Document Intelligence Centre of Excellence

National Center for Scientific Research Demokritos
Patr. Gregoriou E & Neapoleos Str 27
15341 Agia Paraskevi
Athens, Greece

DICE is a Document Intelligence Centre of Excellence. Its mission is to develop novel AI methods and become a world class reference centre in AI for business documents.

Its vision is to connect researchers, scientists and AI professionals with business experts from a wide range of industrial sectors, aiming at using emerging technologies to accelerate innovation.

Research synopsis: Our principal research interests lie in the development of machine learning and statistical methodologies for solving problems involving un-/semi-/fully-structured financial data.

We are part of the Institute of Informatics and Telecommunications (IIT) at the National Center for Scientific Research - NCSR “Demokritos”.

Check out our publications and our projects outreach to learn more abour our work and feel free to reach out, if you are interested!

News

Sep 01, 2025	DICE has a new member🎉! Nick Reskos nice to have you onboard!
Aug 04, 2025	🎉 DICE at ECML-PKDD! Dimitris Kelesis will be presenting two of his works on GNNs and oversmoothing: Partially trained graph convolutional networks resist oversmoothing and Analyzing the effect of residual connections on oversmoothing in graph neural networks. Catch him in Porto in September!
Jun 20, 2025	DICE will be presenting in the 38th IEEE International Symposium on Computer-Based Medical Systems (CBMS), its latest work on Predicting Multi-Class Drug-Drug Interactions Using a Disease-Specific Knowledge Graph💊 as a full oral! Stay tuned for the full paper!
Apr 24, 2025	DICE placed 1st in the Field of Research Classification shared task, hosted by the Natural Scientific Language Processing and Research Knowledge Graphs (NSLP 2025) workshop @ESWC2025 🏆🎉🎉! Stay tuned for the corresponding paper, utiling LLMs for zero-shot classification!

Selected Publications

ACM
Machine Learning for Identifying Risk in Financial Statements: A Survey

Elias Zavitsanos, Eirini Spyropoulou, George Giannakopoulos, and Georgios Paliouras

ACM Comput. Surv., Mar 2025

Abs DOI Bib

The work herein reviews the scientific literature on Machine Learning approaches for financial risk assessment using financial reports. We identify two prominent use cases that constitute fundamental risk factors for a company, namely misstatement detection and financial distress prediction. We further categorize the related work along four dimensions that can help highlight the peculiarities and challenges of the domain. Specifically, we group the related work based on (a) the input features used by each method, (b) the sources providing the labels of the data, (c) the evaluation approaches used to confirm the validity of the methods, and (d) the machine learning methods themselves. This categorization facilitates a technical overview of risk detection methods, revealing common patterns, methodologies, significant challenges, and opportunities for further research in the field.
@article{10.1145/3723157, title = {Machine Learning for Identifying Risk in Financial Statements: A Survey}, author = {Zavitsanos, Elias and Spyropoulou, Eirini and Giannakopoulos, George and Paliouras, Georgios}, year = {2025}, month = mar, journal = {ACM Comput. Surv.}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, doi = {10.1145/3723157}, issn = {0360-0300}, url = {https://doi.org/10.1145/3723157}, keywords = {risk assessment, misstatement detection, financial distress, bankruptcy prediction, fraud detection, financial reports, financial statements, machine learning, data mining, auditing}, }

ICAIF

Bankruptcy Prediction: Data Augmentation, LLMs and the Need for Auditor’s Opinion

Andreas Sideras, Konstantinos Bougiatiotis, Elias Zavitsanos, Georgios Paliouras, and George Vouros

In Proceedings of the 5th ACM International Conference on AI in Finance, Nov 2024

Bib PDF Code Website

@inproceedings{sideras2024bankruptcy,
  title = {Bankruptcy Prediction: Data Augmentation, LLMs and the Need for Auditor's Opinion},
  author = {Sideras, Andreas and Bougiatiotis, Konstantinos and Zavitsanos, Elias and Paliouras, Georgios and Vouros, George},
  year = {2024},
  booktitle = {Proceedings of the 5th ACM International Conference on AI in Finance},
  pages = {453--460},
  month = nov,
}

Nature

Entrant: A large financial dataset for table understanding

Elias Zavitsanos, Dimitris Mavroeidis, Eirini Spyropoulou, Manos Fergadiotis, and Georgios Paliouras

Scientific Data, Aug 2024

Bib PDF Code

@article{zavitsanos2024entrant,
  title = {Entrant: A large financial dataset for table understanding},
  author = {Zavitsanos, Elias and Mavroeidis, Dimitris and Spyropoulou, Eirini and Fergadiotis, Manos and Paliouras, Georgios},
  year = {2024},
  month = aug,
  journal = {Scientific Data},
  publisher = {Nature Publishing Group UK London},
  volume = {11},
  number = {1},
  pages = {876},
}