How AI helps financial institutions perform customer due diligence.

By | September 1, 2017

David McLaughlin, CEO, and founder of QuantaVerse – a provider of Data Science and AI for identifying financial crimes writing recently in the Upside – a new blog from TDWI dedicated to providing information on extracting actionable information from data tells us how AI is helping the financial institutions do due diligence on customers.

He starts off by informing us how financial institutions are forced to rely on expensive and cumbersome KYC (Know Your Customer) initiatives to conduct verification of account opening details and to get additional customer information and documentation. And even when efficiently run this processes fail to uncover the underlying motive of the account owners.

With Financial Crime Enforcement  Network (FinCEN) having come out with new Anti –Money Laundering provisions which in its new Fifth pillar requirement mandates account specific transactional review and analysis from May 11th, 2018, Until now the financial institutions had a client –centric approach to their customer relations and avoided review of individual accounts. But with the Fifth pillar provision coming into effect, Financial Institutions in order to improve their risk management proficiency  are now required to ascertain the accounting objective, account history along with volumes and value of transactions particular to each account.

And going further, financial institutions to mitigate risk also need to monitor, investigate suspicious client activity which involves constant maintenance and up gradation of client information.

These fifth pillar requirements may be addressed effectively by the adoption of new technologies like Artificial Intelligence (AI), machine learning and Big Data. Financial institutions can reduce money laundering risks by deploying solutions to analyze massive amounts of both structured and unstructured data to extract precise information to analyze and review a specific transaction.

Financial Institutions deploying appropriate Data science and AI technologies may address new requirements like Link analysis, Transactional analysis and it may also seek to undertake help from Outside Investigative Sources and Unsupervised Machine Learning Techniques to help meet the growing regulatory demands of state, federal and international regulators.

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