Banking industry has gone through one of the worst crisis in recent times, and is still recovering from the after-shocks. However, there were a lot of learnings that banks would have taken away from this crisis. One of them is the need for a robust risk management system. The crisis dealt a blow to the banking system, catching them off guard when it came to foreseeing the risk. Banks, in the credit card business, face financial risk in the form of both credit risk and fraud risk. Sharma and Agarwal (2013) proposed a model for predicting the credit risk from the merchants. This paper builds upon their technique to predict the fraud risk posed by the merchants to the banks. Fraud risk is an important aspect of risk management systems, particularly in the credit space. The uncertainty surrounding the receipt of paybacks calls for designing robust risk prediction models. Fraud risk is very different from credit risk because fraud risk does not follow a pattern. It happens suddenly, and may not always have a trend before it happens. This creates a need for separate model for fraud risk prediction. This paper develops a fraud risk prediction model that uses logistic regression technique, deployed using SAS. The setup of the study is the merchant-bank relationship in the credit card industry. The model developed in this paper triggers on a transaction level, and assigns a ‘probability score of default (PF) to each merchant for a possible fraud risk whenever a transaction is done at the merchant. Such a score warns the management in advance of probable future losses on merchant accounts. Banks can rank order merchants based on their PF score, and instead of working on the entire merchant portfolio, they can focus on the relatively riskier set of merchants. The PF model is validated by comparing the actual defaults with those predicted by the model and a good alignment is found between the two. The results show that the model can capture 62 percent frauds in the first decile when the transactions are sorted by the probability of fraud computed by the model.

No abstract

Release planning is part of iterative software development and strongly impacts the success of a product by providing a roadmap for future releases. As such, it is of key importance for lean and agile organizations. Often features are highly dependent on each other and the value of a release is influenced by a set of bundled features constituting a theme.This paper addresses the topic of theme-based release planning. Themes might be defined, manually, upfront or as the result of computer-based analysis. In this paper, we propose an analytical approach to detect themes from a given set of feature dependencies. On top of an existing release planning methodology called EVOLVE II, our approach applies clustering performed on a feature dependency graph. The release plans generated from such an approach are a balance between two goals: (i) considering the values of individual features, (ii) detecting and utilizing synergy effects between semantically related features.As a proof-of-concept, we present a case study addressing the theme-based release planning for 50 features of a text processing system. The preliminary evaluation results show improved release plans with regards to accommodating themes.

Business Accounting Seminar, and the Michigan State University Accounting Seminar for their valuable inputs across the various stages of this paper. We take complete responsibility of any error.

No abstract