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Artificial Intelligence and Machine Learning in Anti Money Laundering and Know Your Customer

Easy, New-age Solution to Financial Crimes

Financial institutions, whether banking or non-banking, work according to the rules and regulations of regulatory authorities to capture customer data, transactions, and other activities.

The traditional method in the early days was enough to detect and report fraudulent activities by customers and clients. However, the financial criminals found ways to bypass the conventional methods. There were several cases of non-detection, no reporting, false positives, and false negatives of suspicious transactions.

That’s when Artificial Intelligence and Machine Learning was needed in Anti Money Laundering and Know Your Customer. It is one of the assured ways to detect fraudulent transactions and financial crimes across businesses.

The traditional method of Money Laundering Monitoring and Other Financial Crimes

  • Anomalies in behavior
  • Hidden relationships
  • High-risk entities
  • Transaction patterns
  • Credit, debit, and bank cards

Demerits of the traditional AML transaction monitoring

  • The increased cost of monitoring and operational cost
  • Fail to monitor, detect, and report new fraudulent scenarios

Introduction of New Fraud Risk Model: Leveraging AI & ML in AML and KYC

Artificial Intelligence and Machine Learning make consistent progression via revelation to new fraudulent scenarios. Adaptive testing, pattern and trend detection in the transaction, and making forthcoming decisions through various situations this is what AI and ML were designed to do in AML and KYC implementation.

The AI and ML approach includes tools and methods that help the users get actionable insights for further investigations and improvements in secured transactions to shut down the possibilities of fraudulent transactions completely.

AI and ML technologies also learn to detect new fraud patterns based on data gathered from previous transactions.

Advantages of AI and ML in AML and KYC

Over the years, banking and non-banking institutions have seen a significant positive change in eradicating money laundering, loopholes in the Know Your Customer process, smooth implementation of policies that protect from money laundering.

New money laundering pattern analysis, easy navigation, pre, and post-inspection recommendations, detailed case analysis, and finding future patterns of fraud are some of the advantages of AI and ML the users have experienced.

  • Reduce false negatives
  • Reduce false positives 
  • Priority Alerts 
  • Increase effectiveness and efficiency
  • Continuous learning of new fraud patterns
  • Impeccable Due Diligence
  • Risk Assessment
  • Compliance with Regulatory Authorities
  • Maintenance and Updating Risk Profiles
  • Error-free transaction monitoring and Reporting
  • Seamless integration with the current monitoring system

Users can better understand customer risk profiles through Machine Learning as it is a continuous learning process, so you never lag behind. Continuous updation in risk assessment files keeps the company safe from new emerging risks in the market.

Get in touch with Diagonal Matrix experts to understand more about how you can leverage AI and ML and help your AML and KYC processes prioritize the workflow queues, incorporate new fraud patterns into the current monitoring system, and stay aligned with the regulatory compliance.

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