Dr Alexa Scherf

This course is compulsory on the MSc in Accounting and Data Analytics. This course is not available as an outside option to students on other programmes. This course uses controlled access as part of the course selection process. For information on controlled access courses, including eligibility, application processes, deadlines, and departmental contact details, please refer to the Controlled Access Courses webpage.

Pre-requisites:

Students must have completed AC416 before taking this course.

The course focuses on fraud identification, detection, and prevention. It covers several applications of forensic analytics to publicly-traded firms, including:

• detecting accounting fraud by combining financial and non-financial data
• predicting financial default and bankruptcy
• detecting insider trading around earnings announcements
• identifying red flags for contracting
• predicting internal control deficiencies and material weaknesses. 

The course will draw on cutting-edge research on each topic, and help students develop the necessary skills to leverage big data.

30 hours of seminars in the Winter Term.

This course has a reading week in Week 6 of Winter Term.

The course consists of 1.5-hour sessions delivered twice a week in the Winter Term, with a reading week in Week 6.  Students are expected to read and prepare cases in advance and be ready for in-class discussion.

Case analysis / study weekly.

Students are expected to prepare for each session in advance, having done the assigned readings and having prepared the assigned cases. This is meant to enhance their critical and discussion skills.

Students are also expected to complete data analysis tasks. This is meant to enhance their programming, data management, and data analysis skills.

There is no required textbook for the course. Indicative academic articles:

• Amiram, D., Z. Bozanic, and E. Rouen (2015). Financial statement errors: Evidence from the distributional properties of financial statement numbers. Review of Accounting Studies 20: 1540-1593.
• Beaver, W., M. Correia, and M. McNichols (2011). Financial statement analysis and the prediction of financial distress. Foundations and Trends in Accounting 5(2): 99-173.
• Beneish, M.D. (1999). The detection of earnings manipulation. Financial Analysts Journal 55 (5): 24-36.
• Blackburne, T., J. Kepler, P. Quinn, and D. Taylor (2021). Undisclosed SEC investigations. Management Science 67 (6): 3403-3418.
• Chakrabarty, B., P. Moulton, L. Pugachev, and X. Wang (2024). Catch me if you can: in search of accuracy, score and ease of fraud prediction. Working paper.
• Dechow, P, A. Hutton, and R. Sloan (1996). Causes and consequences of earnings manipulation: An analysis of firms subject to enforcement actions by the SEC. Contemporary Accounting Research 13 (1): 1-36
• Dechow, P., W. Ge, C. Larson, and R. Sloan (2011). Predicting material accounting misstatements. Contemporary Accounting Research 28 (1): 17-82.
• Doyle, J. ,W. Ge, and S. McVay (2007). Determinants of weaknesses in internal control over financial reporting. Journal of Accounting and Economics 44 (1-2): 193-223.
• Feng, M., C. Li, S. McVay, and H. Skaife (2015). Does ineffective internal control over financial reporting affect a firm's operations? Evidence from firms' inventory management. The Accounting Review 90(2): 529-557.

Exam (60%), duration: 120 Minutes, reading time: 15 minutes in the Spring exam period.

Project (40%) in Winter Term Week 11.

This component of assessment includes an element of group work.

There is an exam, to be completed in the Spring exam period, and a group project. The group project will test students’ conceptual understanding of the material, their ability to compile, manipulate, and analyze big data, their ability to draw and communicate insights from statistical analysis, and their ability to work as a team.