Machine Learning Models Trained on Synthetic Transaction Data: Enhancing Anti-Money Laundering (AML) Efforts in the Financial Services Industry
Keywords:
synthetic transaction data, anti-money laundering (AML)Abstract
Financial crimes, notably money laundering, have gotten more complex, necessitating novel AML procedures in financial services. Traditional AML rule-based algorithms and preset heuristics seldom detect complicated money laundering patterns. Finance data is sensitive and vulnerable to privacy and regulatory constraints, restricting its use for machine learning model building and training. This paper examines if machine learning-generated synthetic transaction data might enhance banks AML. Synthetic data is an innovative way to train machine learning algorithms to find money laundering abnormalities without disclosing personal data.
This paper evaluates traditional AML systems' limitations and the difficulties of obtaining and using transaction data due to privacy, regulatory, and data ownership concerns. Analysis of synthetic data production technologies includes GANs, VAEs, and Differential Privacy. These approaches may anonymize sensitive data and produce high-fidelity synthetic transaction data. Machine learning models built on synthetic datasets are tested for their ability to identify complex money laundering tactics that regular models miss. It also addresses the technological and ethical concerns of producing and exploiting synthetic data in finance, in compliance with GDPR and CCPA.
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