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AI Applications

Anti Money Laundering

Detecting Money Laundering Transactions with Machine Learning

Overview

The recent technological advancements have led to the rise in the adoption of intelligent AI-ML solutions that provide significant benefits, including a reduction in compliance costs and improvements in transaction monitoring. Leverage the power of intelligent ML models to teach systems to detect and recognize suspicious behavior. Identify potential money laundering instances and financial crimes with ML algorithms trained on financial transactions.

Overview
Solution

Solution

Leverage the potential of advanced Machine Learning algorithms and methods to win the war between the financial sector and money laundering. Accurately identify suspicious behavior and activities, and generate alerts highlighting anomalies and enable financial institutions to set up technologically intelligent weapons to predict and flag potential money laundering. Minimize losses by stopping fraud in real-time across banking channels and significantly increase operational efficiency by regulating fraud investigation efforts with ease.

Capabilities

  • Recognize Suspicious Behaviour
  • Identify Behavioural Characteristics
  • Name Screening
Machine Learning models capture suspicious transactions that are masked as normal activity. This can be used to generate alerts requiring manual reviews to resolve.
ML techniques such as anomaly detection can be used to identify previously undetected transactional patterns, data anomalies and relationships among suspicious individuals and entities.
Advanced NLP techniques are highly effective and accurate in name tracking than current text matching algorithms. These capture potential nuances such as the order of names, titles, salutations, abbreviations, name variants, and common misspellings.
Machine Learning models capture suspicious transactions that are masked as normal activity. This can be used to generate alerts requiring manual reviews to resolve.
ML techniques such as anomaly detection can be used to identify previously undetected transactional patterns, data anomalies and relationships among suspicious individuals and entities.
Advanced NLP techniques are highly effective and accurate in name tracking than current text matching algorithms. These capture potential nuances such as the order of names, titles, salutations, abbreviations, name variants, and common misspellings.
  • Recognize Suspicious Behaviour
  • Identify Behavioural Characteristics
  • Name Screening
Impact

Improves Risk Protection

Streamlines Compliance Operations

Processes applications faster and efficiently

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