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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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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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Compliance and AI

Compliance and AI: Episode 1 – Ant Stevens on Incorporating AI into Your Compliance Program

What is the role of Artificial Intelligence in compliance? What about Machine Learning? Are you using ChatGPT? We will explore these three questions in this exciting new podcast, Compliance, and AI. Hosted by Tom Fox, the award-winning Voice of Compliance, this podcast will look at how AI will impact compliance programs into the next decade and beyond. If you want to find out why the future is now, join Tom Fox on this journey to the frontiers of compliance.

In this inaugural episode of Compliance and AI, Tom Fox interviews the CEO and President of 6Clicks, Ant Stevens, who explains that generative AI refers to systems that transform inputs into outputs and generate something obvious, like an image, video, or text. The AI works based on an underlying corpus, a kind of brain or reference point. Generative AI outputs are generated based on a corpus of information, making them an effective tool for companies to improve risk and compliance management.

They discuss the latest version of Generative AI, GPT 3, which allows companies to generate more text, images, and videos. The conversation also delves into the benefits of AI in content creation and policy overview creation. The podcast emphasizes the importance of prompt engineering and human input in decision-making. Stevens shares his belief that we are only scratching the surface of what we can do with artificial intelligence and encourages companies to embrace its potential. Get ready to be empowered and leap into the exciting world of Compliance and AI.

Key Insights

1. Incorporate generative AI into your risk and compliance management systems. Generative AI can help automate the compliance process and reduce human error in tracking and managing compliance requirements.

2. Train employees on how to use generative AI platforms. Employees trained on generative AI platforms can better understand their compliance requirements and reduce the risk of violations.

3. Stay current with the latest developments in generative AI technology. Companies that keep up with the latest advancements in generative AI technology can better understand how it can impact their business operations and take advantage of new opportunities.

If you’re interested in learning more about the potential applications of generative AI in risk and compliance, you should listen to the podcast. Stevens shares his insights into how 6clicks uses generative AI to help companies manage risk and compliance requirements more effectively.

Key Quote

“Generative AI refers to systems that effectively transform inputs into outputs, and the outputs generate something obvious, whether it’s an image or video, a slap of text, something like that.”

Resources

Ant Stevens on LinkedIn

6Clicks

Tom Fox

Instagram

Facebook

YouTube

Twitter

LinkedIn

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Sunday Book Review

January 8, 2023 – The Top AI and Machine Learning Books for 2023 Edition

In the Sunday Book Review, I consider books that interest the compliance professional, the business executive, or anyone curious. It could be books about business, compliance, history, leadership, current events, or anything else that might interest me. In today’s edition of the Sunday Book Review, we consider some of the top AI and machine learning books that every compliance professional should read in 2023:

·       Future Ready: The Four Pathways to Capturing Digital Value by Stephanie L. Woerner, Peter Weill, and Ina M. Sebastian

·        Digitalization of Financial Services in the Age of Cloud by Jamil Mina, Armin Warda, Rafael Marins, and Russ Miles

·       Power and Prediction: The Disruptive Economics of Artificial Intelligence by Ajay Agrawal, Joshua Gans, and Avi Goldfarb

·        Practicing Trustworthy Machine Learning by Yada Pruksachatkun, Matthew Mcateer, and Subhabrata Majumdar

Resource

The Enterpriser’s Project- 10 must-read tech books for 2023

Categories
Daily Compliance News

November 20, 2021 the Morality and Machine Learning edition


In today’s edition of Daily Compliance News:

  • Business groups challenge FTC.(WSJ)
  • Will Holmes testify? (WSJ)
  • Rampant sexual harassment at Tesla? (Bloomberg)
  • Morality and machine learning. (NYT)
Categories
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.