Improving Credit Risk Analysis with Cluster Based Modeling and Threshold Selection

Credit risk has been an integral part of financial industry and is a challenging and difficult risk to manage. The diverse behavior of borrowers adds challenges to the risk analysis. Failing to accurately identify the borrowers\u27 risk can lead to huge investment losses. Credit scoring is a popular and commonly used technique to analyze credit risk. A single credit scoring model may not be capable of generating a common rule to classify borrowers and hence segmented modeling can be applied to create more specific classification rules for achieving higher classification accuracy. In this study segmented modeling is applied with threshold selection for each segment to reduce relative cost of misclassification. The results from the study show that threshold selection based on the segmented modeling can give improvement over a single credit scoring model

Exploratory study to explore the role of ICT in the process of knowledge management in an Indian business environment

In the 21st century and the emergence of a digital economy, knowledge and the knowledge base economy are rapidly growing. To effectively be able to understand the processes involved in the creating, managing and sharing of knowledge management in the business environment is critical to the success of an organization. This study builds on the previous research of the authors on the enablers of knowledge management by identifying the relationship between the enablers of knowledge management and the role played by information communication technologies (ICT) and ICT infrastructure in a business setting. This paper provides the findings of a survey collected from the four major Indian cities (Chennai, Coimbatore, Madurai and Villupuram) regarding their views and opinions about the enablers of knowledge management in business setting. A total of 80 organizations participated in the study with 100 participants in each city. The results show that ICT and ICT infrastructure can play a critical role in the creating, managing and sharing of knowledge in an Indian business environment

An Overview of the Use of Neural Networks for Data Mining Tasks

In the recent years the area of data mining has experienced a considerable demand for technologies that extract knowledge from large and complex data sources. There is a substantial commercial interest as well as research investigations in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from datasets. Artificial Neural Networks (NN) are popular biologically inspired intelligent methodologies, whose classification, prediction and pattern recognition capabilities have been utilised successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks

Understanding the Education Trajectories of Young Black Men in New York City: Elementary and Middle-School Years

Making targeted decisions about how, when, and where to intervene to improve educational outcomes for black males requires understanding the complex pathways that shape these outcomes. This study, undertaken for the Black Male Donor Collaborative, uses longitudinal data on a cohort of black males from New York City Schools to gain insights about the different possible student paths, with specific focuses on middle school and math scale scores

Probability of default models of Russian banks

This paper presents results from an econometric analysis of Russian bank defaults during the period 1997–2003, focusing on the extent to which publicly available information from quarterly bank balance sheets is useful in predicting future defaults. Binary choice models are estimated to construct the probability of default model. We find that preliminary expert clustering or automatic clustering improves the predictive power of the models and incor-poration of macrovariables into the models is useful. Heuristic criteria are suggested to help compare model performance from the perspectives of investors or banks supervision authorities. Russian banking system trends after the crisis 1998 are analyzed with rolling regressions.banks; Russia; probability of default model; early warning systems

Early Warning Indicators of Crisis Incidence: Evidence from a Panel of 40 Developed Countries

We provide a critical review of the literature on early warning indicators of economics crises and propose methods to overcome several pitfalls of the previous contributions. We use a quarterly panel of 40 EU and OECD countries for the period 1970–2010. As the response variable, we construct a continuous index of crisis incidence capturing the real costs for the economy. As the potential warning indicators, we evaluate a wide range of variables, selected according to the previous literature and our own considerations. For each potential indicator we determine the optimal lead employing panel vector autoregression, then we select useful indicators employing Bayesian model averaging. We re-estimate the resulting specification by system GMM to account for potential endogeneity of some indicators. Subsequently, to allow for country heterogeneity, we evaluate the random coefficients estimator and illustrate the stability among endogenous clusters. Our results suggest that global variables rank among the most useful early warning indicators. In addition, housing prices emerge consistently as an important domestic source of risk.Early warning indicators, Bayesian model averaging, panel VAR, dynamic panel, macro-prudential policies.