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publications

Focusing IT Audit on Machine Learning Algorithms

Published in MISTI Internal Audit Insights, 2018, 2016

Machine learning algorithms are permeating our world. With applications in banking, investing, social media, advertising, and crime prevention, to name a few, these little black boxes are increasingly being used to inform and drive decisions about our lives and businesses.

Recommended citation: Andrew Clark. (2016). "Focusing IT Audit on Machine Learning Algorithms" MISTI Internal Audit Insights, 2016. https://misti.com/internal-audit-insights/focusing-it-audit-on-machine-learning-algorithms

Introducing Mario, an Internal Audit specific ETL and Data Mart Solution

Published in Self publish, 2017, 2017

Mario is an audit specific data mart which implements the AICPA Audit Data Standards. Built using exclusively open source software, Mario provides a flexible environment that is designed for Internal and External Auditor Journal Entry requirements in disparate system environments.

Recommended citation: Andrew Clark. (2017). "Introducing Mario, an Internal Audit specific ETL and Data Mart Solution" http://www.aclarkdata.github.io/files/MarioPaperFinal.pdf

The Machine Learning Audit - CRISP-DM Framework

Published in ISACA Journal Volume 1, 2018, 2018

Machine learning is revolutionizing many industries, from banking to manufacturing to social media. This mathematical optimization technique is being used to identify credit card fraud, tag individuals in photos and increase e-commerce sales by recommending products. Machine learning can be summarized as a computer recognizing patterns without explicit programming. For example, in traditional software engineering, the computer must explicitly be programmed via control statements (e.g., if this event happens, then do this), necessitating that the engineer design and implement the series of steps the computer will perform to complete the given task. However, when dealing with mass amounts of correlated data (two or more variables moving together or away from each other, e.g., the relationship between temperature and humidity), human intuition breaks down. With advances in computing power, the abundance of data storage and recent advances in algorithm design, machine learning is increasingly being utilized by corporations to optimize existing operations and add new services, giving forward-thinking, innovative companies a durable competitive advantage. This increased usage helps establish the need for machine learning audits. However, a standard procedure for how to perform a machine learning audit has yet to be created. Using the Cross Industry Standard Process for Data Mining (CRISP-DM) framework may be a viable audit solution.

Recommended citation: Andrew Clark. (2018). "The Machine Learning Audit - CRISP-DM Framework." ISACA Journal Volumne 1, 2018. https://www.isaca.org/Journal/archives/2018/Volume-1/Pages/the-machine-learning-audit-crisp-dm-framework.aspx

The Secret to Driving Value Through Artificial Intelligence

Published in MISTI Internal Audit Insights, 2018

Just how close are we to machine learning in Internal Audit? The better question is, “How much money and time do you have?”

Recommended citation: Sarah Swanson, Andrew Clark. (2018). &quotThe Secret to Driving Value Through Artificial Intelligence" MISTI Internal Audit Insights, 2018. https://misti.com/internal-audit-insights/the-secret-to-driving-value-through-artificial-intelligence?utm_term=The%20Secret%20to%20Driving%20Value%20Through%20Artificial%20Intelligence&utm_campaign=AR17-EB0116_USI&utm_content=email&utm_source=Act-On+Software&utm_medium=email&cm_mmc=Act-On%20Software-_-email-_-The%20Audit%20Report-_-The%20Secret%20to%20Driving%20Value%20Through%20Artificial%20Intelligence

Putting Machine Learning in Perspective

Published in ISACA Journal Author Blog, 2018

Machine learning is bantered around in the media often these days, many times erroneously. The key question that concerns auditors is not how to build machine learning algorithms or how to debate on the relative merits between L1 and L2 regularization, but rather, in what context is the algorithm operating within the business? Additionally, do we have assurance that it meets all regulatory and business constraints and fulfills the needs of the enterprise?

Recommended citation: Andrew Clark. (2018). "Putting Machine Learning in Perspective." ISACA Journal Author Blog, 2018. https://www.isaca.org/Journal/archives/2018/Volume-1/Pages/the-machine-learning-audit-crisp-dm-framework.aspx

Machine Learning Security Considerations and Assurance

Published in IT Business Net, 2018, 2018

Machine learning security is an emerging concern for companies, as recent research by teams from Google Brain, OpenAI, US Army Research Laboratory and top universities has shown how machine learning models can be manipulated to return results fitting the attackers desire. One area of significant finding has been in image recognition models.

Recommended citation: Andrew Clark. (2018). "Machine Learning Security Considerations and Assurance." IT Business Net, 2018. http://www.itbusinessnet.com/article/Machine-Learning-Security---Considerations-and-Assurance--5373956

talks

Where Open Source Meets Audit Analytics

Published:

Open source software is taking the computer science community and IT departments by storm. The breadth of options, the timeliness of updates, the price, and the sense of community are all contributing factors to the rise of open source computing. For many years audit analytics has been confined to the Computer Assisted Auditing Techniques, CAAT, software vendors ACL, IDEA and now Arbutus. However, these software programs require extensive training to use effectively, are not very flexible, and in most cases fail to provide the outcome auditors are expecting. Moving to an open source platform based around the python ecosystem allows for true customization of analytics, and provides a common language to interact with your IT department. By using the same set of tools, an auditing department can move from rudimentary AP duplicate tests all the way to advanced classification and clustering machine learning tests. Although the barrier to entry for open source software is higher than for most CAATs, with cross-functional collaboration, a truly customized, sustainable, and highly effective analytics program can be created.

Machine Learning for Auditors: What you need to know

Published:

Machine learning is a hot topic in today’s discourse with a myriad of economic and social implications. As it gains wider adoption, what does it mean for assurance professionals? With the proliferation of buzzwords and the black box nature of machine learning, Andrew will help you cut through the noise and understand what fundamental changes are occurring and what is still more hype than reality.

Where Open Source Meets Audit Analytics

Published:

Open source software is taking the computer science community and IT departments by storm. The breadth of options, the timeliness of updates, the price, and the sense of community are all contributing factors to the rise of open source computing. For many years audit analytics has been confined to the Computer Assisted Auditing Techniques, CAAT, software vendors ACL, IDEA and now Arbutus. However, these software programs require extensive training to use effectively, are not very flexible, and in most cases fail to provide the outcome auditors are expecting. Moving to an open source platform based around the python ecosystem allows for true customization of analytics, and provides a common language to interact with your IT department. By using the same set of tools, an auditing department can move from rudimentary AP duplicate tests all the way to advanced classification and clustering machine learning tests. Although the barrier to entry for open source software is higher than for most CAATs, with cross-functional collaboration, a truly customized, sustainable, and highly effective analytics program can be created.

The Machine Learning Audit

Published:

As it gains wider adoption, what does machine learning mean for internal auditors and their organizations? With the proliferation of buzzwords and the black box nature of machine learning, Mr. Clark will help you cut through the noise and understand what fundamental changes are occurring and what is still more hype than reality. The session will include an overview of what machine learning is, examine its current and potential impact on industries and organizations, and explain the need for an objective audit. The presentation will conclude with an example of what a machine learning audit would consist of, and what steps would be required to perform one.

The Machine Learning Audit - keynote

Published:

As it gains wider adoption, what does machine learning mean for internal auditors and their organizations? With the proliferation of buzzwords and the black box nature of machine learning, Mr. Clark will help you cut through the noise and understand what fundamental changes are occurring and what is still more hype than reality. The session will include an overview of what machine learning is, examine its current and potential impact on industries and organizations, and explain the need for an objective audit. The presentation will conclude with an example of what a machine learning audit would consist of, and what steps would be required to perform one.

Active Directory for Auditors

Published:

Active Directory is audited loosely during SOX and ITGC audits, however, it is misunderstood and often audited ineffectively and inefficiently. This presentation will provide an overview of Active Directory design and guidelines for auditing it.

Machine Learning for Auditors

Published:

Machine learning is permeating our world. As it gains wider adoption, what does it mean for assurance professionals? This session will help you cut through the buzzwords and discover how machine learning can be leveraged in audit and compliance.

Reinventing Auditing with Machine Learning

Published:

Internal Audit is responsible for providing the 3rd line of defense assurance over the effectiveness of controls in mitigating enterprise risks. We are primarily a judgment-based operation, relying on “humanness” to ascertain if risks are sufficiently being mitigated. This sort of environment makes it difficult to employ machine learning, given the ambiguity of decisions and the need for interpretability to back up decisions that were made. However, these limitations give us the ability to become more imaginative, finding unique ways to employ machine learning. In this talk, Andrew will provide two examples of prototypes being used in audit, an unsupervised machine learning exploratory “clustering” environment to provide insight into looking at data in new ways; and a supervised NLP model that classifies audit reports into different classes for use in reporting.

Machine Learning: What Assurance Professionals Need to Know

Published:

Machine learning has evolved past an esoteric technique worked on by academics and research institutes into a viable technology being deployed at many companies. Machine learning has been significantly changing the competitive landscape of business models worldwide, contributing to the demise of established business, such as Blockbuster, to creating entirely new businesses, such as algorithmic advertising. This presentation strives to address the questions of what assurance professionals need to know about this technology and how to provide assurance around machine learning implementations and its unique risks.

Big Data and other Buzzwords

Published:

With so much noise and buzzwords floating around regarding data analytics, it can be rather difficult to decipher between the signal (what is worthwhile) and what is only talk. Sometimes the rhetoric even starts within your organization, confounding the issue further. During Andrew’s session, he will provide attendees with the knowledge they need to tune out the bogus information while gleaning valuable insights for developing and deploying their audit analytics program. The presentation will conclude with tangible examples of a successful Manufacturing Audit Analytics program, and recommendations for how to get yours up and running. After attending, participants will be able to articulate how steps for setting up an analytics program within their departments, as well be armed with knowledge for educating senior leadership on the fundamental changes in technology that are occurring, and what is just marketing.

Machine Learning Risk Management

Published:

Machine learning algorithms are permeating our world. With applications in banking, investing, social media, advertising, and crime prevention, to name a few, these little black boxes are increasingly being used to inform and drive decisions about our lives and businesses. Machine Learning Risk Management is an often overlooked aspect of creating, deploying, and monitoring machine learning applications. Andrew will explain the dangers associated with an absence of controls during the machine learning process. He will then demonstrate how controls prevent modeling biases and suggest ways to develop and deploy machine learning applications with a control-centric, engineered approach.

AWS for Auditors

Published:

Cloud computing is becoming more prevalent as more enterprises embrace the public cloud and adopt it for some if not all of their computing and storage needs. This presentation will go over best practices for an enterprise grade AWS deployment.

The Machine Learning Audit

Published:

As it gains wider adoption, what does machine learning mean for internal auditors and their organizations? With the proliferation of buzzwords and the black box nature of machine learning, Mr. Clark will help you cut through the noise and understand what fundamental changes are occurring and what is still more hype than reality. The session will include an overview of what machine learning is, examine its current and potential impact on industries and organizations, and explain the need for an objective audit. The presentation will conclude with an example of what a machine learning audit would consist of, and what steps would be required to perform one.

Blockchain for Auditors

Published:

​Blockchain is a revolutionary technology for some industries and applications, however it is commonly misunderstand and confused with the cryptocurrency Bitcoin. Blockchain is the underlying technology that makes Bitcoin and other cryptocurrencies possible, but it can be used for much more than cryptocurrencies alone. This presentation explains what Blockchain is, how it works and expounds on the uses of blockchain technology outside of cryptocurrencies in order to equip IT auditors with the knowledge they need to advise their companies on blockchain implementations. Blockchain has several very real uses cases, such as in logistics and financial payments clearing, however there is a lot of misleading or false information out there. The basics between what a public and private blockchain is, how the chain operates, what are miners, what security considerations exist, etc. will be discussed, along with an overview of how to audit this technology.

Best Practices for AWS and IT Audit

Published:

​Cloud computing is becoming more prevalent as more enterprises embrace the public cloud and adopt it for some, if not all, of their computing and storage needs. This presentation will go over best practices for enterprise-grade AWS deployment and the risks associated with a cloud computing environment.

RVA Tech Talks

Published:

Andrew Clark is a co-founder and Chief Technology Officer of a machine learning assurance company called Monitaur. Monitaur is a Machine Learning Assurance platform addressing the needs of companies using models to make decisions in regulated…

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.