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Leveraging Machine Learning with the Right Internal Audit Solution

Visitors face an ever-expanding landscape of challenges and opportunities in today’s world. Machine learning (ML) represents a transformative force, offering new ways to enhance audit quality, efficiency, and insight. But how can internal auditors effectively integrate this technology into their workflows? The key lies in choosing the right internal audit solution that seamlessly incorporates ML capabilities, ensuring auditors are equipped to tackle today’s complexities while preparing for tomorrow’s challenges.

Machine learning (ML) is a subset of artificial intelligence that focuses on developing systems that can learn from and make decisions based on data. In internal auditing, ML can automate repetitive tasks, identify patterns in large datasets, and even predict future trends. This not only speeds up the audit process but also enhances the accuracy and depth of audit insights.

Key Applications of Machine Learning in Internal Audits:

  • Risk Assessment: ML algorithms can analyze vast amounts of data to identify risk patterns and anomalies, helping auditors focus on areas with the highest risk.
  • Control Testing: Automated ML tools can test controls more frequently and thoroughly than manual processes, increasing the likelihood of detecting control failures.
  • Fraud Detection: ML can help predict and identify fraudulent activities based on historical audit data, thereby reducing potential losses.
  • Predictive Analytics: ML can forecast potential non-compliances or areas where controls might fail, allowing auditors to be proactive rather than reactive.

Selecting the right software solution is crucial when integrating ML into internal auditing. There are some critical factors to consider. The ML-powered audit solution must seamlessly integrate with IT infrastructure and data systems. This integration ensures auditors can leverage ML capabilities without disrupting existing workflows or data integrity. As organizations grow and data volumes increase, the ML solution should be able to scale accordingly. This includes handling more extensive datasets and adapting to new audits and compliance requirements.

ML can be complex, but the user interface of the audit solution should be different. A user-friendly interface that simplifies complex processes allows auditors to utilize ML features effectively without needing specialized training. Your chosen solution should offer advanced data analytics features, including data visualization tools, which help auditors make sense of the patterns and anomalies detected by ML algorithms. These tools are crucial for translating ML insights into actionable audit decisions. Any ML solution must comply with relevant data protection regulations, such as GDPR in the European Union or HIPAA in the United States. Additionally, the solution should have robust security measures to protect sensitive audit data from unauthorized access or breaches.

If there is one overlap between ML and traditional internal audit, it is that solutions for internal audit are not static, and ML is no different. ML continuously learns from new data and auditing experiences. This capability ensures that the system evolves and improves its accuracy and effectiveness. Finally, tech support is critical, especially when deploying complex technologies like ML. The right solution provider should offer comprehensive support and training, helping audit teams fully understand and leverage ML capabilities.

Successfully implementing an ML-powered audit solution involves more than just selecting the right software; you should have a planned strategy for an effective implementation. Some strategies for effective implementation include engaging relevant stakeholders early in the process, including IT, compliance, and executive teams, to ensure alignment and address any concerns. Test before implementation so that pilot tests of the ML solution can be conducted in specific audit areas before a full rollout. This helps identify any issues and refine the system for better performance. Training on any new system is critical, especially with an advanced ML solution. You should provide extensive training and support to audit staff to help them adapt to the latest tools and processes.  But as with any new rollout, it does not stop with implementation, as there should be continuous monitoring and continuous improvement as warranted.  Change management practices can facilitate a smoother transition and higher adoption rates.

As the complexity of business environments and regulations continues to grow, the role of internal audit becomes increasingly critical. Leveraging machine learning within audit solutions offers a path forward to keep pace with these changes and stay ahead of them. By choosing the right ML-powered internal audit solution and implementing it thoughtfully, audit departments can transform operations, delivering more value and stronger organizational compliance. The future of internal auditing is not just about adapting to changes—it’s about leading the charge with innovation and insight.

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Compliance Tip of the Day

Compliance Tip of the Day: Machine Learning for Internal Audit

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 consider how to best use machine learning both for internal audits and external audits.

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

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Elevating Your Risk Assessment Game with AI and Machine Learning, Part II

We conclude this two-part blog post on using Artificial Intelligence (I) and Machine Learning (ML) in risk assessments. By embracing AI and machine learning, compliance professionals can elevate their risk assessment capabilities, drive more informed decision-making, and position their organizations for long-term success in an increasingly complex and volatile business landscape. Today, we conclude with how to use these tools and some use cases.

When adopting AI-powered risk assessment solutions, compliance functions will face several key challenges, which can be addressed through a well-planned and strategic approach. Key challenges include implementing a robust data governance framework to ensure data quality, integration, and accessibility across the organization. Invest in data cleansing, normalization, and enrichment processes to prepare the data for AI models. You must be able to demonstrate how you got to certain decisions. To do so, you can use tools such as decision trees or logistic regression to explain their decision-making process better.

Your risk management model should ensure the accuracy, reliability, and fairness of the AI-powered risk assessment. To do so, you can establish a comprehensive model validation and governance framework, which includes regular performance monitoring, stress testing, and bias testing. The model validation process involves cross-functional teams, including risk experts, data scientists, and compliance professionals.

Multiple compliance areas lend themselves to use cases for AI and machine learning in risk assessment.

  1. Fraud Detection and Prevention. Machine learning algorithms can analyze transaction data, user behavior patterns, and other relevant information to identify suspicious activities and detect potential fraud in real-time. AI-powered anomaly detection can flag unusual transactions or account activities that deviate from the norm, allowing organizations to investigate fraud risks quickly and mitigate them.
  2. Vendor and Third-Party Risk Management. AI can rapidly assess the risk profiles of vendors, suppliers, and other third parties by aggregating and analyzing structured and unstructured data from various sources, including news reports, social media, and regulatory filings. Machine learning models can continuously monitor third-party relationships, detect changes in risk factors, and provide dynamic risk scoring to support vendor due diligence and ongoing risk mitigation.
  3. Compliance and Regulatory Risk. AI-driven natural language processing can help organizations stay on top of evolving regulatory requirements by automatically scanning and interpreting new laws, regulations, and industry guidelines. Machine learning can assist in identifying potential compliance gaps, policy violations, and other regulatory risks by analyzing internal data, such as employee activities, communications, and transactions.
  4. Operational Risk Assessment. AI and machine learning can model and simulate complex business processes, identify potential points of failure, and predict the likelihood and impact of operational disruptions. These technologies can also be leveraged to monitor and analyze real-time data from IoT devices, sensors, and other operational systems to detect anomalies and emerging risks.
  5. Enterprise Risk Management. AI-powered risk aggregation and correlation analysis can help organizations gain a more holistic, enterprise-wide view of their risk landscape, identifying interdependencies and potential risk concentrations. Machine learning algorithms can assist in prioritizing risks based on factors such as likelihood, impact, and velocity, enabling more informed decision-making and resource allocation.
  6. Emerging Risk Identification. AI and machine learning can scour vast amounts of external data, including news, social media, and industry reports, to identify emerging risks and trends that may not be apparent through traditional risk assessment methods. These technologies can also simulate future scenarios and stress test the organization’s resilience against potential black swan events or disruptive changes in the business environment.

By focusing on these traditional corporate risks, compliance professionals can enhance their risk assessment capabilities, improve decision-making, and better position themselves to navigate the increasingly complex and dynamic risk landscape. Integrating AI and machine learning into risk assessment requires a strategic, well-planned approach, commitment to continuous improvement, and a culture of innovation.

As you embark on this transformative journey, remember that integrating AI and ML is not a one-time event but a continuous refinement, learning, and adaptation process. Stay agile, keep an open mind, and be prepared to navigate the evolving compliance and risk management landscape.

The future of risk assessment is here, and it is powered by the extraordinary potential of artificial intelligence and machine learning for compliance professionals. Embrace this opportunity to unlock new levels of insight, efficiency, and proactivity – and lead your organization towards a more resilient and compliant future.

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Blog

Elevating Your Risk Assessment Game with AI and Machine Learning, Part I

I am on a mission to explore how AI and machine learning (ML) can impact the compliance profession, the compliance profession, and the corporate compliance function. Today, I want to explore using AI and ML in risk assessment. I believe that they both have the potential to transform the way we approach risk identification, analysis, and mitigation. By harnessing the capabilities of AI and ML, compliance teams can elevate their risk assessment game and position their organizations for long-term success. Today, in Part I, we consider why you should utilize AI and ML in your risk assessment process and the first steps to take.

For years, organizations have relied on manual, human-driven risk assessment approaches. This often involves painstaking data gathering, expert interviews, document reviews, and applying risk frameworks and methodologies. While these time-tested methods have their merits, they are inherently limited in several ways:

  • Subjectivity and Bias: Human risk assessors bring their own experiences, perspectives, and biases to the table, which can lead to inconsistent or skewed risk evaluations.
  • Scalability Challenges: As businesses grow in size and complexity, manually assessing every risk factor becomes overwhelming and resource-intensive.
  • Reactivity vs. Proactivity: Traditional risk assessment tends to be retrospective, focusing on known or historical risks. Anticipating emerging threats requires a more forward-looking, proactive approach.
  • Lack of Real-Time Responsiveness: The pace of change in today’s business environment means that risk profiles can shift rapidly. Manual processes may need help to keep up with these dynamic conditions.

AI and ML offer promising solutions to overcome the limitations of manual risk assessment. By leveraging these technologies, compliance teams can identify a more significant overall set of risks. AI-powered systems can scour vast internal and external datasets to uncover potential risk factors that human analysts may have overlooked. Machine learning algorithms can identify patterns, anomalies, and correlations, providing a more comprehensive, data-driven view of the risk landscape.

However, it is not simply the ability to uncover more risks through greater data sets but also the ability to use AI and ML tools. Compliance professionals can quantify and model risk variables with greater precision, considering a broader range of factors and their interdependencies. This allows for more accurate risk scoring, prioritization, and scenario planning. This leads directly to anticipating emerging threats and vulnerabilities, empowering organizations to take proactive measures.

Consistency and objectivity are critical for any risk assessment. In this area, AI and ML-based systems can apply consistent, standardized risk assessment methodologies, reducing the impact of individual biases and subjectivity. Automated risk assessment powered by AI and ML can also process large volumes of data and handle complex risk evaluation tasks, freeing compliance professionals to focus on strategic decision-making. The goal is to move towards a more continual monitoring system, and here,  AI-driven risk assessment can be integrated into real-time monitoring and alert systems, allowing organizations to quickly identify and respond to changes in their risk profiles.

How does a compliance function implement all of this AI and ML? There are several steps you should consider.

  • Assess Your Data Readiness: Effective AI and ML-powered risk assessment relies on high-quality, structured data availability. The DOJ mandates that you have access to your company’s data, including identifying any gaps or limitations and developing a plan to enhance data governance and management.
  • Identify Use Cases and Prioritize: Conduct a thorough analysis of your risk assessment needs and pain points. In other words, what are your high-risk areas? Determine which specific areas – such as fraud detection, vendor risk management, or third parties – could benefit the most from AI and ML-driven solutions.
  • Evaluate and Select the Right Tools: Research and evaluate a range of AI and ML-powered risk assessment platforms and solutions. Consider factors like integration capabilities, user-friendliness (it’s all about the UX), scalability, and the provider’s track record in compliance and risk management.
  • Pilot and Iterate: Start with a targeted pilot project to test the viability and effectiveness of your chosen AI and ML-based risk assessment approach. (Hint: Start small with a low-risk target.) Closely monitor the results, gather feedback, and continuously refine the solution to optimize its performance.
  • Train Your Team: Ensure compliance and risk management professionals have the necessary skills and knowledge to effectively leverage AI and ML technologies. Invest in training, workshops, and collaboration with data science and technology experts.
  • Establish Governance and Oversight: Develop robust governance frameworks to ensure the responsible and ethical use of AI and ML in risk assessment. This includes addressing algorithm bias, data privacy, and human oversight.
  • Foster a Culture of Innovation: Encourage a mindset of continuous improvement and experimentation within your compliance function. Empower team members to explore new ways of leveraging emerging technologies to enhance risk assessment and drive organizational resilience.

Join us tomorrow to consider implementation and some compliance use cases.

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Compliance Tip of the Day

Compliance Tip of the Day: Why Use Ai and ML in Risk Assessments?

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 this episode, we consider why you should move away from human-driven risk assessment to AI and ML-assisted risk assessments.

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

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Innovation in Compliance

Innovation in Compliance: Steve Brown on AI Integration for Evolving Compliance Challenges

Innovation comes in many forms, and compliance professionals need to not only be ready for it but also embrace it.

Today, Tom Fox visited Steve Brown, Managing Director and Head of Business Development at StarCompliance, the sponsor of this podcast. We take a deep dive into utilizing AI, machine learning, and generative AI for compliance prevention, detection and much more.

Steve Brown has maintained a lengthy career in the compliance industry, currently holding the position of Head of Business Compliance and Business Development at StarCompliance.

Throughout his career of over two decades, he has served in important roles in investment banking compliance and advised global firms on risk and regulatory issues.

StarCompliance, a company that specializes in employee compliance software across 117 countries, delves into the importance of compliance culture within corporations, the role of technology in enhancing compliance efforts, and the unique perspective Brown brings to technology solutions from his extensive compliance background.

They also touch upon the significance of a comprehensive compliance solution and the future of compliance technology, including the potential applications of AI, machine learning, and data analytics to anticipate and navigate regulatory challenges.

Key Highlights:

  • Global Ethics Compliance Solutions by StarCompliance
  • AI Integration for Evolving Compliance Challenges
  • Data-Driven Compliance Culture Enhancement Solutions
  • Ethical Culture through Integrated Compliance Analytics

Resources:

Steve Brown on LinkedIn 

StarCompliance

Tom Fox

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Data Driven Compliance

Data Driven Compliance: Vincent Walden – Analyzing the Philips FCPA Enforcement Action Using AI

Are you struggling to keep up with the ever-changing compliance programs in your business? Look no further than the award-winning Data Driven Compliance podcast, hosted by Tom Fox, is a podcast featuring an in-depth conversation around the uses of data and data analytics in compliance programs.

Data Driven Compliance is back with another exciting episode featuring the insightful Vince Walden from KonaAI. In this episode, Walden and host Tom Fox discuss how data analytics can help uncover potential FCPA enforcement actions, using the Philips case as an example. They delve into the benefits of internal controls and the segregation of duties to prevent bribery and corruption. Walden goes on to examine the customer 360 model, which focuses on analyzing customer orders to pinpoint risky transactions and potential improper payments. Additionally, they explore Kona AI’s platform, which utilizes advanced algorithms to pick up problems and highlight high-risk transactions.

The podcast also features a discussion on the use of artificial intelligence and how machine learning can help compliance professionals identify anomalies that require investigation. You won’t want to miss the exciting upcoming episode where Walden showcases real-world examples of how companies can use machine learning in 2023.  Tune in to Data Driven Compliance and stay ahead of the curve in the compliance world!

Key Highlights

·      Data analytics for FCPA compliance detection

·      Kona AI’s Customer Analytics and Risk Assessment

·      Improper Vendor Payments Tracking

·      The importance of second level reviews in internal control

·      Analytics and Investigating Fraud Potential

·      Improving Precision in Machine Learning Models

KEY QUOTES

“Just those basic type of analytics could have been easily spotted these issues.”

“These are the types of things that when you could just sort, you would be able to find those high risk transactions.”

“Nowadays the technology is there to spot these types of activities when compliance has access to the data.”

“Let’s see if this event took place. And he just did a simple Google search on the Internet couldn’t find the event.”

Resources:

Vince Walden on LinkedIn 

KonaAI

 Tom Fox 

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Blog

Utilizing Machine Learning and AI in Your GRC Practice

I recently had the chance to visit with Andrew Robinson to discuss utilizing ML and AI into your GRC practice for a sponsored podcast.  Robinson is the co-founder and Chief Information Security Officer at 6clicks. You can check out Robinson’s podcast episode here.
We began with the very basic proposition that many compliance professionals and others are scared by AI in the GRC space. Robinson believes it is based on the fear of the unknown, both to many inside and outside of GRC. Yet, increasingly GRC professionals see how AI and ML can be used within reg tech, technology companies, as well as in the compliance space to move forward through taking advantage of natural language processing. Robinson explained this is a component of ML that can help understand text. There is a lot of text in the world of compliance. When you can then overlay an AI component on all the standards, laws, and regulations any multi-national organization must follow, you begin to see the power of such a tool.
We next turned to dealing with compliance across multiple jurisdictions. For GRC professionals working internationally, Robinson said they must “maintain mappings or what you commonly call in the US ‘crosswalks of compliance’ frameworks.” He went on to explain these frameworks are “useful because it can allow a consultant to help a client understand how they might stack up against a particular standard. Robinson provided the example that if an organization is already complying with ISO 27,001, through these mappings, it might be able to give them an idea about what that level of compliance they have through the lens of a different framework or standard that may be relevant like the NIST cybersecurity framework.”
Yet the 6clicks approach is much more than a regulatory approach. It is a business centered approach which provides discreet business advantages. Indeed, this is one of the reasons I find the 6clicks approach so exciting as it creates a business advantage by performing quality GRC. These tools increase efficiency and profitability. Robinson went further noting, that “we come out with a public estimate of 10 times saving in using machine learning to assist with building up GRC mapping.” That is some serious productivity savings and increase.
However, this productivity increase and potential cost saving does not remove the human element. This final concept is critical in moving forward. Robinson said, “I’m of the view that humans have a very important role to play. This role is supervising the machine learning models to make sure that what they are producing and the results that they are coming out with are accurate and reliable.” If they are using spreadsheets and word documents; they should, come to terms with the fact that companies and clients no longer want spreadsheets and word documents as a deliverable. GRC professionals and consultants need to need to start using similar tools and improving the way that they service their clients. Clients, both in-house and external, are starting to demand and look for this approach. Robinson noted, “the reality is that if you are doing anything else it will be seen as subpar, and no one wants to be delivering sort of subpar products. I look for a solution that can meet your customer expectations and help you deliver your services long into the future.”
We concluded by looking at GRC tools with ML and AI at a strategic level, at the senior executive level and even at the Board of Director level. Robinson feels that management at this level “understands the benefits because they understand the problem.” Their goals are to simplify compliance while understanding risk exposure. From this point, management can move to create a risk-based solution. Robinson believes, these are the types of “business problems that executives are dealing with on a daily basis. Having awareness of the machine learning model can help them navigate that complexity.” From where I sit, when you can take a tool that improves business process efficiency and use it to increase profitability through more effectual risk management it is a win for everyone.
For more information on 6clicks, check out their website here.