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Employee Profile

Rogelio Andrade Mancisidor

Assistant Professor - Department of Data Science and Analytics

Publications

Andrade Mancisidor, Rogelio; Kampffmeyer, Michael, Aas, Kjersti & Jenssen, Robert (2022)

Generating customer's credit behavior with deep generative models

Knowledge-Based Systems, 245, s. 1- 13. Doi: 10.1016/j.knosys.2022.108568 - Full text in research archive

Innovation is considered essential for today's organizations to survive and thrive. Researchers have also stressed the importance of leadership as a driver of followers' innovative work behavior (FIB). Yet, despite a large amount of research, three areas remain understudied: (a) The relative importance of different forms of leadership for FIB; (b) the mechanisms through which leadership impacts FIB; and (c) the degree to which relationships between leadership and FIB are generalizable across cultures. To address these lacunae, we propose an integrated model connecting four types of positive leadership behaviors, two types of identification (as mediating variables), and FIB. We tested our model in a global data set comprising responses of N = 7,225 participants from 23 countries, grouped into nine cultural clusters. Our results indicate that perceived LMX quality was the strongest relative predictor of FIB. Furthermore, the relationships between both perceived LMX quality and identity leadership with FIB were mediated by social identification. The indirect effect of LMX on FIB via social identification was stable across clusters, whereas the indirect effects of the other forms of leadership on FIB via social identification were stronger in countries high versus low on collectivism. Power distance did not influence the relations.

Andrade Mancisidor, Rogelio; Kampffmeyer, Michael, Aas, Kjersti & Jenssen, Robert (2021)

Learning latent representations of bank customers with the Variational Autoencoder

Expert Systems With Applications, 164 Doi: 10.1016/j.eswa.2020.114020 - Full text in research archive

Learning data representations that reflect the customers’ creditworthiness can improve marketing campaigns, customer relationship management, data and process management or the credit risk assessment in retail banks. In this research, we show that it is possible to steer data representations in the latent space of the Variational Autoencoder (VAE) using a semi-supervised learning framework and a specific grouping of the input data called Weight of Evidence (WoE). Our proposed method learns a latent representation of the data showing a well-defied clustering structure. The clustering structure captures the customers’ creditworthiness, which is unknown a priori and cannot be identified in the input space. The main advantages of our proposed method are that it captures the natural clustering of the data, suggests the number of clusters, captures the spatial coherence of customers’ creditworthiness, generates data representations of unseen customers and assign them to one of the existing clusters. Our empirical results, based on real data sets reflecting different market and economic conditions, show that none of the well-known data representation models in the benchmark analysis are able to obtain well-defined clustering structures like our proposed method. Further, we show how banks can use our proposed methodology to improve marketing campaigns and credit risk assessment.

Andrade Mancisidor, Rogelio; Kampffmeyer, Michael, Aas, Kjersti & Jenssen, Robert (2020)

Deep generative models for reject inference in credit scoring

Knowledge-Based Systems, 196 Doi: 10.1016/j.knosys.2020.105758 - Full text in research archive

Credit scoring models based on accepted applications may be biased and their consequences can have a statistical and economic impact. Reject inference is the process of attempting to infer the creditworthiness status of the rejected applications. Inspired by the promising results of semi-supervised deep generative models, this research develops two novel Bayesian models for reject inference in credit scoring combining Gaussian mixtures and auxiliary variables in a semi-supervised framework with generative models. To the best of our knowledge this is the first study coupling these concepts together. The goal is to improve the classification accuracy in credit scoring models by adding reject applications. Further, our proposed models infer the unknown creditworthiness of the rejected applications by exact enumeration of the two possible outcomes of the loan (default or non-default). The efficient stochastic gradient optimization technique used in deep generative models makes our models suitable for large data sets. Finally, the experiments in this research show that our proposed models perform better than classical and alternative machine learning models for reject inference in credit scoring, and that model performance increases with the amount of data used for model training.

Academic Degrees
Year Academic Department Degree
2021 University of Tromsø Ph.D.