Bankruptcy Prediction and Fraud Detection Using Cutting Edge Recursive Partitioning Techniques

Dr. Kuldeep Kumar

Professor of Economics
Bond University

About the Speaker

Dr. Kuldeep Kumar did his PhD from University of Kent at Canterbury and has taught at Indian Institute of Management, Lucknow and National University of Singapore before joining Bond University, Gold Coast, Australia in 1993.

Winner of several awards including Commonwealth Fellowship, Commission of European Countries Post Doc Fellowship, Young Statistician award of the International Statistical Institute which is given to three young statisticians in the whole world every two years, Bond-Oxford Fellowship and Vice Chancellor’s Quality award for research supervision, Dr. Kumar is also winner of several Teaching Excellence awards and Research Excellence awards. He has also won Lecturer of the year award and best research paper awards. Recently in 2013 he has won outstanding contribution award from the International Society of Management Engineers. Dr. Kumar is currently Professor and Head of Economics and Statistics Department in the Faculty of Business at Bond University.

Dr. Kumar has published more than 110 research papers in the international refereed journals/conference proceedings, eleven chapters in the books, 25 book reviews for Royal Statistical Society besides presenting papers in more than hundred seminars and conferences. He has been keynote speaker and chaired sessions in several conferences. He has also edited a special issue of Managerial Finance journal besides several conference proceedings. His current research interests are in the areas of bankruptcy prediction, financial fraud detection, forensic accounting and higher education. He has supervised many Masters and PhD students in this area. He has received several research grants in these areas. His papers are well cited in many journals and books. He has refereed papers for more than 30 top journals and is on the editorial board of several international journals.

Dr. Kumar is Fellow of Royal Statistical Society since 1984 and a chartered statistician. Recently he was awarded the status of Chartered Scientist by the Science Council of United Kingdom.

About the Seminar

Billions of dollars are lost every year due to bankruptcies and fraud related activities. Financial fraud and then the consequent bankruptcy of a business was one of the reasons for the financial crisis and is usually an extremely costly event. Downturn in financial and economic conditions has triggered a jump in the fraudulent activity in the corporate world. In this talk we will discuss some cutting edge recursive partitioning techniques to predict financial distress. Also many business decisions rely on the accuracy of financial statements, but resources are not available to comprehensively investigate all of them. Moreover, detection of fraud in financial statements is difficult. Consequently, there is a need for better aids such as detection models developed using supervised learning.

Using models developed in this research, financial statements can be automatically classified as either fraudulent or legitimate, as well as being ranked according to their likelihood of being fraudulent. This information can be used to improve early detection, which would mitigate the costs of fraud and help deter it from occurring by increasing the probability of being detected. Beneficiaries of this information include auditors, investors, financiers, employees, customers, suppliers, regulators, company directors and the financial markets as a whole through improved integrity and allocation of resources.