AI is transforming how financial firms meet FINRA compliance requirements. By automating processes like communication monitoring, data analysis, and fraud detection, AI helps firms handle complex regulatory demands efficiently. Here's what you need to know:

• What is FINRA Compliance? FINRA regulates U.S. securities firms to protect investors and ensure market integrity. Non-compliance can lead to fines, suspensions, or criminal charges.
• How AI Helps: AI processes large datasets, monitors communications (email, social media, etc.), detects risks like insider trading, and updates compliance programs in real time.
• Key Rules: FINRA Rule 3110 requires oversight of all communications, including AI-generated content. Regulation Best Interest (Reg BI) ensures brokers act in clients' best interests.
• Risks to Manage: AI introduces risks like data bias, privacy concerns, and cybersecurity threats. Firms must implement strong governance, recordkeeping, and employee training.
• Tools in Action: Platforms like Quartz automate communication archiving and reporting, reducing manual effort while meeting FINRA and SEC standards.

AI simplifies compliance while addressing evolving regulatory challenges. Firms using AI can better manage risks, improve oversight, and maintain adherence to FINRA rules.

[AI Series] Building an AI Tool for Financial Compliance With Mamal Amini

Core FINRA Compliance Requirements

Grasping FINRA's core requirements is crucial for leveraging AI in policy enforcement. These regulations form the foundation for financial firms' operations, and AI can be a powerful tool to strengthen compliance in several critical areas.

Recordkeeping and Supervision Standards

FINRA Rule 3110 mandates that firms supervise all communications, including those created by AI, using a combination of technical and human oversight. This rule also requires firms to establish written procedures for reviewing electronic communications related to their investment banking or securities business.

FINRA is clear: firms are accountable for all communications, including those generated by AI. They must ensure these communications comply with securities laws, FINRA rules, and specific requirements for supervision, recordkeeping, and content standards.

"FINRA clarifies that the supervision of AI chatbot communications with investors aligns with the existing standards for retail communications, institutional communications, and correspondence under Rule 3110."

This puts compliance teams in a pivotal role. They need to implement technical controls to monitor AI-generated communications, collaborate with data scientists to validate AI models, and use supporting technologies to maintain transparency and monitor AI-assisted interactions. For example, AI tools must be configured to capture and retain relevant parts of conversations for recordkeeping.

AI-powered review systems can assist in analyzing investor content, flagging risks, and reviewing communications in multiple languages. However, FINRA stresses the importance of a "human-in-the-loop" approach to validate the issues flagged by machine learning tools.

These measures align closely with broader customer protection initiatives under Regulation Best Interest (Reg BI).

Regulation Best Interest (Reg BI) and Customer Protection

The SEC's Regulation Best Interest (Reg BI) sets a "best interest" standard for broker-dealers and their representatives when recommending securities transactions or investment strategies to retail customers. It ensures that brokers prioritize the customer's interests over their own financial or other benefits.

Reg BI includes four key components: Disclosure, Care, Conflict of Interest, and Compliance. The SEC explains:

"whether a broker-dealer has acted in the retail customer's best interest under the General Obligation will turn on an objective assessment of the facts and circumstances of how these specific components of Regulation Best Interest are satisfied at the time that the recommendation is made (and not in hindsight)"

Recent cases highlight the financial repercussions of Reg BI violations. For instance, JP Morgan Affiliates paid $151 million to settle SEC enforcement actions on October 31, 2024. Other notable cases include Lion Street Financial, LLC and Western International Securities, Inc., both of which faced settlements in late 2024.

AI can significantly aid in reducing compliance risks under Reg BI by automating risk detection, identifying anomalies, and streamlining processes. This is particularly vital as fraud against individuals over 60 results in losses exceeding $3 billion annually, according to the FBI's Elder Fraud Report 2023.

These obligations set the stage for firms to adapt to evolving regulatory demands in technology governance.

Recent Regulatory Updates

FINRA has shifted its focus toward new challenges posed by technologies like generative AI, building on recordkeeping and customer protection requirements. According to FINRA Regulatory Notice 24-09, compliance obligations depend on how a tool is used, not the technology itself.

"Regulatory expectations are technology-agnostic. As emphasized in FINRA Regulatory Notice 24-09, compliance obligations apply based on how a tool is used - not what it is."

FINRA Rule 3110 now explicitly addresses technology governance. Firms using generative AI must ensure compliance with rules on governance, model risk management, data privacy, and AI reliability. This involves evaluating AI tools before deployment to confirm they meet compliance standards.

"FINRA intends for its rules and guidance to be technologically neutral and to function dynamically with evolutions in technology and member firms' processes."

Firms must carefully assess what their AI tools generate, how these outputs are used, and whether they can be effectively supervised. This includes documenting policies for AI usage, storage, and oversight.

Additionally, FINRA, the SEC, and NASAA have warned about the rising use of AI by fraudsters in investment scams. This highlights the need for robust AI governance to protect both firms and their clients.

The regulatory environment continues to evolve with trends like extended-hours trading, new forms of investment fraud, and increasing adoption of generative AI in financial services.

How to Set Up AI-Powered Policy Enforcement

Once you've outlined your core compliance requirements, the next step is to implement AI governance and operational practices that align with FINRA's technology-neutral standards. A phased, strategic approach can help you integrate AI-powered policy enforcement into your existing compliance framework.

Creating and Documenting Policies

To align with FINRA's requirements, start by formalizing your AI governance framework. Written policies are the foundation of any compliance program. Under FINRA Rule 3110, firms must maintain customized supervisory systems. When AI tools are part of this framework, your policies should address several key areas.

First, establish a cross-functional governance structure that includes compliance, IT, legal, and business representatives. Your policies should cover areas like technology governance, model risk management, data privacy, and AI model reliability. Document clear procedures for procuring, implementing, training, maintaining, and overseeing AI systems.

Model risk management is particularly important. Keep a detailed inventory of all AI models, assign risk ratings, and set performance benchmarks. Include processes for ongoing monitoring and testing, using stressed scenarios and new datasets.

For data governance, focus on ensuring accuracy and addressing potential biases in datasets. Include steps for regularly reviewing the credibility and reliability of data sources. These measures will help maintain the integrity of your AI systems.

Adding AI Tools to Compliance Systems

You don't need to overhaul your entire compliance infrastructure to integrate AI tools. API-based solutions can seamlessly enable real-time fraud detection and compliance monitoring while working within your existing systems. Pilot programs can help you test effectiveness before making larger investments.

Cloud-based platforms are another option, offering the ability to process large volumes of compliance data more efficiently and at a lower cost. However, standardizing your data is critical, as legacy systems often contain unstructured or siloed information that can hinder AI performance.

For messaging compliance, platforms like Quartz can simplify integration. Quartz's AI-powered system archives and monitors communications across platforms like iMessage and WhatsApp. It ensures compliance with FINRA and SEC regulations through automated reporting, misuse detection, and integration with your current compliance tools.

When choosing AI tools, ensure they meet regulatory standards, such as explainable decision-making and comprehensive audit trails. Your tools should clearly demonstrate how compliance decisions are made and maintain records that satisfy FINRA's supervision and recordkeeping requirements.

Start with simpler use cases, such as using AI to review Know Your Client (KYC) files or conduct sanctions investigations. Set clear thresholds and guardrails for when AI models can trigger actions autonomously, and include human review layers to allow compliance teams to override AI decisions when necessary.

Once integrated, make sure your team is equipped to manage and oversee these systems effectively.

Training and Supervising Employees

Employee training is a critical component of any AI-powered compliance program. A well-designed training strategy ensures your team understands how to use AI responsibly and effectively.

Tailor training to the specific risks and responsibilities of each role. Compliance staff, for example, need different AI knowledge than trading desk employees or customer service teams. Training should explain how AI works, its capabilities, and its limitations, while emphasizing data privacy, security, and compliance requirements.

Teach employees to identify and address biases in AI outputs. They should know how to recognize when AI-generated content is incomplete or inaccurate and understand how to override or correct it.

Incorporate real-world examples and case studies into your training materials. Use scenarios that reflect your firm's operations, such as how AI tools flag suspicious activity or detect regulatory violations.

Provide ongoing learning opportunities through webinars, microlearning modules, and knowledge-sharing platforms to keep employees updated on AI advancements and emerging risks. Regular updates ensure your training evolves alongside AI technology and regulatory expectations.

Establish clear escalation procedures for AI-related issues. Employees should know when and how to report inaccuracies or compliance concerns. Promote transparency in AI decision-making and view audits as opportunities for improvement.

Finally, monitor the effectiveness of your training program using metrics like employee feedback, completion rates, and post-training assessments. This data can help identify areas for improvement and demonstrate your commitment to maintaining strong AI governance.

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Managing AI-Related Risks in FINRA Compliance

Expanding on the foundation of an AI compliance framework, effectively managing the risks tied to AI becomes a crucial focus. While AI enhances compliance efforts, it also introduces new challenges that demand careful attention. According to McKinsey, 72% of organizations now use AI in some capacity, marking a 17% increase from 2023. Yet, a study by the IBM Institute for Business Value reveals that 96% of leaders believe adopting generative AI heightens the risk of security breaches. To maintain regulatory compliance, it’s essential to understand and address these risks.

"AI risk management is the process of systematically identifying, mitigating and addressing the potential risks associated with AI technologies." – Annie Badman

AI risks generally fall into four main areas: data risks, model risks, operational risks, and ethical and legal risks. For firms working under FINRA compliance, specific challenges include managing model risks, ensuring data governance, protecting customer privacy, and maintaining effective supervisory control systems. Each firm must also assess the implications of AI tools based on their unique business models and use cases. A critical component of mitigating these risks is establishing thorough recordkeeping practices.

Managing Recordkeeping and Data Tracking

Accurate documentation and effective data management are non-negotiable when it comes to compliance. FINRA’s Rule 3110 extends supervision requirements to include AI-generated content and decisions, making detailed recordkeeping a core aspect of regulatory adherence.

• Performance monitoring and benchmarking: Establish benchmarks for model performance and implement continuous monitoring and reporting processes. Regularly test AI systems - both at the outset and on an ongoing basis - using varied and stress-tested datasets to ensure compliance as market conditions and data patterns shift.
• Data governance: Address accuracy and bias concerns as required by FINRA. Review datasets for potential biases and verify the legitimacy of data sources. Implement clear procedures for data classification, access controls, and retention policies to align with FINRA’s recordkeeping rules.
• Explainability and audit trails: Ensure your AI models can clearly document how decisions are made. This transparency is critical for compliance and should be a key feature of your model risk management process. Maintain comprehensive records that satisfy FINRA’s supervision and recordkeeping requirements.

Once recordkeeping is in place, the next step is to secure your systems and data against emerging cybersecurity threats.

Cybersecurity and Data Privacy

Financial institutions face unique cybersecurity risks, particularly when adopting AI systems. With their access to sensitive customer data and high-value transactions, they are prime targets for cyberattacks. A breach could result in significant financial losses - potentially in the millions.

AI integration adds complexity to the cybersecurity landscape. The IBM study found that only 24% of current generative AI projects are adequately secured. Additionally, financial institutions often depend on external providers for AI services, creating systemic risks if disruptions occur.

• Strengthen data protection: Use encryption, implement strict access controls, and segment networks. Classify data, rely on trusted infrastructure, and ensure secure storage and deletion practices.
• Access control and authentication: Employ multi-factor authentication (MFA) for internal users, administrators, and vendors. Develop and test robust authentication and access control procedures, and use encryption to safeguard sensitive information.
• Ongoing security monitoring: Regularly update and patch systems to address vulnerabilities. Conduct data security risk assessments and verify datasets to ensure data integrity. Be aware that bad actors may use AI to enhance the effectiveness of their cyberattacks.
• Third-party risk management: Given the reliance on external AI providers, conduct thorough vendor assessments and ensure clear contractual obligations for data protection and compliance.

Once systems are secured, the focus shifts to reducing bias and ensuring the accuracy of AI models.

Ensuring Accuracy and Reducing Bias

AI systems can unintentionally amplify biases, leading to compliance risks that demand proactive oversight. McKinsey reports that only 18% of organizations have a council or board empowered to oversee responsible AI governance, highlighting the need for more robust oversight frameworks.

• Bias detection and mitigation: Incorporate bias testing into your AI governance framework to prevent discriminatory outcomes. Test AI-driven tools for accuracy, fairness, and explainability. Cleanse training data to minimize the impact of outliers and potential malicious inputs.
• Human oversight: Introduce human review for AI outputs and set thresholds for when machine learning models can act autonomously. Compliance teams should have the authority to override AI decisions, especially in high-stakes scenarios like regulatory reporting or customer communications.
• Continuous monitoring: Regularly retrain models and cleanse data to counteract drift as market conditions evolve. This ensures that AI systems remain aligned with FINRA compliance standards.
• Cross-functional governance: Establish a governance structure that includes compliance, legal, IT, and business representatives. This team should oversee AI deployment, monitoring, and risk management strategies.
• Fallback procedures: Prepare for unexpected AI failures by implementing manual processes for critical functions. Clear escalation procedures should also be in place to address AI-related issues.

As FINRA continues to stress the compliance risks tied to increased reliance on technology, adopting comprehensive risk management practices allows firms to leverage AI while meeting regulatory expectations for responsible use.

Real Example: AI-Powered Messaging Compliance with Quartz

Quartz offers a practical look at how AI-driven policy enforcement can simplify compliance. By aligning with FINRA's recordkeeping and supervision requirements, Quartz ensures firms can stay compliant without overhauling their existing systems. It works seamlessly with current communication tools and personal devices, eliminating the need for additional hardware or complicated integrations. Let’s explore how Quartz delivers on its promise of smarter compliance.

"SEC & FINRA Compliance Made Easy. Really Easy." - Quartz Intelligence

Archiving and Monitoring Communications

One of Quartz's standout features is its ability to securely archive communications across multiple platforms like iMessage, WhatsApp, Outlook, Teams, and text messages. This ensures comprehensive recordkeeping, a critical requirement for FINRA compliance. Setting it up is simple: users add Quartz as a contact on their preferred chat platform, and the AI takes over. It monitors conversations, enforces compliance policies, and archives data automatically - no manual effort required.

Quartz goes beyond basic keyword detection. Its AI Compliance Agents analyze context and intent around the clock, providing a deeper layer of oversight. The platform also integrates effortlessly with existing compliance systems, enhancing them with AI's advanced capabilities.

Automated Compliance Reporting

Quartz doesn’t stop at archiving; it also simplifies reporting with its automated tools. The platform’s AI Compliance Agents manage everything from archiving and triaging to reviewing and reporting digital communications.

"Save the busy work for us – Quartz AI Compliance Agents handle all of your digital communications archiving triaging, review, reporting, and more." - Quartz Intelligence

This automation includes real-time monitoring, policy enforcement, misuse detection, and report generation. By handling these tasks, Quartz allows compliance teams to focus on higher-level strategies. Impressively, it reduces false positives by 98%, cutting down on unnecessary distractions. Plus, Quartz offers unlimited data exports, free cloud storage, and customizable workflows for managing alerts and escalations, giving organizations the flexibility to shape compliance processes to their needs.

Privacy-Focused Monitoring

Quartz strikes a balance between regulatory compliance and employee privacy. Its intelligent monitoring system flags non-compliant behavior and issues corrective alerts without being intrusive. With continuous monitoring, the platform identifies potential violations and takes immediate corrective action, all while operating discreetly in the background.

This real-world example highlights how AI can transform compliance. Quartz turns a traditionally manual and time-consuming process into an automated, intelligent system that not only meets FINRA standards but also improves overall business efficiency.

Conclusion and Main Points

AI-powered policy enforcement is transforming how financial firms handle FINRA compliance. Instead of relying on manual methods that can be inefficient and error-prone, companies now have the ability to use intelligent systems to monitor, archive, and report communications with precision.

The regulatory landscape makes this shift more than just a smart move - it’s becoming a necessity. FINRA's 2025 Annual Regulatory Oversight Report emphasizes that AI can influence nearly every aspect of a member firm’s regulatory responsibilities, from combating financial crimes to bolstering cybersecurity measures. With the rise of AI-driven cybersecurity threats, the importance of strong cybersecurity programs has never been clearer.

Some of the standout benefits of AI-powered compliance systems include automated recordkeeping across various communication platforms and real-time policy enforcement. These capabilities help firms stay ahead of emerging threats while ensuring they meet essential compliance requirements. Platforms like Quartz illustrate how these systems work in practice.

Quartz offers a practical example of AI in compliance. By combining automated recordkeeping with proactive policy enforcement, it allows firms to archive and monitor communications on platforms like iMessage and WhatsApp. This ensures adherence to FINRA and SEC regulations through autonomous reporting, misuse detection, and seamless integration with existing compliance tools. Importantly, Quartz enables firms to enhance their compliance processes without needing to overhaul their current infrastructure.

As technology evolves, so do regulatory standards. Firms that adopt advanced AI enforcement systems are better positioned to tackle future regulatory challenges while maintaining operational efficiency. Investing in intelligent compliance tools today lays the groundwork for sustained regulatory success in an increasingly complex digital world.

FAQs

How can financial firms use AI to comply with FINRA regulations while addressing risks like data bias and cybersecurity threats?

To align with FINRA regulations, financial firms need to ensure their AI systems adhere to strict standards for recordkeeping and transparency. This involves conducting regular audits and updating AI tools to stay compliant. At the same time, implementing strong cybersecurity measures - like encryption, access controls, and advanced threat detection - helps safeguard sensitive data against breaches.

When it comes to tackling data bias, firms can take proactive steps such as using transparent algorithms, performing routine bias assessments, and establishing clear policies for ethical AI practices. It's also essential to stay up-to-date with changing regulatory frameworks and adjust internal procedures to reflect the latest AI applications and associated risks. These efforts not only support compliance but also help reduce the risk of potential challenges.

How can firms integrate AI tools into their compliance systems without disrupting daily operations?

To make the most of AI tools in compliance systems, companies should start by clearly outlining their compliance objectives and identifying specific tasks where AI can improve efficiency - like monitoring communications or analyzing data. Choose AI solutions that integrate smoothly with your current systems and have the capacity to grow alongside your organization.

Strong data management practices are a must. This includes setting up access controls and maintaining audit logs to protect sensitive information and meet regulatory standards. Involve key stakeholders early in the process, keep a close eye on how the AI performs, and stay ready to tweak workflows to avoid unnecessary disruptions. Following these steps can help businesses use AI effectively while ensuring operational stability and meeting compliance obligations.

How does AI support compliance with FINRA Rule 3110, and why is human oversight still important?

AI makes it easier for organizations to comply with FINRA Rule 3110 by automating how communications and activities are supervised and recorded. With real-time monitoring, it can quickly spot potential violations and maintain precise, thorough records - all while cutting down on manual work. This allows companies to meet regulatory requirements more efficiently.

That said, human oversight is still a critical piece of the puzzle. People are needed to interpret the context, review alerts generated by AI, and confirm that automated processes align with regulatory standards. By blending AI's speed and accuracy with human judgment, organizations can build a well-rounded and effective compliance approach.

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