Indranil Bose

Business data mining — a machine learning perspective

The objective of this paper is to inform the information systems (IS) manager and business analyst about the role of machine learning techniques in business data mining. Data mining is a fast growing application area in business. Machine learning techniques are used for data analysis and pattern discovery and thus can play a key role in the development of data mining applications. Understanding the strengths and weaknesses of these techniques in the context of business is useful in selecting an appropriate method for a specific application. The paper, therefore, provides an overview of machine learning techniques and discusses their strengths and weaknesses in the context of mining business data. A survey of data mining applications in business is provided to investigate the use of learning techniques. Rule induction (RI) was found to be most popular, followed by neural networks (NNs) and case-based reasoning (CBR). Most applications were found in financial areas, where prediction of the future was a dominant task category.

Manipulation of online reviews: An analysis of ratings, readability, and sentiments

As consumers become increasingly reliant on online reviews to make purchase decisions, the sales of the product becomes dependent on the word of mouth (WOM) that it generates. As a result, there can be attempts by firms to manipulate online reviews of products to increase their sales. Despite the suspicion on the existence of such manipulation, the amount of such manipulation is unknown, and deciding which reviews to believe in is largely based on the readers discretion and intuition. Therefore, the success of the manipulation of reviews by firms in generating sales of products is unknown. In this paper, we propose a simple statistical method to detect online reviews manipulation, and assess how consumers respond to products with manipulated reviews. In particular, the writing style of reviewers is examined, and the effectiveness of manipulation through ratings, sentiments, and readability is investigated. Our analysis examines textual information available in online reviews by combining sentiment mining techniques with readability assessments. We discover that around 10.3% of the products are subject to online reviews manipulation. In spite of the deliberate use of sentiments and ratings in manipulated products, consumers are only able to detect manipulation taking place through ratings, but not through sentiments. The findings from this research ensue a note of caution for all consumers that rely on online reviews of books for making purchases, and encourage them to delve deep into the book reviews without being deceived by fraudulent manipulation.

Auto-ID: managing anything, anywhere, anytime in the supply chain

Tagging every item in a supply chain promises to help send the right product to the right destination at the right time, reducing the cost of operations and transportation and minimizing distribution lead times.

Detection of financial statement fraud and feature selection using data mining techniques

Recently, high profile cases of financial statement fraud have been dominating the news. This paper uses data mining techniques such as Multilayer Feed Forward Neural Network (MLFF), Support Vector Machines (SVM), Genetic Programming (GP), Group Method of Data Handling (GMDH), Logistic Regression (LR), and Probabilistic Neural Network (PNN) to identify companies that resort to financial statement fraud. Each of these techniques is tested on a dataset involving 202 Chinese companies and compared with and without feature selection. PNN outperformed all the techniques without feature selection, and GP and PNN outperformed others with feature selection and with marginally equal accuracies.

Quantitative models for direct marketing: A review from systems perspective

In this paper, quantitative models for direct marketing models are reviewed from a systems perspective. A systems view consists of input, processing, and output and the six key activities of direct marketing that take place within these constituent parts. A discussion about inputs for direct marketing models is provided by describing the various types of data used, by determining the significance of the data, and by addressing the issue of selection of appropriate data. Two types of models, statistical and machine learning based, are popularly used for conducting direct marketing activities. The advantages and disadvantages of these two approaches are discussed along with enhancements to these models. The evaluation of output for direct marketing models is done on the basis of accuracy and profitability. Some challenges in conducting research in the area of quantitative direct marketing models are listed and some significant research questions are proposed.

ERP and SCM systems integration: The case of a valve manufacturer in China

Many modern organizations integrate enterprise resource planning (ERP) and supply chain management (SCM) systems, as they work in a complementary fashion. This often results in technical and organizational challenges. Neway, a Chinese organization, recently went through this complex process. This required efficient procurement and management of hardware, software, and human resources for successful completion. The integrated system was found to improve operations, foster a paperless environment, and provide efficient inventory tracking and picking. It also had several tangible benefits, including reduced lead time and improved inventory accuracy. ERP and SCM systems integration is still a novel concept for a Chinese manufacturing organization. Our case study details the organizations experience, identifies challenges that were faced, and describes solutions adopted to overcome them.

Do green supply chain management initiatives impact stock prices of firms

Although firms have been taking green supply chain management (GSCM) initiatives, it is not known whether they create value for firms. We analyze 104 announcements related to GSCM using an event study, and determine what causes statistically significant gain in stock prices for these firms. Manufacturing firms, firms with high R&D expenses, and early adopters show a strong increase in stock prices on the day of the announcement. At the same time, small firms, firms not well-known for taking green initiatives, as well as firms that are low in growth potential considerably surprise the market when they make such announcements.

Deciding the financial health of dot-coms using rough sets

We conducted an empirical investigation of dot-coms from a financial perspective. Data from the financial statements of 240 such businesses was used to compute financial ratios and the rough sets technique was used to evaluate whether the financial ratios could predict financial health of them based on available data. The most predictive financial ratios were identified and interesting rules concerning the financial ratios and financial health of dot-coms were discovered. It was shown that rough sets performed a satisfactory job of predicting financial health and were more suitable for detecting unhealthy dot-coms than healthy ones.

Prediction of financial distress: An empirical study of listed Chinese companies using data mining

The deterioration in profitability of listed companies not only threatens the interests of the enterprise and internal staff, but also makes investors face significant financial loss. It is important to establish an effective early warning system for prediction of financial crisis for better corporate governance. This paper studies the phenomenon of financial distress for 107 Chinese companies that received the label ‘special treatment’ from 2001 to 2008 by the Shanghai Stock Exchange and the Shenzhen Stock Exchange. We use data mining techniques to build financial distress warning models based on 31 financial indicators and three different time windows by comparing these 107 firms to a control group of firms. We observe that the performance of neural networks is more accurate than other classifiers, such as decision trees and support vector machines, as well as an ensemble of multiple classifiers combined using majority voting. An important contribution of the paper is to discover that financial indicators, such as net profit margin of total assets, return on total assets, earnings per share, and cash flow per share, play an important role in prediction of deterioration in profitability. This paper provides a suitable method for prediction of financial distress for listed companies in China.

Manipulation in digital word-of-mouth: A reality check for book reviews

Built upon the discretionary accrual-based earnings management framework, our paper develops a discretionary manipulation proxy to study the management of online reviews. We reveal that fraudulent review manipulation is a serious problem for 1) non-bestseller books; 2) books whose reviews are classified as not very helpful; 3) books that experience greater variability in the helpfulness of their online reviews; and 4) popular books as well as high-priced books. We also show that review management decreases with the passage of time. Just like fraudulent earnings management, manipulated online reviews reflect inauthentic information from which consumers might derive wrong valuation especially for books with the above characteristics and be persuaded to purchase the wrong item. The findings from this research sound a note of caution for all consumers that make use of online reviews of books for making purchases and encourage them to delve deeper into the reviews without getting trapped in their fraudulent manipulation.