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M17.5 original car lab advisory board meeting (june 16 2020) v1 (003)

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CarLab Advisory Board Meeting

M17.5 original car lab advisory board meeting (june 16 2020) v1 (003)

  1. 1. 17.5th CarLab Advisory Board Meeting Webex call June 15, 2020
  2. 2. Agenda • Weather, News, Sports Miklos Vasarhelyi • Advancement of analytics in standards Helen Brown-Liburd • Current research projects: • -- Dark Owl/ Inbev/ B3 Arion Cheong • -- RPA projects Abby Zhang • -- Prefeitura Wenru Wang • -- PIOB Kevin Moffitt • -- GASB Ben Yoon • -- Covid Kelly Duan • Open Discussion • Coming Events Annual CAR Lab Advisory Board Meeting • Proposed date: November 5, 2020 50 WCARS at RBS in Newark – Nov. 6 & 7, 2019 2
  3. 3. PCAOB Data and Technology Update • PCAOB auditing standards are not precluding or detracting from firms’ ability to use, they acknowledge—that our current standards do not explicitly encourage the use of such tools, indicate when their use might be appropriate, or highlight related risks or pitfalls associated with their use. • Technology based tools inform the auditor’s risk assessment by providing different perspectives, exposing previously unidentified relationships that may reveal new risks, and providing more information to be used when assessing risks. – However, when performing certain risk assessment procedures these tools do not diminish the importance of addressing other requirements related to risk assessment that do not necessarily lend themselves to the use of tools • The audit evidence standard does not preclude the auditor from using technology-based tools to perform audit procedures more efficiently to obtain audit evidence 3
  4. 4. CARLab Project Report DarkOwl (Darkweb Data Collector) B3 (Brazilian Stock Exchange) AB InBev (Brewing Company) Darknet market activity monitoring and auditing • Darknet market postings (Blog+Forum+Paste) • Data analysis • Textual analysis Machine driven market manipulative activity • Real-time Transactions (All transactions – B3) • Data analysis (Time Series+ML) Fraud detection model development • Investigation Report • Free Beer • Data analysis • ML
  5. 5. RPA • P1: - automated selected substantive testings in the Employee Benefit Plan audits. This became Andrea's dissertation and we have some professional articles about it. • P2: - automated a type of confirmation procedure. The paper is published in IJAIS. • P3: - automated selected processes in audit planning of Single Audits. Chanta and Abby are in the process of paper revision. • P4: We automated a mundane task in the real estate audits. After going through challenges of automation within virtual machines and dealing with an audit software that is not automation-friendly, we are able to automate a process that usually takes 15-20 min to 5 min. P4 is conducting tests now. • P5. Potentially automate an audit documentation procedure that involves moving data from word documents to a cloud-based internal audit software. Now we are waiting for documentation and data. (new phd students, Fangbing and Lanxin, will be in this project) • P6: Potentially automate an administration task for auditors. We are in the process of discussing potential solutions. (new phd students, Fangbing and Lanxin will be in this project)
  6. 6. Continuous Monitoring and Audit Methodology for Medication Procurement Wenru Wang – Rutgers University Miklos A. Vasarhelyi – Rutgers University June 15, 2020
  7. 7. Overview • Prefeitura Rio de Janeiro • 30,000+ Medication procurement data, 2017 – 2019 • Audit analytics for data preprocessing • Continuous monitoring and audit system for exception and anomaly detections 7
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  11. 11. COVID-19 Procurement • What has happened (descriptive analytics) • What will happen (predictive analytics) • What is the optimized solution (prescriptive analytics). • Data cleaning – Text mining, machine learning. 11
  12. 12. GASB Post-Implementation Review Project Ben Yoon (Ben Yoon, Kathy Wei, Huaxia Li, Prof. Kevin Moffitt, Prof. Miklos Vasarhelyi, and Prof. Irfan Bora) June 2020
  13. 13. • Are pensions safe?  In 2012, the GASB announced the pension standards for more transparent reporting (No. 67 and 68). Background
  14. 14. • In 2012, the GASB announced the pension standards for more transparent reporting (No. 67 and 68). • Are all the entities following the GASB’s new pension standards? - It would be useful if the GASB can monitor the CAFRs. - But there are challenges: 1) The CAFR reports are PDF documents and long (100-300 pages). 2) Different CAFRs uses different formats. 3) CAFRs are scattered in the Internet. Project objective
  15. 15. • This project consists of 4 steps. Collect CAFRs Convert CAFRs Analyze CAFRs (117 pre-defined items) Create reports • Rutgers has conducted initial pilot tests. - Step1: Collecting CAFRs from multiple repositories - Step2: Converting PDF documents - Step3: Extracting items from the CAFRs - Step4: Report with Excel format 4 steps of this project
  16. 16. Converting PDF • Conversion into MS-Excel format • Illustrative example
  17. 17. Extracting information
  18. 18. Continuous Intelligent Pandemic Monitoring (CIPM) 18 Presented by: Huijue Kelly Duan
  19. 19. Background 19 • With the current outbreak of COVID-19 pandemic, authorities are struggling for accurate and timely information in order to control the spread of the epidemic and provide adequate instructions to the communities. • The fundamental issue is that the key metrics used to guide policy and action are dimensionally incorrect for a multitude of reasons – The number of contaminated people – Hospitalizations and heath cases – The number of people who are asymptomatic • Tremendous filtering on who gets tested; lacking in sufficient test kits
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  21. 21. Research Objectives 21 • Use measurement science (accounting), assurance science (auditing) to examine the situation • Apply AI-based predictive analytics to epidemic research • Accounting methodologies + machine learning algorithms – Continuous Monitoring – Multidimensional Audit Data Selection (MADS) – Machine learning and artificial neural networks • This study aims to – the augment of information quality and timeliness – the provision of adequate policy
  22. 22. Framework 22 Data Collection • Establish a timely repository database of relevant exogenous and endogenous data sources Office announcement, social media posts, personal GPS data, Google flu trend, fever data based on thermometer reading apps, medical supplies purchase amount, economic related data, etc. Model Construction • Create a systematic and continuous COVID-19 monitoring model by using different epidemic models and machine learning algorithms  Classic Epidemic Models (SIR, SEIR, separable-temporal exponential-family random graph models)  AI predictions (SVM, XGBoost, Neural Network models)  Economic Impact Alerts • Incorporate audit risk assessment to establish an alert system Action Recommendations • Incorporate the knowledge obtained from different analysis to provide different levels of preventive policies and strategies regarding the pandemic, economics, as well as medical resources related recommendations.
  23. 23. 23 Figure 1:Utilizing the SIR model to predict the total number of confirmed cases in United States; and the inflection point after the outbreak (This model does not consider the exogenous variables) Figure 2: simulating the impacts of different social distancing policies on infected cases assuming the total number of population is equal to 1000 (This model does not consider the exogenous variables)
  24. 24. Innovation and Strategic Advantages • Following the accounting frameworks, this study aims to establish a continuous monitoring system for COVID-19 – The continuous monitoring and alert activation process is close to real- time, which enables earlier reaction to the signals – The monitoring activities are continuous, which helps preventing future outbreaks • The model uses Internet of Things (IoT) to gather the relevant exogenous data to perform comprehensive data analytics • Depending on the specific contingencies, action recommendations on quarantine and economy support will be predictively and automatically provided 24
  25. 25. Conclusions • We Have new leadership • Many organizations are jumping into the fray • We need to deal better with the bureaucratic challenges of Rutgers • COVID makes us more important but creates many challenges • Our teaching mission is becoming more important • Many opportunities are available • WE NEED YOUR ADVICE

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