Rashmi Malhotra

Evaluating consumer loans using neural networks

A number of credit-scoring models that accurately classify consumer loan applications have been developed to aid traditional judgmental methods. This study compares the performance of multiple discriminant analysis and neural networks in identifying potential loan. While there is not a significant improvement in the performance of neural network over discriminant analysis model in identifying good credit loans, the neural network models consistently perform better than the multiple discriminant analysis models in identifying potential problem loans. To alleviate the problem of bias in the training set and to examine the robustness of neural network classifiers in identifying problem loans, we cross-validate our results through seven different samples of the data.

Differentiating between good credits and bad credits using neuro-fuzzy systems

Abstract To evaluate consumer loan applications, loan officers use many techniques such as judgmental systems, statistical models, or simply intuitive experience. In recent years, fuzzy systems and neural networks have attracted the growing interest of researchers and practitioners. This study compares the performance of artificial neuro-fuzzy inference systems (ANFIS) and multiple discriminant analysis models to screen potential defaulters on consumer loans. Using a modeling sample and a test sample, we find that the neuro-fuzzy system performs better than the multiple discriminant analysis approach to identify bad credit applications. Further, neuro-fuzzy systems have many advantages over traditional computational methods. Neuro-fuzzy system models are flexible, more tolerant of imprecise data, and can model non-linear functions of arbitrary complexity.

Analysing financial services industry using data envelopment analysis

The ongoing credit crisis in the financial markets has led to tremendous turmoil in the financial services industry. As a result, during the last one year, we have seen a substantial decline in the profitability and liquidity of the financial services companies. In this paper, we analyse the financial performance of thirteen leading financial services firms to evaluate their relative standing in the industry. We illustrate the use of data envelopment analysis (DEA), an operations research technique, to evaluate the relative financial strength of thirteen financial services firms by benchmarking the financial ratios of a firm against its peers. DEA clearly brings out the firms that are operating more efficiently in comparison to other firms in the industry, and points out the areas in which poorly performing firms need to improve.

Business Plus Intelligence Plus Technology Equals Business Intelligence

In this article the authors will show how the parallel developments of information technology at the operational business level and decision support concepts progressed through the decades of the twentieth century with only minimal success at strategic application. They will posit that the twin technological developments of the world-wide-web and very inexpensive mass storage provided the environment to facilitate the convergence of business operations and decision support into the strategic application of business intelligence.

The impact of internet and e-commerce on the evolving business models in the financial services industry

The financial services industry has changed profoundly because of a new delivery channel (the internet), the explosion of e-commerce and the emergence of a knowledge-intensive economy. This paper explores the major technology and change drivers of the finance industry. It also suggests ways to develop an overall internet and e-commerce strategy for the financial services industry.

Meta-modeling framework: A new approach to manage meta-modelbase and modeling knowledge

In this era of digital revolution models are considered an important organizational resource. This demands a new approach in managing the repository of models for decision makers. This study presents a framework for meta-modeling of knowledge. The study proposes a generalized method for meta-modelbase management to use, store, and manage modeling knowledge. Further, to illustrate the application of the meta-modeling framework, we present the design and development of MetaSimModel - a knowledge-based decision support environment to organize, manage, use, and reuse modeling knowledge of simulation models in a flexible manufacturing environment. This study illustrates the use of meta-models (models that store knowledge about other models) to represent models. The ability to construct larger, composite models from reusable, pre-fabricated components plays an important role in improving productivity in a modeling environment.

USING DATA ENVELOPMENT ANALYSIS TO ANALYZE THE PERFORMANCE OF NORTH AMERICAN CLASS I FREIGHT RAILROADS

With increased crude oil prices, railroad is emerging as a cheaper alternative to trucks and other less fuel efficient modes of transportation. As a result, with increase in crude oil price, while other modes of transportation have suffered economic slump, railroad industry is thriving with every company reporting an increase in revenue and profits. In this study, we analyze the performance of seven North American Class I freight railroads. In this chapter, we illustrate the use of data envelopment analysis (DEA), an operations research technique, to analyze the financial performance of the U.S. railroad industry by benchmarking a set of financial ratios of a firm against its peers. DEA clearly brings out the firms that are operating more efficiently in comparison with other firms in the industry and points out the areas in which poorly performing firms need to improve.

SIDE: A Decision Support System Using a Combination of Swarm Intelligence and Data Envelopment Analysis

To make sound decisions, managers analyze data from multiple sources using different dimensions and eventually integrate the results of their analysis. This study proposes the design of a multi-attribute-decision-support-system that combines the analytical power of two different tools: data envelopment analysis (DEA) and particle swarm optimization (PSO), one of the major algorithms using swarm intelligence. DEA measures the relative efficiency of decision making units that use multiple inputs and outputs to provide non-objective measures without making any specific assumptions about data. On the other hand PSOs main strength lies in exploring the entire search space. This study proposes a modeling technique that jointly uses the two techniques to benefit from the two methodologies.

Benchmarking large U.S. retailers using a data envelopment analysis model

In this chapter, we illustrate the use of data envelopment analysis, an operations research technique, to analyze the financial performance of the seven largest retailers in the United States by benchmarking a set of financial ratios of a firm against its peers. Data envelopment analysis clearly brings out the firms that are operating more efficiently in comparison to other firms in the industry, and points out the areas in which poorly performing firms need to improve.

A DEA-based multidimensional framework for analysing emerging markets

This study develops a multidimensional framework, using data envelopment analysis as a benchmarking tool, to assess the performance of the emerging markets. Using data envelopment analysis approach, this study compares the relative performance of 21 emerging nations against one another with eight economic variables as the benchmark parameters. This study finds that there is lack of convergence in the performance of 21 nations and some nations have performed more efficiently in contrast to other nations. The study also shows the areas in which inefficient nations are lagging behind and how they can improve their performance to bring them at par with other emerging markets.