Publications

Why Private Cryptocurrencies Cannot Serve as International Reserves but Central Bank Digital Currencies Can

Published in Economic Analysis Research Group (EARG). University of Reading, 2019

This paper begins by a recap on the ambition and mechanism behind Bitcoin, followed by an overview of the top 10 cryptocurrencies by market capitalization.

Recommended citation: Clark and Mihailov (June 2019). "Why Private Cryptocurrencies Cannot Serve as International Reserves but Central Bank Digital Currencies Can" https://aclarkdata.github.io/files/Why%20Private%20Cryptocurrencies%20Cannot%20Serve%20as%20InternationalReserves%20but%20Central%20Bank%20Digital%20Currencies%20Can.pdf

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

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

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

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

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

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