Indian Institute of Management Bangalore

About: Indian Institute of Management Bangalore is a education organization based out in Bengaluru, Karnataka, India. It is known for research contribution in the topics: Emerging markets & Context (language use). The organization has 491 authors who have published 1254 publications receiving 23853 citations. The organization is also known as: IIMB.

A permanent cordon sanitaire: intra-village spatial segregation and social distance in India

Abstract: ‘Social distance,’ and ‘social distancing’ have become the linguae francae of our world ravaged by COVID-19. Pandemic related social distancing prescriptions, however, do not operate in a vacuum. H...

Spotting Earnings Manipulation: Using Machine Learning for Financial Fraud Detection

Abstract: Earnings manipulation and accounting fraud leads to reduced firm valuation in the long run and a public distrust in the company and its management. Yet, manipulation of accruals to hide liabilities and inflate earnings has been a long-standing fraudulent conduct amongst many listed firms. As auditing is time consuming and restricted to a sample of entries, fraud is either not detected or detected belatedly. We believe that supervised machine learning models can be used to determine high risk firms early enough for auditing by the regulator. We also discuss the anomaly detection unsupervised learning methodology. Since the proportion of manipulators is much lower than the non-manipulators, the biggest challenge in predicting earnings manipulation is the imbalance in the data leading to biased results for conventional statistical models. In this paper, we build ensemble models to detect accrual manipulation by borrowing theory from the seminal work done by Beneish. We also showcase a novel simulation-based sampling technique to efficiently handle imbalanced dataset and illustrate our results on data from listed Indian firms. We compare existing ensemble models establishing the superiority of fairly simple boosting models whilst commenting on the shortfall of area under ROC curve as a performance metric for imbalanced datasets. The paper makes two major contributions: (i) a functional contribution of suggesting an easily deployable strategy to identify high risk companies; (ii) a methodological contribution of suggesting a simulation-based sampling approach that can be applied in other cases of highly imbalanced data for utilizing the entire dataset in modeling.

Periodic Review, Push Inventory Policies for Remanufacturing

Abstract: textSustainability has become a major issue in most economies, causing many leading companies to focus on product recovery and reverse logistics. This research is focused on product recovery, and in particular on production control and inventory management in the remanufacturing context. We study a remanufacturing facility that receives a stream of returned products according to a Poisson process. Demand is uncertain and also follows a Poisson process. The decision problems for the remanufacturing facility are when to release returned products to the remanufacturing line and how many new products to manufacture. We assume that remanufactured products are as good as new. In this paper, we employ a "push" policy that combines these two decisions. It is well known that the optimal policy parameters are difficult to find analytically; therefore, we develop several heuristics based on traditional inventory models. We also investigate the performance of the system as a function of return rates, backorder costs and manufacturing and remanufacturing lead times; and we develop approximate lower and upper bounds on the optimal solution. We illustrate and explain some counter-intuitive results and we test the performance of the heuristics on a set of sample problems. We find that the average error of the heuristics is quite low.