190 citations · 464 across the 23 of their papers we have counts for
25 papers
Fair Models in Credit: Intersectional Discrimination and the Amplification of Inequity
Savina Kim, Stefan Lessmann, Galina Andreeva +1
The increasing usage of new data sources and machine learning (ML) technology in credit modeling raises concerns with regards to potentially unfair decision-making that rely on pro…
Multimodal Document Analytics for Banking Process Automation
Christopher Gerling, Stefan Lessmann
Traditional banks face increasing competition from FinTechs in the rapidly evolving financial ecosystem. Raising operational efficiency is vital to address this challenge. Our stud…
The Deep Promotion Time Cure Model
Victor Medina-Olivares, Stefan Lessmann, Nadja Klein
We propose a novel method for predicting time-to-event in the presence of cure fractions based on flexible survivals models integrated into a deep neural network framework. Our app…
Predictive modeling for limited distributed targets
Björn Bokelmann, Stefan Lessmann
Many forecasting applications have a limited distributed target variable, which is zero for most observations and positive for the remaining observations. In the econometrics liter…
A Data-driven Case-based Reasoning in Bankruptcy Prediction
Wei Li, Wolfgang Karl Härdle, Stefan Lessmann
There has been intensive research regarding machine learning models for predicting bankruptcy in recent years. However, the lack of interpretability limits their growth and practic…
Improving uplift model evaluation on RCT data
Björn Bokelmann, Stefan Lessmann
Estimating treatment effects is one of the most challenging and important tasks of data analysts. In many applications, like online marketing and personalized medicine, treatment n…
Forecasting Cryptocurrency Returns from Sentiment Signals: An Analysis of BERT Classifiers and Weak Supervision
Duygu Ider, Stefan Lessmann
Anticipating price developments in financial markets is a topic of continued interest in forecasting. Funneled by advancements in deep learning and natural language processing (NLP…
Leveraging Image-based Generative Adversarial Networks for Time Series Generation
Justin Hellermann, Stefan Lessmann
Generative models for images have gained significant attention in computer vision and natural language processing due to their ability to generate realistic samples from complex da…
Modeling Irregular Time Series with Continuous Recurrent Units
Mona Schirmer, Mazin Eltayeb, Stefan Lessmann +1
Recurrent neural networks (RNNs) are a popular choice for modeling sequential data. Modern RNN architectures assume constant time-intervals between observations. However, in many d…
Personalization in E-Grocery: Top-N versus Top-k Rankings
Franziska Scherpinski, Stefan Lessmann
Business success in e-commerce depends on customer perceived value. A customer with high perceived value buys, returns, and recommends items. The perceived value is at risk wheneve…
Fairness in Credit Scoring: Assessment, Implementation and Profit Implications
Nikita Kozodoi, Johannes Jacob, Stefan Lessmann
The rise of algorithmic decision-making has spawned much research on fair machine learning (ML). Financial institutions use ML for building risk scorecards that support a range of…
Interpretable Multiple Treatment Revenue Uplift Modeling
Robin M. Gubela, Stefan Lessmann
Big data and business analytics are critical drivers of business and societal transformations. Uplift models support a firm's decision-making by predicting the change of a customer…
Data driven value-at-risk forecasting using a SVR-GARCH-KDE hybrid
Marius Lux, Wolfgang Karl Härdle, Stefan Lessmann
Appropriate risk management is crucial to ensure the competitiveness of financial institutions and the stability of the economy. One widely used financial risk measure is Value-at-…
Improving Crime Count Forecasts Using Twitter and Taxi Data
Lara Vomfell, Wolfgang Karl Härdle, Stefan Lessmann
Crime prediction is crucial to criminal justice decision makers and efforts to prevent crime. The paper evaluates the explanatory and predictive value of human activity patterns de…
Conditional Wasserstein GAN-based Oversampling of Tabular Data for Imbalanced Learning
Justin Engelmann, Stefan Lessmann
Class imbalance is a common problem in supervised learning and impedes the predictive performance of classification models. Popular countermeasures include oversampling the minorit…
Targeting customers under response-dependent costs
Johannes Haupt, Stefan Lessmann
This study provides a formal analysis of the customer targeting problem when the cost for a marketing action depends on the customer response and proposes a framework to estimate t…
Response Transformation and Profit Decomposition for Revenue Uplift Modeling
Robin M. Gubela, Stefan Lessmann, Szymon Jaroszewicz
Uplift models support decision-making in marketing campaign planning. Estimating the causal effect of a marketing treatment, an uplift model facilitates targeting communication to…
Affordable Uplift: Supervised Randomization in Controlled Experiments
Johannes Haupt, Daniel Jacob, Robin M. Gubela +1
Customer scoring models are the core of scalable direct marketing. Uplift models provide an estimate of the incremental benefit from a treatment that is used for operational decisi…
Churn Prediction with Sequential Data and Deep Neural Networks. A Comparative Analysis
C. Gary Mena, Arno De Caigny, Kristof Coussement +2
Off-the-shelf machine learning algorithms for prediction such as regularized logistic regression cannot exploit the information of time-varying features without previously using an…
Shallow Self-Learning for Reject Inference in Credit Scoring
Nikita Kozodoi, Panagiotis Katsas, Stefan Lessmann +2
Credit scoring models support loan approval decisions in the financial services industry. Lenders train these models on data from previously granted credit applications, where the…
Evaluating software defect prediction performance: an updated benchmarking study
Libo Li, Stefan Lessmann, Bart Baesens
Accurately predicting faulty software units helps practitioners target faulty units and prioritize their efforts to maintain software quality. Prior studies use machine-learning mo…
Can Deep Learning Predict Risky Retail Investors? A Case Study in Financial Risk Behavior Forecasting
Yaodong Yang, Alisa Kolesnikova, Stefan Lessmann +3
The paper examines the potential of deep learning to support decisions in financial risk management. We develop a deep learning model for predicting whether individual spread trade…
Robust identification of email tracking: A machine learning approach
Johannes Haupt, Benedict Bender, Benjamin Fabian +1
Email tracking allows email senders to collect fine-grained behavior and location data on email recipients, who are uniquely identifiable via their email address. Such tracking inv…
Health Analytics: a systematic review of approaches to detect phenotype cohorts using electronic health records
Norman Hiob, Stefan Lessmann
The paper presents a systematic review of state-of-the-art approaches to identify patient cohorts using electronic health records. It gives a comprehensive overview of the most com…