengthen compliance oversight with AI solutions like iPM Portfolio Architect AI, which is designed to support transparency, controls, and informed decision-making. Contact INDATA to learn how governed AI can support investment operations.
FAQs
What types of compliance tasks can AI help monitor?
AI can support compliance teams by helping analyze investment and operational data, identify patterns or potential exceptions, and surface information for further review. The specific applications will depend on a firm’s systems, data, policies, and compliance requirements. AI should complement—not replace—the judgment and oversight of compliance professionals.
What data does AI need to support compliance monitoring?
The effectiveness of AI depends heavily on the quality, completeness, and accessibility of the underlying data. For investment firms, this can include portfolio, trading, transaction, operational, performance, and other relevant enterprise data. A governed data foundation helps AI tools produce more reliable and explainable outputs.
For AI tools to have access to all this data, technology to aggregate and analyze it is necessary. It ensures accuracy and completeness for compliance monitoring.
Who should oversee AI governance for compliance within an investment firm?
Within investment firms, several stakeholders should be involved in overseeing AI governance. Organizations should assemble a cross-functional team to include a C-suite representative (CIO or CTO), legal, compliance, risk management, IT, and any other core business units like traders and portfolio managers. It’s crucial to have diverse representation so companies can understand all of the use cases.
How can firms evaluate whether an AI compliance tool is reliable?
Firms should evaluate AI compliance tools based on several factors. The first is to audit the accuracy of its algorithms. Second, firms should test different models with use cases to determine how effective and accurate they are. Third, organizations should verify that AI is mapping outputs to regulatory maps. Also, check the software providers’ data privacy and security standards. Finally, understand exactly how the system deploys AI and how scalable it is.
