AI for Anti-Money Laundering: From Rule-Based to Neural Transaction Monitoring in Banking

Олег Ивченко; Iryna Ivchenko

doi:10.5281/zenodo.21904322

August 12, 2026Open Access

AI for Anti-Money Laundering: From Rule-Based to Neural Transaction Monitoring in Banking

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Key Points

To evaluate the transition from traditional rule-based architectures to neural network approaches for anti-money laundering transaction monitoring in the banking sector.
Analyzed banking transaction monitoring frameworks comparing deterministic rule-based algorithms with deep learning models.
Evaluated the capacity of neural architectures to identify complex, evolving patterns of illicit financial behavior across high-volume transaction data.
Neural transaction monitoring models demonstrate enhanced detection capabilities over static rule-based systems by capturing non-linear relationships across financial data.
Artificial intelligence frameworks reduce false positive rates and adapt more effectively to emerging money laundering typologies in financial networks.

Implication

Analysis demonstrates transition from rule-based systems to neural transaction monitoring in banking, highlighting improved detection of illicit financial activity.

Abstract

Research article: AI for Anti-Money Laundering: From Rule-Based to Neural Transaction Monitoring in Banking

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Cite This Study

Ивченко et al. (2026) studied this question.

synapsesocial.com/papers/6a7ec775b70b84ec8b913df1 https://doi.org/10.5281/zenodo.21904322

View Full Paper

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AI for Anti-Money Laundering: From Rule-Based to Neural Transaction Monitoring in Banking

Key Points

Implication

Abstract

Discussion

Cite This Study

AI for Anti-Money Laundering: From Rule-Based to Neural Transaction Monitoring in Banking

Key Points

Implication

Abstract

Discussion

Cite This Study

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