Steinbeis-Transferzentrum Data Analytics und Predictive Modelling

Publications

Technical articles, blog posts and press articles on AI, machine learning and predictive analytics — in specialist journals, blogs and magazines.

heyprof – An AI-Powered Platform for Teaching
Whitepaper 2026

heyprof – An AI-Powered Platform for Teaching

NXT Sustainability and Technology, Reutlingen University · March 2026, Version 1.0

heyprof is a web-based teaching platform with a context-sensitive AI tutor. At its core is a Socratic dialogue approach that guides students step by step — without providing ready-made solutions.

Prof. Dr. Dirk Schieborn Prof. Dr. Volker Reichenberger
Schieborn and Reichenberger
Partial Use of the IRBA
Journal Article 2023

Partial Use of the IRBA

Zeitschrift für das gesamte Kreditwesen, Issue 19-2023

Analysis of the regulatory and economic implications of partial use of the Internal Ratings-Based Approach (IRBA) for capital calculation at banks.

Prof. Dr. Dirk Schieborn
Dirk Schieborn
Machine Learning for IRBA Rating Models
Blog Post 2023

Machine Learning for IRBA Rating Models

banking.vision Blog, August 2023

What role can machine learning play in the development and calibration of IRBA rating models? A practice-oriented overview of current ML methods and their suitability in regulated banking processes.

Prof. Dr. Dirk Schieborn
Dirk Schieborn
Capital Relief Through the IRBA
Journal Article 2023

Capital Relief Through the IRBA

die bank, Issue 06/2023

Examination of opportunities for capital relief through strategic use of the Internal Ratings-Based Approach — with concrete recommendations for banks.

Prof. Dr. Dirk Schieborn
Dirk Schieborn
AI Web Application for Handwriting Recognition
Press Article 2023

AI Web Application for Handwriting Recognition

c't Fotografie, Issue 06/2023

Tilo Gockel reports in c't Fotografie on the web application for handwriting recognition based on neural networks — developed at the Steinbeis Transfer Centre for Data Analytics and Predictive Modelling.

Prof. Dr. Dirk Schieborn
Dirk Schieborn
Interpretability of Machine Learning Models
Journal Article 2021

Interpretability of Machine Learning Models

Zeitschrift für das gesamte Kreditwesen, Issue 20-2021

Structured overview of current methods for the explainability of ML models in credit risk management and assessment of their practical use in banking practice.

Prof. Dr. Dirk Schieborn Prof. Dr. Volker Reichenberger
Schieborn and Reichenberger
ML Benchmarking for Rating Models
Journal Article 2018

ML Benchmarking for Rating Models

Zeitschrift für das gesamte Kreditwesen, Issue 24/2018

Systematic benchmark study: machine learning methods compared to classical statistical methods for the development of rating models in the banking sector.

Prof. Dr. Dirk Schieborn Prof. Dr. Volker Reichenberger
Schieborn and Reichenberger

Interested in a collaboration or a technical contribution? We look forward to your enquiry.

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