Machine Learning for Shadow Economy Detection — Classification of Suspicious Transaction Patterns

Key Points

• The aim is to explore the effectiveness of machine learning in identifying and classifying suspicious transaction patterns associated with the shadow economy.
• Applied machine learning algorithms to analyze transaction data.
• Classified transaction patterns based on identified suspicious behaviors.
• Utilized dataset reflective of typical financial activities.
• Successfully classified transaction patterns indicative of shadow economy activities.
• Achieved high accuracy rates in identifying suspicious transactions.
• Demonstrated potential for real-time detection of illicit financial activities.