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Computer Science

D-Index
33
Citations
7111
World Ranking
12436
National Ranking
608

Overview

Stefan Lessmann is affiliated with Humboldt-Universität zu Berlin in Germany. Their research spans multiple fields with a strong focus on computer science and business, management, and accounting. The scientist's work extensively covers artificial intelligence, marketing, economics and econometrics, management science and operations research, and accounting.

Their research topics highlight various specialized areas including financial distress and bankruptcy prediction, stock market forecasting methods, consumer market behavior and pricing, imbalanced data classification techniques, market dynamics and volatility, customer churn and segmentation, and advanced causal inference techniques.

Stefan Lessmann has contributed to a range of academic venues with recurring publications in:

  • arXiv (Cornell University)
  • European Journal of Operational Research
  • SSRN Electronic Journal
  • Expert Systems with Applications
  • RePEc: Research Papers in Economics

Some of the recent papers authored or coauthored by Stefan Lessmann include:

  • Deep learning for detecting financial statement fraud, 2020, Decision Support Systems
  • Predicting online shopping behaviour from clickstream data using deep learning, 2020, Expert Systems with Applications
  • Fairness in credit scoring: Assessment, implementation and profit implications, 2021, European Journal of Operational Research
  • A Hybrid Approach of Machine Learning and Lexicons to Sentiment Analysis: Enhanced Insights from Twitter Data of Natural Disasters, 2021, Information Systems Frontiers
  • Explainable AI for Operational Research: A defining framework, methods, applications, and a research agenda, 2023, European Journal of Operational Research

The scientist frequently collaborates with several coauthors, including:

  • Wolfgang Karl Härdle
  • Daniel Traian Pele
  • Björn Bokelmann
  • Nikita Kozodoi
  • Wouter Verbeke

Stefan Lessmann's research integrates elements of machine learning, data analysis, and operational research to address challenges in financial and consumer markets. Their work encompasses studies on fraud detection, online consumer behavior, fairness in credit scoring, sentiment analysis in social media, and explainable AI applications in operational research.

Best Publications

  • Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings

    S. Lessmann;B. Baesens;C. Mues;S. Pietsch

  • Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research

    Stefan Lessmann;Bart Baesens;Bart Baesens;Hsin-Vonn Seow;Lyn C. Thomas

  • A comparative study of LSTM neural networks in forecasting day-ahead global horizontal irradiance with satellite data

    Shikhar Srivastava;Stefan Lessmann

  • The impact of preprocessing on data mining: An evaluation of classifier sensitivity in direct marketing

    Sven F. Crone;Stefan Lessmann;Robert Stahlbock

  • Deep learning for detecting financial statement fraud

    Patricia Craja;Alisa Kim;Stefan Lessmann

  • Conditional Wasserstein GAN-based oversampling of tabular data for imbalanced learning

    Justin Engelmann;Stefan Lessmann

  • A comparative analysis of data preparation algorithms for customer churn prediction

    Kristof Coussement;Stefan Lessmann;Geert Verstraeten

  • Genetic Algorithms for Support Vector Machine Model Selection

    S. Lessmann;R. Stahlbock;S.F. Crone

  • Extreme learning machines for credit scoring: An empirical evaluation

    Artem Bequé;Stefan Lessmann

  • Predicting online shopping behaviour from clickstream data using deep learning

    Dennis Koehn;Stefan Lessmann;Markus Schaal

  • Incorporating textual information in customer churn prediction models based on a convolutional neural network

    Arno De Caigny;Arno De Caigny;Kristof Coussement;Kristof Coussement;Koen W. De Bock;Stefan Lessmann

  • Fairness in credit scoring: Assessment, implementation and profit implications

    Nikita Kozodoi;Johannes Jacob;Stefan Lessmann

  • A multi-objective approach for profit-driven feature selection in credit scoring

    Nikita Kozodoi;Stefan Lessmann;Konstantinos Papakonstantinou;Yiannis Gatsoulis

  • A reference model for customer-centric data mining with support vector machines

    Stefan Lessmann;Stefan Voß

  • Can deep learning predict risky retail investors? A case study in financial risk behavior forecasting

    Alisa Kim;Y. Yang;Stefan Lessmann;Tiejun Ma

  • A Hybrid Approach of Machine Learning and Lexicons to Sentiment Analysis: Enhanced Insights from Twitter Data of Natural Disasters

    Shalak Mendon;Shalak Mendon;Pankaj Dutta;Abhishek Behl;Stefan Lessmann

  • Forex exchange rate forecasting using deep recurrent neural networks

    Alexander Jakob Dautel;Wolfgang Karl Härdle;Stefan Lessmann;Hsin-Vonn Seow

  • A conceptual framework for the adoption of big data analytics by e-commerce startups: a case-based approach

    Abhishek Behl;Pankaj Dutta;Stefan Lessmann;Yogesh K. Dwivedi

  • Targeting customers for profit: An ensemble learning framework to support marketing decision-making

    Stefan Lessmann;Johannes Haupt;Kristof Coussement;Koen W. De Bock

  • Car resale price forecasting: The impact of regression method, private information, and heterogeneity on forecast accuracy

    Stefan Lessmann;Stefan Voß

  • Can Deep Learning Predict Risky Retail Investors? A Case Study in Financial Risk Behavior Forecasting

    Yaodong Yang;Alisa Kolesnikova;Stefan Lessmann;Tiejun Ma

Frequent Co-Authors

Stefan Voß
Stefan Voß Universität Hamburg
Bart Baesens
Bart Baesens KU Leuven
Lyn C. Thomas
Lyn C. Thomas University of Southampton
Marco C. Campi
Marco C. Campi University of Brescia
Gary M. Weiss
Gary M. Weiss Fordham University
Samarjit Kar
Samarjit Kar National Institute of Technology Durgapur

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