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It is a great honor to be selected as a marketing legend, and 117 of my refereed journal articles are published in nine volumes by Sage India as part of the Legend series. In this chapter, I discuss my preparation for an academic career and the trajectory my research has followed. I reflect on my research contributions to marketing by selectively summarizing the key contributions in each of the nine volumes and draw out some lessons and principles I have learned in the process.

Traditionally, loan officers use different credit scoring models to complement judgmental methods to classify consumer loan applications. This study explores the use of decision trees, AdaBoost, and support vector machines (SVMs) to identify potential bad loans. Our results show that AdaBoost does provide an improvement over simple decision trees as well as SVM models in predicting good credit clients and bad credit clients. To cross-validate our results, we use k-fold classification methodology.