AI-ENHANCED FRAUD DETECTION IN FINANCIAL WORKFLOWS: A HYBRID ML-LLM FRAMEWORK FOR RISK SCORING AND ANOMALY ANALYTICS

Автор(и)

  • Артем Цимбал National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” https://orcid.org0009-0006-8786-8428

DOI:

https://doi.org/10.30890/2567-5273.2025-42-03-004

Ключові слова:

fraud detection, risk scoring, financial workflows, machine learning (ML), large language models (LLM), real-time analytics, stream processing, semantic enrichment, explainable AI (XAI), latency and SLO, compliance, auditability.

Анотація

The rapid expansion of cashless payments and instant transfers has intensified performance and transparency requirements for fraud detection. Decisions to block or approve transactions must be issued within milliseconds, without compromising accuracy, use

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Опубліковано

2025-12-30

Як цитувати

Цимбал, А. (2025). AI-ENHANCED FRAUD DETECTION IN FINANCIAL WORKFLOWS: A HYBRID ML-LLM FRAMEWORK FOR RISK SCORING AND ANOMALY ANALYTICS. Modern Engineering and Innovative Technologies, 3(42-03), 3–17. https://doi.org/10.30890/2567-5273.2025-42-03-004

Номер

№ 42-03 (2025)

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Авторське право (c) 2025 Автори

Creative Commons License

Ця робота ліцензується відповідно до Creative Commons Attribution 4.0 International License.

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