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2 Gurus Talk Compliance

2 Gurus Talk Compliance: Episode 31— AI, Compliance and Crypto

What happens when two top compliance commentators get together? They talk compliance, of course. Join Tom Fox and Kristy Grant-Hart in 2 Gurus Talk Compliance as they discuss the latest compliance issues in this week’s episode!

In this episode of 2 Gurus Talk Compliance Podcast, hosts Kristy Grant-Hart and Tom Fox discuss AI’s role in unmasking whistleblowers, the latest fallout from cryptocurrency firms under SEC scrutiny, advancements in tracking sanctioned commodities, and the humorous mishap involving a Florida man and laxatives. They also delve into the implications of workplace violence prevention laws, BP’s new office relationship rules, and check in on corruption and legal developments involving figures like Bob Menendez and Benny Steinmetz. Ending on a lighter note, a Florida man finds himself in trouble after substituting opioids with laxatives.

Stories Include:

  • Tyson Foods CFO was suspended for drunk driving. (Bloomberg)
  • 5 takeaways from Menendez trial.(CNN)
  • FAA says greater oversight needed over Boeing.(NYT)
  • Terraform settles with SEC for $4.5bn.(FT)
  • Beny Steinmetz profile.(OCCPR)
  • The Double-Edged Impact of AI Compliance Algorithms on Whistleblowing (National Law Review)
  • BP Tightens Rules Over Office Relationships in Wake of Former CEO’s Departure (WSJ)
  • Keeping Sanctioned Russian Timber Out of the EU Is Tricky. This Nonprofit Has a Solution (WSJ)
  • New York Bill Would Provide Protections Against Workplace Violence for Retail Employees (Seyfarth)
  • Florida Man Steals Constipation Drugs Thinking They Were Opioids (Florida has a right to know) 

Resources:

Kristy Grant-Hart on LinkedIn

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Compliance and AI

Compliance and AI: Lucas Croteau on AI and Reporting within Life Sciences Compliance

What is the role of Artificial Intelligence in compliance? What about Machine Learning? Are you using ChatGPT? These are but three of the many questions we will explore in this cutting-edge podcast series, Compliance and AI, hosted by Tom Fox, the award-winning Voice of Compliance.

In this episode, Tom visits Lucas Croteau, a leader in life sciences compliance.

This podcast delves into Lucas’s professional journey, his work with transparency reporting for companies, and his tenure with MediSpend, which led him to co-found TracedData. Croteau discusses his target market, primarily small to midsize pharmaceutical, medical device, and biotech companies, and the pressing need for transparency and compliance in these industries. The conversation also explores the role of artificial intelligence in compliance reporting, the challenges of managing regulatory requirements globally, and the importance of strategic partnerships for efficient compliance programs.

Key Highlights:

  • Lucas Croteau ‘s Professional Background
  • Founding TracedData and Market Needs
  • Making Compliance Easy with TracedData
  • Data Capture in Life Sciences Compliance
  • AI in Compliance Reporting
  • Global Regulatory Challenges
  • Future of Life Sciences Compliance

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Lucas Croteau on LinkedIn

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Innovation in Compliance

Innovation in Compliance: Jennifer Arnold on Optimizing Financial Crime Detection with Minerva

Innovation comes in many forms, and compliance professionals need to not only be ready for it but also embrace it. In this episode, Tom Fox visits with Jennifer Arnold, a seasoned anti-money laundering (AML) professional. Jennifer is a co-founder of Minerva, the sponsor of this episode. Minerva is an innovative investigation and screening platform.

Minerva is an invaluable tool for financial investigators, enabling quick and efficient data analysis to support informed decision-making. She discusses Minerva’s capability to search for critical data, such as adverse media and criminal activity, enhancing the investigator’s role through automation and speed. By combining the expertise of skilled investigators with advanced data science, Minerva significantly maximizes the effectiveness of AML investigations in today’s data-rich environment.

We take a deep dive into how Minerva integrates AI into its processes for detecting financial crime. The technology employs simple data aggregation to target relevant data sources, performing entity resolution for a nuanced and accurate view of clients. This approach minimizes false positives, streamlines work for the Financial Intelligence Unit and ensures that information examined is meaningful and precise.

Key Highlights:

  • Introduction to Minerva’s AI Integration
  • Data Aggregation and Intelligence
  • Entity Resolution and Contextual Data
  • Accurate Client Risk Assessment
  • Reducing False Positives

Resources:

Jennifer Arnold on  LinkedIn 

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Blog

Anti-Money Laundering in the Age of AI

In a recent episode of the podcast Innovation in Compliance, I had the pleasure of speaking with Jennifer Arnold, a leading expert in anti-money laundering (AML) and the co-founder of Minerva, a cutting-edge investigation and screening platform. Our conversation explored her professional journey, the current AML landscape, and how Minerva is leveraging AI to revolutionize financial crime investigations.

Arnold’s career began in some of Canada’s largest banks, including CIBC and BMO, and extended to Wells Fargo. Her role at these institutions involved designing and deploying anti-financial crime programs. However, this work’s manual nature and challenges led her to co-found Minerva. She said, “I grew incredibly frustrated with how that work was getting done…so I left, took my work best friend, and we started Minerva.”

Minerva, named after the Roman goddess of defensive battle strategy, reflects Arnold’s view of AML as a strategic defense mechanism. The company’s primary customers include financial services providers, banks, credit unions, centralized crypto exchanges, and fintech companies.

One of the most significant challenges financial institutions face is the pervasive issue of false positives. These are instances where a compliance system flags a transaction or individual as potentially suspicious despite no illicit activity. Dealing with false positives can be time-consuming and resource-intensive, diverting valuable investigative resources from genuine threats.

However, a new breed of AI-powered AML solutions is emerging to address this challenge head-on. One such innovative platform is Minerva, which has been specifically designed to tackle the false positive problem through the power of data and natural language processing. Arnold noted that using “data and natural language processing to distinguish between subjects, you can provide a nuanced view of risk and significantly reduce false positives.” This is a game-changer for compliance teams, who can now focus on high-priority, high-risk cases rather than chasing down false alarms.

The key is to leverage advanced AI and machine learning algorithms to analyze vast troves of data in real-time. Unlike traditional AML systems that often rely on static rules and rigid parameters, Minerva’s deep learning platform can dynamically adapt to the rapidly changing sanctions landscape and evolving financial crime tactics.

Arnold noted, “In the last 24 to 36 months, the volume and frequency of changes in sanctions lists have increased dramatically. “It’s crucial for technologies to access data in real-time to ensure compliance and mitigate risks effectively.” Minerva’s real-time data integration capabilities enable financial institutions to stay ahead of the curve, ensuring their AML programs are always up-to-date and responsive to the latest threats.

But data alone is not enough. Effective AML also requires robust identity verification (IDV) processes to establish a clear understanding of the customer and their associated risks. As Jennifer emphasized, “If you took a perfect look at the customer at the beginning of the relationship, you have a much better chance of understanding what risk is walking in your door.”

Using IDV capabilities to leverage AI and machine learning to analyze millions of data points, you can enable compliance teams to differentiate between subjects accurately. By creating a comprehensive and nuanced view of each customer, an entity resolution algorithm can significantly reduce false positives plaguing traditional AML systems.

Beyond identifying potential risks, any system must add documentation and compliance reporting as key outputs. Final reports provide a clear data lineage for every piece of information, allowing financial institutions to demonstrate their adherence to regulatory requirements. “If they wanted a roadmap to recreate the investigation, they have everything they need,” she said, highlighting the importance of this feature for compliance professionals who must regularly report to regulators.

As with all AI solutions and tools, the human element remains crucial. It should act as a “co-pilot, assisting investigators by automating routine tasks and providing rapid insights, but the final analysis and decision-making still rest with seasoned compliance experts.” Looking ahead, Arnold foresees a significant shift in the AML landscape, moving from a reactive to a more proactive, real-time approach. “To fulfill the promise of AML—identifying, detecting, deterring, preventing, and predicting financial crime—a move towards real-time data sharing and analysis is essential,” she said.

The evolving landscape of AML and innovative approaches are being developed to tackle financial crime. By leveraging advanced technologies to reduce false positives, access real-time data, and enhance identity verification, your organization can pave the way for a new era of compliance in which financial institutions can focus on what truly matters: protecting the integrity of the global financial system.

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Compliance Tip of the Day

Compliance Tip of the Day: Continuous Monitoring of AI

Welcome to “Compliance Tip of the Day,” the podcast where we bring you daily insights and practical advice on navigating the ever-evolving landscape of compliance and regulatory requirements.

Whether you’re a seasoned compliance professional or just starting your journey, our aim is to provide you with bite-sized, actionable tips to help you stay on top of your compliance game.

Join us as we explore the latest industry trends, share best practices, and demystify complex compliance issues to keep your organization on the right side of the law.

Tune in daily for your dose of compliance wisdom, and let’s make compliance a little less daunting, one tip at a time.

In today’s episode, we begin a weeklong look at some of the ways Generative AI is changing compliance and risk management. Today we consider some of the key challenges that organizations need to navigate to accomplish continuous monitoring of AI.

For more information on the Ethico ROI Calculator and a free White Paper on the ROI of Compliance, click here.

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Blog

AI in Compliance Week: Part 5 – Continuous Monitoring of AI

This blog post concludes a five-part series I ran this week on some of the keys intersecting AI and compliance. Yesterday, I wrote that businesses must proactively address the potential for bias at every stage of the AI lifecycle—from data collection and model development to deployment and ongoing monitoring. In this final blog post, I deeply dive into continuously monitoring your AI. We begin this final Part 5 with some key challenges organizations must navigate to accomplish this task.

As we noted yesterday, data availability and high data quality are essential. Garbage In, Garbage Out. Robust bias monitoring requires access to comprehensive, high-quality data that accurately reflects the real-world performance of your AI system. Acquiring and maintaining such datasets can be resource-intensive, especially as the scale and complexity of the AI system grow. However, this is precisely what the Department of Justice (DOJ) expects from a corporate compliance function.

How have you determined your key performance indicators (KPIs) and interpretation? Selecting the appropriate fairness metrics to track and interpret the results can be complex. Different KPIs may capture various aspects of bias, and tradeoffs between them can exist. Determining the proper thresholds and interpreting the significance of observed disparities requires deep expertise.

Has your AI engaged in Model Drift or Concept Shift? Compliance professionals are aware of the dreaded ‘mission creep. AI models can exhibit “drift” over time, where their performance and behavior gradually diverge from the original design and training. Additionally, the underlying data distributions and real-world conditions can change, leading to a “concept shift” that renders the AI’s outputs less reliable. Continuously monitoring these issues and making timely adjustments is critical but challenging. Companies will need to establish clear decision-making frameworks and processes to address model drift and concept shift.

Operational complexity is a critical issue in continuous AI monitoring. Integrating continuous bias monitoring and mitigation into the AI system’s operational lifecycle can be logistically complex. This requires coordinating data collection, model retraining, and deployment across multiple teams and systems while ensuring minimal service disruptions.

Everyone must buy in or in business-speak – Organizational Alignment must be in place.  Not surprisingly, it all starts with the tone at the top. Your organization should foster a responsible AI development and deployment culture with solid organizational alignment and leadership commitment. Maintaining a sustained focus on bias monitoring and mitigation requires buy-in and alignment across the organization, from executive leadership to individual contributors. Overcoming organizational silos, competing priorities, and resistance to change can be significant hurdles.

There will be evolving regulations and standards. The regulatory landscape governing the responsible use of AI is rapidly growing, with new laws and industry guidelines emerging. Keeping pace with these changes and adapting internal processes can be an ongoing challenge. Staying informed about evolving regulations and industry standards and adapting internal processes will be mission-critical.

The concept of AI explainability and interpretability will be critical going forward.  As AI systems become more complex, providing clear, explainable rationales for their decisions and observed biases becomes increasingly crucial. Enhancing the interpretability of these systems is essential for effective bias monitoring and mitigation. This will be improved by developing robust data management practices to ensure the availability and quality of data for bias monitoring. The bottom line is that companies should prioritize research and development to improve the explainability and interpretability of AI systems, enabling more effective bias monitoring and mitigation.

A financial commitment will be required, as continuous bias monitoring and adjustment can be resource-intensive. It requires dedicated personnel, infrastructure, and budget allocations and investing in specialized expertise, both in-house and through external partnerships, to enhance the selection and interpretation of fairness metrics. Organizations must balance these needs against other business priorities and operational constraints. Companies must allocate the necessary resources, including dedicated personnel, infrastructure, and budget, to sustain continuous bias monitoring and adjustment efforts.

Organizations should adopt a comprehensive, well-resourced approach to AI governance and bias management to overcome these challenges. This includes developing robust data management practices, investing in specialized expertise, establishing clear decision-making frameworks, and fostering a responsible AI development and deployment culture.

Continuous monitoring and adjusting AI systems for bias is a complex, ongoing endeavor, but it is critical to ensure these powerful technologies’ ethical and equitable use. By proactively addressing the challenges, organizations can unlock AI’s full potential while upholding their commitment to fairness and non-discrimination.

By proactively addressing these challenges, organizations can unlock AI’s full potential while upholding their commitment to fairness and non-discrimination. Continuous monitoring and adjusting AI systems for bias is a complex, ongoing endeavor, but it is a critical component of responsible AI development and deployment.

As the AI landscape continues to evolve, organizations prioritizing this crucial task will be well-positioned to navigate the ethical and regulatory landscape, build trust with their stakeholders, and drive sustainable innovation that benefits society.

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Blog

AI in Compliance Week: Part 4 – Keeping Your AI – Powered Decisions Fair and Unbiased

As artificial intelligence (AI) becomes increasingly integrated into business operations and decision-making, ensuring the fairness and lack of bias in these AI systems is paramount. This is especially critical for companies operating in highly regulated industries, where prejudice and discrimination can lead to significant legal, financial, and reputational consequences. Implementing AI responsibly requires a multifaceted approach beyond simply training the models on large datasets. Companies must proactively address the potential for bias at every stage of the AI lifecycle – from data collection and model development to deployment and ongoing monitoring.

Based upon what the Department of Justice said in the 2020 Evaluation of Corporate Compliance Programs, a corporate compliance function is the keeper of both Institutional Justice and Institutional Fairness in every organization. This will require compliance to be at your organization’s forefront of ensuring your AI-based decisions are fair and unbiased. What strategies does a Chief Compliance Officer (CCO) or compliance professional employ to help make sure your AI-powered decisions remain fair and unbiased?

The adage GIGO (garbage in, garbage out) applies equally to the data used to train AI models. If the underlying data contains inherent biases or lacks representation of particular demographic groups, the resulting models will inevitably reflect those biases. It would help if you made a concerted effort to collect training data that is diverse, representative, and inclusive. Audit your datasets for potential skews or imbalances and supplement them with additional data sources to address gaps. Regularly review your data collection and curation processes to identify and mitigate biases.

The composition of your AI development teams can also significantly impact the fairness and inclusiveness of the resulting systems. Bring together individuals with diverse backgrounds, experiences, and perspectives to participate in every stage of the AI lifecycle. A multidisciplinary team including domain experts, data scientists, ethicists, and end-users can help surface blind spots, challenge assumptions, and introduce alternative viewpoints. This diversity helps ensure your AI systems are designed with inclusivity and fairness in mind from the outset.

It would help if you employed comprehensive testing for bias, which is essential to identify and address issues before your AI systems are deployed. By Incorporating bias testing procedures into your model development lifecycle and then making iterative adjustments to address any problems identified. There are a variety of techniques and metrics a compliance professional can use to evaluate your models for potential biases:

  • Demographic Parity: Measure the differences in outcomes between demographic groups to ensure equal treatment.
  • Equal Opportunity: Assess the accurate favorable rates across groups to verify that the model’s ability to identify positive outcomes is balanced.
  • Disparate Impact: Calculate the ratio of selection rates for different groups to detect potential discrimination.
  • Calibration: Evaluate whether the model’s predicted probabilities align with actual outcomes consistently across groups.
  • Counterfactual Fairness: Assess whether the model’s decisions would change if an individual’s protected attributes were altered.

As AI systems become more complex and opaque, transparency and explainability become increasingly important, especially in regulated industries. (Matt Kelly and I discussed this topic on this week’s Compliance into the Weeds.) It would help if you worked to implement explainable AI techniques that provide interpretable insights into how your models arrive at their decisions. By making the decision-making process more visible and understandable, explainable AI can help you identify potential sources of bias, validate the fairness of your models, and ensure compliance with regulatory requirements around algorithmic accountability.

As Jonathan Marks continually reminds us, corporations rise and fall on their government models and how they operate in practice. Compliance professionals must cultivate a strong culture of AI governance within your organization, with clear policies, methods, and oversight mechanisms in place. This should include:

  • Executive-level Oversight: Ensure senior leadership is actively involved in setting your AI initiatives’ strategic direction and ethical priorities.
  • Cross-functional Governance Teams: Assemble diverse stakeholders, including domain experts, legal/compliance professionals, and community representatives, to provide guidance and decision-making on AI-related matters.
  • Auditing and Monitoring: Implement regular, independent audits of your AI systems to assess their ongoing performance, fairness, and compliance. Continuously monitor for any emerging issues or drift from your established standards.
  • Accountability Measures: Clearly define roles, responsibilities, and escalation procedures to address problems or concerns and empower teams to take corrective action.

By embedding these governance practices into your organizational DNA, you can foster a sense of shared responsibility and proactively manage the risks associated with AI-powered decision-making. As with all other areas of compliance, maintaining transparency and actively engaging with key stakeholders is essential for building trust and ensuring your AI initiatives align with societal values, your organization’s culture, and overall stakeholder expectations. A CCO and compliance function can do so through a variety of ways:

  • Regulatory Bodies: Stay abreast of evolving regulations and industry guidelines and collaborate with policymakers to help shape the frameworks governing the responsible use of AI.
  • Stakeholder Representatives: Seek input from diverse community groups, civil rights organizations, and other stakeholders to understand their concerns and incorporate their perspectives into your AI development and deployment processes.
  • End-users: Carsten Tams continually reminds us that it is all about the UX. A compliance professional in and around AI should engage with the employees and other groups directly impacted by your AI-powered decisions and incorporate their feedback to improve your systems’ fairness and user experience.

By embracing a spirit of transparency and collaboration, CCOs and compliance professionals will help your company navigate the complex ethical landscape of AI and position your organization as a trusted, responsible leader in your industry. Similar to the management of third parties, ensuring fairness and lack of bias in your AI-powered decisions is an ongoing process, not a one-time event. Your company should dedicate resources to continuously monitor the performance of your AI systems, identify any emerging issues or drift from your established standards, and make timely adjustments as needed. You must regularly review your fairness metrics, solicit feedback from stakeholders, and be prepared to retrain or fine-tune your models to maintain high levels of ethical and unbiased decision-making. Finally, fostering a culture of continuous improvement will help you stay ahead of the curve and demonstrate your commitment to responsible AI.

As AI is increasingly embedded in business operations, the stakes for ensuring fairness and mitigating bias have never been higher. By adopting a comprehensive, multifaceted approach to AI governance, your organization can harness this transformative technology’s power while upholding ethical and unbiased decision-making principles. The path to responsible AI may be complex, but the benefits – trust, compliance, and long-term sustainability – are worth the effort.

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Compliance Into the Weeds

Compliance into the Weeds: AI Accountability and Explainability

The award-winning Compliance into the Weeds is the only weekly podcast that takes a deep dive into a compliance-related topic, literally going into the weeds to more fully explore a subject.

Looking for some hard-hitting insights on compliance? Look no further than Compliance into the Weeds!

In this episode, Tom Fox and Matt Kelly delve into the recent speech by Michael Hsu, the head of the Office of the Comptroller of the Currency, on the accountability challenges posed by artificial intelligence in the banking sector.

The discussion highlights Hsu’s emphasis on the lack of a robust accountability framework for AI, illustrating the issue with the Air Canada chatbot incident. The conversation also touches on potential systemic risks AI could pose to the financial sector, the need for explainable AI, and the shared responsibility model used in cloud computing as a potential template for addressing these challenges. The episode underscores the necessity for compliance officers to ensure contracts and IT controls are in place and stresses the importance of developing trust and accountability mechanisms before widespread AI adoption.

Key Highlights:

  • AI Accountability: A Regulator’s Perspective
  • Case Study: Air Canada’s AI Mishap
  • Legal and Technological Challenges
  • Exploring Solutions and Shared Responsibility

Resources:

Matt on Radical Compliance

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Great Women in Compliance

Great Women in Compliance: Privacy and AI Compliance – A Principled Approach

In this episode of the Great Women in Compliance podcast, Hemma and Ellen host a roundtable with Hope Anderson, a partner in White & Case’s Data, Privacy & Cybersecurity Practice, and Jean Liu, Assistant General Counsel, Privacy, Safety, and Regulatory Affairs who joined Microsoft in 2023 as part of the Nuance Communications, Inc. acquisition.

Hope and Jean have a wealth of experience advising on privacy, AI, and data governance compliance issues, and they are well-positioned to leverage this experience in the wake of a rapidly evolving regulatory landscape. Hemma and Ellen didn’t waste a minute mining these two experts for practical tips and recommendations for those of us looking to get smart quickly and grapple with what seems like a behemoth task of keeping up with developments in technology and legislation while at the same time, making sure we don’t get left behind in learning to leverage AI in our functions.

Join us for an engaging ride through the ups and downs of privacy and AI compliance, and be inspired as we were by the great opportunities to develop new and exciting use cases while mitigating risk and the chance to unlock the power of responsible and ethical AI for our businesses.

Key Highlights:

  • Getting up to speed with the rapidly evolving regulatory landscape

  • The role of AI principles vs policies and procedures

  • Human Rights, Bias, and AI

  • Keeping the “Human in the Loop”

  • Thoughts on a US Federal AI or Privacy Law

  • Leveraging AI for Ethics and Compliance

  • Key resources and recommendations

Resources:

Join the Great Women in Compliance community on LinkedIn here.

Guest Bios:

Hope Anderson is a partner in White & Case’s Data, Privacy & Cybersecurity Practice, based in Los Angeles. She has extensive experience advising on all aspects of privacy and is at the forefront of Generative AI, advising on the technology’s legal implications and practical applications. A member of the Firm’s Global Technology Industry Group, Hope has extensive experience in privacy and product counseling. She advises on e-commerce, privacy by design, Generative AI, AR/VR, biometrics, analytics, and issues implicating consumer protection, marketing, and advertising laws.

Jean C. Liu is an Assistant General Counsel in the Privacy, Safety, and Regulatory Affairs division and joined Microsoft in 2023 as part of the Nuance Communications, Inc. acquisition. Immediately before its acquisition, Jean served as Nuance’s Vice President and Chief Legal, Compliance, and Privacy Officer, leading the global legal, compliance, and privacy functions. She developed and implemented data privacy policies and practices to ensure that customer and business data, including protected health information, is strictly governed and privacy is maintained. Jean has over 29 years of experience leading compliance and privacy programs, successfully managing data incidents, including regulatory investigations, and implementing best governance and risk management practices across multiple industries.

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Compliance Tip of the Day

Compliance Tip of the Day: AI Powered Internal Controls

Welcome to “Compliance Tip of the Day,” the podcast where we bring you daily insights and practical advice on navigating the ever-evolving landscape of compliance and regulatory requirements.

Whether you’re a seasoned compliance professional or just starting your journey, our aim is to provide you with bite-sized, actionable tips to help you stay on top of your compliance game.

Join us as we explore the latest industry trends, share best practices, and demystify complex compliance issues to keep your organization on the right side of the law.

Tune in daily for your dose of compliance wisdom, and let’s make compliance a little less daunting, one tip at a time.

In today’s episode, we begin a weeklong look at some of the ways Generative AI is changing compliance and risk management. Today we look at how to set up AI-powered internal controls from a compliance perspective.

 

For more information on the Ethico ROI Calculator and a free White Paper on the ROI of Compliance, click here.