80 citations · 133 across the 16 of their papers we have counts for
16 papers
Privacy Preserving Diffusion Models for Mixed-Type Tabular Data Generation
Timur Sattarov, Marco Schreyer, Damian Borth
We introduce DP-FinDiff, a differentially private diffusion framework for synthesizing mixed-type tabular data. DP-FinDiff employs embedding-based representations for categorical f…
Diffusion-Scheduled Denoising Autoencoders for Anomaly Detection in Tabular Data
Timur Sattarov, Marco Schreyer, Damian Borth
Anomaly detection in tabular data remains challenging due to complex feature interactions and the scarcity of anomalous examples. Denoising autoencoders rely on fixed-magnitude noi…
Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis
Timur Sattarov, Marco Schreyer, Damian Borth
The increasing demand for privacy-preserving data analytics in various domains necessitates solutions for synthetic data generation that rigorously uphold privacy standards. We int…
GraphGuard: Contrastive Self-Supervised Learning for Credit-Card Fraud Detection in Multi-Relational Dynamic Graphs
Kristófer Reynisson, Marco Schreyer, Damian Borth
Credit card fraud has significant implications at both an individual and societal level, making effective prevention essential. Current methods rely heavily on feature engineering…
FedTabDiff: Federated Learning of Diffusion Probabilistic Models for Synthetic Mixed-Type Tabular Data Generation
Timur Sattarov, Marco Schreyer, Damian Borth
Realistic synthetic tabular data generation encounters significant challenges in preserving privacy, especially when dealing with sensitive information in domains like finance and…
FinDiff: Diffusion Models for Financial Tabular Data Generation
Timur Sattarov, Marco Schreyer, Damian Borth
The sharing of microdata, such as fund holdings and derivative instruments, by regulatory institutions presents a unique challenge due to strict data confidentiality and privacy re…
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…
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…
Federated and Privacy-Preserving Learning of Accounting Data in Financial Statement Audits
Marco Schreyer, Timur Sattarov, Damian Borth
The ongoing 'digital transformation' fundamentally changes audit evidence's nature, recording, and volume. Nowadays, the International Standards on Auditing (ISA) requires auditors…
Continual Learning for Unsupervised Anomaly Detection in Continuous Auditing of Financial Accounting Data
Hamed Hemati, Marco Schreyer, Damian Borth
International audit standards require the direct assessment of a financial statement's underlying accounting journal entries. Driven by advances in artificial intelligence, deep-le…
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…
Leaking Sensitive Financial Accounting Data in Plain Sight using Deep Autoencoder Neural Networks
Marco Schreyer, Chistian Schulze, Damian Borth
Nowadays, organizations collect vast quantities of sensitive information in `Enterprise Resource Planning' (ERP) systems, such as accounting relevant transactions, customer master…
Learning Sampling in Financial Statement Audits using Vector Quantised Autoencoder Neural Networks
Marco Schreyer, Timur Sattarov, Anita Gierbl +2
The audit of financial statements is designed to collect reasonable assurance that an issued statement is free from material misstatement 'true and fair presentation'. Internationa…
Adversarial Learning of Deepfakes in Accounting
Marco Schreyer, Timur Sattarov, Bernd Reimer +1
Nowadays, organizations collect vast quantities of accounting relevant transactions, referred to as 'journal entries', in 'Enterprise Resource Planning' (ERP) systems. The aggregat…
Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks
Marco Schreyer, Timur Sattarov, Christian Schulze +2
The detection of fraud in accounting data is a long-standing challenge in financial statement audits. Nowadays, the majority of applied techniques refer to handcrafted rules derive…
Detection of Anomalies in Large Scale Accounting Data using Deep Autoencoder Networks
Marco Schreyer, Timur Sattarov, Damian Borth +2
Learning to detect fraud in large-scale accounting data is one of the long-standing challenges in financial statement audits or fraud investigations. Nowadays, the majority of appl…