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The purpose of this paper is to create a model called “Balanced score for the balanced score card” and to provide an objective benchmarking indicator for evaluating the achievement of the strategic goals of the company.

The paper uses the concepts of “Balanced scorecard” proposed by Robert. S. Kaplan and David P. Norton. This paper also adopts the model given by Brown P.A. and Gibson D.F. and the extension to the model provided by P.V. Raghavan and M. Punniyamoorthy. Preference theory is used to calculate the relative weightage for each factor, using the process of pair wise comparison. The balanced score for balanced scorecard provides a single value by taking into account all the essential objective and subjective factors – be it financial or non‐financial. It also provides a suitable weightages for those parameters. The target performance and the actual performance are compared and the analysis is made.

Information from a leading organization was obtained and the balanced score for a balance scorecard was calculated for that organization. The variations were analyzed through this model. The depth and objectivity in the analysis is highlighted.

This provides a single bench marking measure to evaluate how far the firm had been successful in achieving the strategies. The paper has adopted the preference theory which limits the weightage to be accorded to the factors concerned. However, further refinement can be provided by the usage of analytic hierarchy process for arriving suitable weightages.

The organization can calculate the balanced score by themselves, by assigning appropriate importance to the activities – as they deem fit. It is a tailor made benchmarking information system created by the firm for itself.

This is of value to the top management to identify the important activities and setting suitable target measures to be achieved in those activities. The variations are arrived by comparing the targeted performance with the actual. This will help the firm to take suitable actions under those parameters where there are significant deviations.

The purpose of this paper is to develop an integrated closed loop performance management model for service industries.

The service performance of any organization is measured by considering qualitative and quantitative dimensions. The qualitative dimension includes the service quality factors. In order to measure the service quality precisely, fuzzy analytical hierarchy process (FAHP) has been employed in this paper. The data pertaining to both qualitative and quantitative dimensions are combined using extended Brown‐Gibson (EBG) model to measure the service performance. As an improvement process, fuzzy quality function deployment (FQFD) has been employed to redesign the existing services. A case study from automobile repair shops illustrates the usability of the model.

The developed model quantifies service performance and ensures the improvement of the service process. The proposed model takes into account the uncertainty that occurs while capturing the subjective assessment from customers and service engineers. The case study shows that the model can be used to gain service process understanding and identify significant factors for redesigning. Detailed results are presented.

The paper describes a novel method for service performance management. Fuzzy assessment of customers' feedback and service managers' feedback is much closer to human thinking than methods based on crisp numbers.

The purpose of this paper is to provide a framework for designing a multiple performance measurement tool for evaluating, comparing and benchmarking the working of engineering educational institutes in a group based on total quality management (TQM) criteria and performance measurement criterion, respectively.

Proposed framework is based on fuzzy analytic hierarchy process (FAHP) which takes in to account the fuzziness of human opinion for realistic outcome and generalization of the results. Based on the proposed framework a case study was conducted on engineering institutes of central India for collecting data and analyzing the current practices followed in these institutes. A relationship among TQM implementation criterion was developed, their respective weights derived and then institutes were ranked.

It was found that the rank of institutions based on both the criterion is same, which indicates that the institutes having better TQM implementation have better performance.

The research in this paper is limited to Indian scenario; studies in other countries and sectors may be conducted to compare the results obtained.

The results will help policy makers in identifying institutions having poor performance in the region.

The paper is navel in its attempt to provide a model based on TQM criteria for evaluating the working of engineering educational institutes in a group in terms of their relative weightage and benchmark.

This paper attempts to develop a hybrid model using advanced data mining techniques for the detection of Stock Price Manipulation. The hybrid model detailed in this article elucidates the application of a Genetic Algorithm based Artificial Neural Network to classify stocks witnessing activities that are suggestive of potential manipulation.

Price, volume and volatility are used as the variables for this model to capture the characteristics of stocks. An empirical analysis of this model is carried out to evaluate its ability to predict stock price manipulation in one of the largest emerging markets – India, which has a large number of securities and significant trading volumes. Further, the article compares the performance of this hybrid model with a conventional standalone model based on Quadratic Discreminant Function (QDF).

Based on the results obtained, the superiority of the hybrid model over the conventional model in its ability to predict manipulation in stock prices has been established.

The classification by the proposed model is agnostic of the type of manipulation – action‐based, information‐based or trade‐based.

The market regulators can use these techniques to ensure that sufficient deterrents are in place to identify a manipulator in their market. This helps them carry out their primary function, namely, investor protection. These models will help effective monitoring for abnormal market activities and detect market manipulation.

Implementing this model at a regulator or SRO helps in strengthening the integrity and safety of the market. This strengthens investor confidence and hence participation, as the investors are made aware that the regulators implementing market manipulation detection techniques ensure that the markets they monitor are secure and protects investor interest.

This is the first time a hybrid model has been used to detect market manipulation.

The purpose of this study is to propose a new consumer value segmentation method for low-dimensional dense market datasets to quickly detect and cluster the most profitable customers for the enterprises.

In this study, the comprehensive segmentation bases (CSB) with richer meanings were obtained by introducing the weighted recency-frequency-monetary (RFM) model into the common segmentation bases (SB). Further, a new market segmentation method, the CSB-MBK algorithm was proposed by integrating the CSB model and the mini-batch k-means (MBK) clustering algorithm.

The results show that our proposed CSB model can reflect consumers' contributions to a market, as well as improve the clustering performance. Moreover, the proposed CSB-MBK algorithm is demonstrably superior to the SB-MBK, CSB-KMA and CSB-Chameleon algorithms with respect to the Silhouette Coefficient (SC), the Calinski-Harabasz (CH) Index , the average running time and superior to the SB-MBK, RFM-MBK and WRFM-MBK algorithms in terms of the inter-market value and characteristic differentiation.

This paper provides a tool for decision-makers and marketers to segment a market quickly, which can help them grasp consumers' activity, loyalty, purchasing power and other characteristics in a target market timely and achieve the precision marketing.

This study is the first to introduce the CSB-MBK algorithm for identifying valuable customers through the comprehensive consideration of the clustering quality, consumer value and segmentation speed. Moreover, the CSB-MBK algorithm can be considered for applications in other markets.

The purpose of this study is to compare the classification learning ability of our algorithm based on boosted support vector machine (B-SVM), against other classification techniques in predicting the credit ratings of banks. The key feature of this study is the usage of an imbalanced dataset (in the response variable/rating) with a smaller number of observations (number of banks).

In general, datasets in banking sector are small and imbalanced too. In this study, 23 Scheduled Commercial Banks (SCBs) have been chosen (in India), and their corresponding corporate ratings have been collated from the Indian subsidiary of reputed global rating agency. The top management of the rating agency provided 12 input (quantitative) variables that are considered essential for rating a bank within India. In order to overcome the challenge of dataset being imbalanced and having small number of observations, this study uses an algorithm, namely “Modified Boosted Support Vector Machines” (MBSVMs) proposed by Punniyamoorthy Murugesan and Sundar Rengasamy. This study also compares the classification ability of the aforementioned algorithm against other classification techniques such as multi-class SVM, back propagation neural networks, multi-class linear discriminant analysis (LDA) and k-nearest neighbors (k-NN) classification, on the basis of geometric mean (GM).

The performances of each algorithm have been compared based on one metric—the geometric mean, also known as GMean (GM). This metric typically indicates the class-wise sensitivity by using the values of products. The findings of the study prove that the proposed MBSVM technique outperforms the other techniques.

This study provides an algorithm to predict ratings of banks where the dataset is small and imbalanced. One of the limitations of this research study is that subjective factors have not been included in our model; the sole focus is on the results generated by the models (driven by quantitative parameters). In future, studies may be conducted which may include subjective parameters (proxied by relevant and quantifiable variables).

Various stakeholders such as investors, regulators and central banks can predict the credit ratings of banks by themselves, by inputting appropriate data to the model.

In the process of rating banks, the usage of an imbalanced dataset can lessen the performance of the soft-computing techniques. In order to overcome this, the authors have come up with a novel classification approach based on “MBSVMs”, which can be used as a yardstick for such imbalanced datasets. For this purpose, through primary research, 12 features have been identified that are considered essential by the credit rating agencies.