Towards AI Analyst: Querying Costly Features for Fraud and Money Laundering Detection
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00388386" target="_blank" >RIV/68407700:21230/25:00388386 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/COMPSAC65507.2025.00262" target="_blank" >https://doi.org/10.1109/COMPSAC65507.2025.00262</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/COMPSAC65507.2025.00262" target="_blank" >10.1109/COMPSAC65507.2025.00262</a>
Alternative languages
Result language
angličtina
Original language name
Towards AI Analyst: Querying Costly Features for Fraud and Money Laundering Detection
Original language description
Fraud and money laundering detection in financial transaction data is a highly regulated field where explanations and human decisions are paramount to deploying any automatic detection models. Artificial intelligence (AI) methods are already routinely used for the detection of suspicious transactions, but their application in the final stage of analysis is underexplored. To assess the prospects for AI in this task, we analyze the work of a human analyst as a data processing agent. Specifically, we analyze textual feedback from real human analysts to determine the focus of their work and identify how AI can improve it. We conclude that analysts do not spend their time reanalyzing or explaining the raw transaction data but actively seek additional information. This paradigm is formalized in machine learning as learning with costly features. We applied state-of-the-art methods to studied data and concluded that using the costly features paradigm leads to an optimal trade-off between the cost of the feature acquisition and model performance. We believe that this approach is ready to be applied in the production stage. We provide a full code of our approach as well as a modification of publicly available data to reproduce our results.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA22-32620S" target="_blank" >GA22-32620S: Unsupervised learning from heterogenous structured data</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2025 IEEE 49th Annual Computers, Software, and Applications Conference (COMPSAC)
ISBN
979-8-3315-7435-2
ISSN
2836-3787
e-ISSN
2836-3795
Number of pages
6
Pages from-to
1905-1910
Publisher name
IEEE Computer Society
Place of publication
Los Alamitos
Event location
Toronto
Event date
Jul 8, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001575960000253