40 citations · 148 across the 18 of their papers we have counts for
23 papers
Federated Continual Learning to Detect Accounting Anomalies in Financial Auditing
Marco Schreyer, Hamed Hemati, Damian Borth +1
The International Standards on Auditing require auditors to collect reasonable assurance that financial statements are free of material misstatement. At the same time, a central ob…
Model Zoos: A Dataset of Diverse Populations of Neural Network Models
Konstantin Schürholt, Diyar Taskiran, Boris Knyazev +2
In the last years, neural networks (NN) have evolved from laboratory environments to the state-of-the-art for many real-world problems. It was shown that NN models (i.e., their wei…
Hyper-Representations as Generative Models: Sampling Unseen Neural Network Weights
Konstantin Schürholt, Boris Knyazev, Xavier Giró-i-Nieto +1
Learning representations of neural network weights given a model zoo is an emerging and challenging area with many potential applications from model inspection, to neural architect…
RESHAPE: Explaining Accounting Anomalies in Financial Statement Audits by enhancing SHapley Additive exPlanations
Ricardo Müller, Marco Schreyer, Timur Sattarov +1
Detecting accounting anomalies is a recurrent challenge in financial statement audits. Recently, novel methods derived from Deep-Learning (DL) have been proposed to audit the large…
Multi-view Contrastive Self-Supervised Learning of Accounting Data Representations for Downstream Audit Tasks
Marco Schreyer, Timur Sattarov, Damian Borth
International audit standards require the direct assessment of a financial statement's underlying accounting transactions, referred to as journal entries. Recently, driven by the a…
Heterogeneous Ensemble for ESG Ratings Prediction
Tim Krappel, Alex Bogun, Damian Borth
Over the past years, topics ranging from climate change to human rights have seen increasing importance for investment decisions. Hence, investors (asset managers and asset owners)…