Compliance with regulations like the Model Audit Rule (MAR) and the Sarbanes-Oxley Act (SOX) can be both a critical safeguard and an operational challenge. The need for consistent testing, documentation, and ongoing internal controls monitoring can be time-consuming and prone to human error. 

Artificial intelligence and automation tools are transforming this reality by streamlining compliance processes, enhancing accuracy, and equipping organizations with data-driven insights that reduce risk. Below, we explore how insurers and other organizations can harness AI and automation to elevate compliance with MAR and SOX while simultaneously optimizing resource allocation and decision-making. We also detail how Johnson Lambert can support your journey toward next-generation compliance. 

The Convergence of MAR and SOX Compliance

MAR applies to insurers crossing specified premium thresholds, imposing requirements for financial audits, internal controls, and governance structures. SOX, on the other hand, focuses primarily on public companies’ internal controls over financial reporting. Although these regulations differ in scope and origin, they share a similar goal: to ensure the integrity of financial reporting and protect stakeholders.

For insurance organizations with multiple regulatory touchpoints (including MAR and, in some cases, SOX), the compliance burden can be significant. Teams often duplicate efforts across discrete compliance obligations. AI and automation can help unify control testing processes, documentation efforts, and reporting standards—thereby reducing silos, minimizing manual errors, and improving overall efficiency.

Why AI and Automation?

1. Reduced Errors and Enhanced Accuracy

Manual control testing and data analysis leave room for human error, especially when large volumes of transactions and documentation are involved. AI systems, powered by machine learning algorithms, excel at processing high volumes of structured and unstructured data quickly and accurately. By centralizing data and automating repetitive tasks, insurers reduce the likelihood of missing or duplicating information—a common source of non-compliance issues.

2. Scalable and Cost-Effective Processes

As insurance organizations grow—particularly those nearing or surpassing the $300 million and $500 million thresholds outlined by MAR—compliance demands increase. Traditional manual processes do not scale easily and can require significant staffing. AI-driven tools, however, scale up relatively quickly, enabling consistent performance whether handling 10,000 or 1 million data points. This scalability also makes it easier to factor in new acquisitions, product lines, or market expansions without overhauling existing workflows.

3. Real-Time Monitoring and Risk Detection

AI-enabled dashboards can offer real-time visibility into transactions and financial processes, flagging anomalies or high-risk items automatically (e.g., unusual journal entries, patterns indicating fraudulent activity). By identifying control gaps as they occur rather than months later, insurers can take swift corrective actions and mitigate potential compliance issues before they escalate.

Use Cases of AI and Automation in MAR & SOX Compliance

Data Ingestion and Classification

A significant barrier for many insurance organizations is disparate data stored across multiple systems in varied formats, including claims systems, policy management platforms, and regulatory compliance tools. AI-driven solutions like Informatica, Talend, or Azure Synapse Analytics can aggregate, cleanse, and classify this information, ensuring consistent terminology and mapping enterprise-wide. Specialized insurance-focused tools like Guidewire DataHub can provide additional features, such as pre-built insurance data models and integration with underwriting or claims systems. These tools streamline workflows by replacing hours of manual data wrangling with quick, automated processes tailored for compliance.

Control Testing and Documentation

Tools like AuditBoard and Workiva have introduced AI capabilities that drive productivity by providing intelligent suggestions, report summarization, issue creation and mapping.   Automated workflow platforms route testing requests, collect supporting evidence, and capture exceptions in a centralized repository. These tools speed up the testing cycle and simplify external audits by reducing the manual workload associated with insurance-specific regulatory needs. Platforms such as MindBridge AI use machine learning to detect control anomalies across insurance-related financial processes, including premium collections and claims payouts. 

Continuous Audit and Monitoring

For insurance organizations dealing with high transaction volumes and potential fraud risks, continuous audit tools are essential. Platforms like Galvanize (HighBond), AuditBoard and Workiva are governance, risk and compliance tools that support continuous monitoring. Algorithms are used to build “normal” baselines for key financial processes, automatically flagging anomalies such as irregular claims patterns or unusual journal entries. These tools offer real-time monitoring of transactions, enabling insurers to detect and resolve control deficiencies swiftly. This not only minimizes risks but also supports proactive compliance, ensuring readiness for audits and regulatory examinations at any time.

Policy and Procedure Review

Natural Language Processing (NLP) tools offer valuable capabilities for scanning insurance-related documents. These platforms can identify inconsistencies, omissions, or misalignments with MAR and SOX requirements across underwriting policies, claims procedures, and financial control guidelines. They can support a proactive approach to policy review, ensuring compliance while preventing fines, reputational damage, or operational disruptions.

4 Things to Consider Before Implementing AI

While AI and automation can revolutionize MAR and SOX compliance, they also introduce unique risks that need careful consideration. 

1. Data Governance and Security

Before introducing any AI-driven compliance solution, insurers must ensure robust data governance. Sensitive client information, policy details, and financial records all require layers of protection from both a technical and procedural standpoint. Encryption, role-based access, and ongoing security monitoring should be non-negotiable elements of the implementation strategy.

2. Talent and Change Management

Implementing AI is as much about people as it is about technology. Internal audit teams, finance, and operations personnel require training to interpret AI-generated insights effectively. Consider establishing a cross-functional project group or center of excellence to oversee AI-related initiatives, manage vendor relationships, and coordinate training.

3. Integration with Existing Systems

AI platforms often need to integrate with established enterprise resource planning (ERP) systems, governance, risk, and compliance (GRC) tools, and data warehouses. Conduct a thorough IT architecture review to pinpoint integration challenges. Deploying APIs or leveraging middleware solutions can help ensure seamless data flow between systems.

4. Regulatory and Stakeholder Alignment

Regulators are increasingly open to technology-forward approaches but demand clarity on how AI-derived conclusions are reached. It’s essential to maintain an audit trail of AI or automation-driven actions—who initiated them, what data was used, and what decisions were made.

By proactively identifying and managing these AI-specific risks, organizations can maximize the efficiency and accuracy gains from AI and automation—without sacrificing the robust governance and control environment demanded by MAR and SOX.

A Roadmap for Implementation

Pilot Programs

• Begin with a limited-scope pilot focusing on a high-impact yet manageable area—e.g., automating the testing of a few key controls. Gather metrics on time saved, error rates reduced, and user acceptance.
• Assess the pilot’s impact on data quality, test coverage, and overall compliance speed to build a business case for broader adoption.

Full-Scale Deployment and Phased Rollout

• Expand to additional controls and processes, possibly in multiple business units or lines of business.
• Refine the AI algorithms over time using feedback loops and performance metrics to further improve accuracy.

Ongoing Optimization and Audit Committee Oversight

• In the world of MAR and SOX, compliance is not a one-and-done initiative. Keep refining AI models and automation scripts to adapt to evolving business environments and regulatory guidance.
• Provide regular updates to the audit committee on the effectiveness of AI-driven solutions, including any high-risk findings or newly identified control gaps.

Achieving Long-Term Value

Beyond the immediate compliance benefits, AI and automation support strategic decision-making. Accurate, real-time data offers sharper insights into customer behavior, market trends, and operational risks. When integrated effectively, compliance data can inform broader business strategies—ranging from product development to mergers and acquisitions—enabling leadership to make decisions with both agility and confidence.

For growing insurance organizations, AI-driven compliance can become a competitive differentiator. Investors and rating agencies take note of forward-thinking risk management. Demonstrating that your control environment is optimized with advanced technologies can signal stability, transparency, and innovation—traits that resonate in a highly competitive market.

How Johnson Lambert Helps Insurers Navigate AI Integration

Johnson Lambert offers the guidance and risk management support insurers need to implement secure, compliant AI systems. Our internal audit solutions align AI initiatives with MAR and SOX requirements, mitigating financial, operational, and compliance risks. We assist in creating or refining AI governance frameworks—mapping financial systems to security controls, reviewing oversight structures, and checking for data biases. Our approach is rooted in best practices from frameworks like the NAIC’s Model Bulletin on AI, the NYDFS Circular on AI systems, and National Institute of Standards and Technology (NIST’s) AI Risk Management Framework (AI RMF).

As AI becomes integral to underwriting, fraud detection, and customer service, our deep expertise in insurance operations and regulatory compliance helps you take advantage of opportunities while minimizing risks. 

In addition, our cybersecurity assessment services tackle data vulnerability concerns—from protecting intellectual property to verifying responsible third-party model usage. Leveraging frameworks such as those from NIST, NYDFS, and Control Objectives for Information and Related Technologies (COBIT), we deliver customized assessments and reports. Our membership in the Center for Internet Security ensures we stay at the forefront of security and compliance best practices.

Explore How AI Can Transform Your Compliance Landscape

Our team offers deep industry experience and a commitment to helping insurers thrive in an evolving regulatory environment. Connect with Johnson Lambert today to start your journey toward next-generation compliance.