Online payment fraud:from anomaly detection to risk management

Online banking fraud occurs whenever a criminal can seize accounts and transfer funds from an individual’s online bank account.Successfully preventing this requires the detection of as many fraudsters as possible,without producing too many false alarms.This is a challenge for machine learning owing to the extremely imbalanced data and complexity of fraud.In addition,classical machine learning methods must be extended,minimizing expected financial losses.Finally,fraud can only be combated systematically and economically if the risks and costs in payment channels are known.We define three models that overcome these challenges:machine learning-based fraud detection,economic optimization of machine learning results,and a risk model to predict the risk of fraud while considering countermeasures.The models were tested utilizing real data.Our machine learning model alone reduces the expected and unexpected losses in the three aggregated payment channels by 15%compared to a benchmark consisting of static if-then rules.Optimizing the machine-learning model further reduces the expected losses by 52%.These results hold with a low false positive rate of 0.4%.Thus,the risk framework of the three models is viable from a business and risk perspective.

Forensic Accounting and Fraud： Capability and Competence Requirements in Malaysia

The 2011 Global Economic Crime Survey instituted by PricewaterhouseCoopers （PwC） confirms the economic crime in Malaysia to be on the increase and, therefore, requires immediate attention to stem the tides. In anticipation of the challenges occasioned due to a shift from the modified cash basis to the accrual basis of accounting, the Malaysian State＇s determination to move from a developing nation to a developed nation, and to be ranked among the first 10 in 2020, this paper presents the need for forensic accountant and auditor capability （i.e., mindset and skills） on forensic accountant and auditor competence （i.e., task performance fraud risk assessment （TPFRA）） in the Malaysian public sector. It also draws the attention of the users of public sector accountants and auditors to the understanding of fraud mechanisms and how to deal with fraudsters. The population of this study comprised the accountants and auditors in the office of the Accountant General and Auditor General of Malaysia. The objective of this paper is to investigate the competence requirements of accountants and auditors in the effective and efficient utilization of capability requirements, which have the potentials to usher in the best global practices in fighting fraud in the Malaysian public sector.