Machine learning in sanctions screening has revolutionized the way financial institutions detect and prevent illicit transactions, reducing false positives and enhancing the accuracy of compliance checks. The use of Machine Learning (ML) techniques and capabilities helps improve the process of performing sanctions screening which ensures regulatory compliance.
ML tools have the capability to extract data in real time from different available data sources, data sets, data fields, files, linked websites, and portals. Therefore, the use of ML helps institutions improve the regulatory compliance process, especially anti-money laundering, and countering the financing of terrorism (AML/CFT).
The use of ML in compliance processes enables timely revisiting and updating of compliance programs and policies. ML models help detect changes in client or customer behavior by analyzing their transactions and activities.
This technique of data analysis can be implemented to enrich the compliance process, including transaction monitoring. This will make it possible to detect clients or customers with suspicious activity or behavior for investigation and reporting.
Indeed, what is missing in traditional behavioral analysis devices is the data linking and identification of hidden transaction patterns, which can emerge because money launderers are generally one step ahead.
ML understands the screening parameters based on available and linked data sources, and data sets. ML identifies customers and their transactions based on the linked profile matching factors, such as full and exact name matching, date of birth, nationality, jurisdiction, sources of income, purpose of the account and transaction, etc.
Machine Learning in Sanctions Screening
Using artificial intelligence (AI) and machine learning (ML) enabled algorithms demand the input of relevant and correct identification or matching data, to perform deep and relevant sanctions screening.
ML helps compliance specialists counter different screening challenges such as typos, incomplete data strings, use of nicknames, spelling differences, etc. ML helps in the identification and understanding of broader data and different data sets, and enables the extraction of meaningful data for decision purposes.
ML capabilities enable the maintenance and use of correct data sets and data fields from thousands of transactions, to perform transaction review and monitoring.
The use of ML helps avoid inadequate name screening, and it ultimately increases the efficiency of anti-money laundering (AML) measures through the identification of true matches at the time of onboarding and during the relationship with customers. ML, based on input parameters, performs name screening, sanction screening, negative list screening, and various other screenings to improve regulatory compliance. ML algorithms detect and lower the risk of identifying a customer who is not a sanctioned individual.
ML algorithms may solve sanction search inaccuracies, such as the watchlists and international sanctions may contain names belonging to Russian, or other nationalities that do not use the Latin alphabet, which may lead to name search inaccuracies. ML helps in using complex big data, with hundreds of millions of names and large comparison scenarios, and provides meaningful data outputs for compliance decisions.
ML ensures that all negative individual or entity lists, linked media sources, sanctions lists, and internal negative lists are considered in performing the screening process. Relevant search parameters are extracted based on the search to be performed, and the data available in the lists are instantly scanned to identify the exact or near matches for better compliance decisions.
As ML learns over time based on input and practical experiences, the process of scanning and searching becomes more accurate over time.
Final Thoughts
Machine Learning (ML) has revolutionized the landscape of sanctions screening and regulatory compliance. By harnessing ML’s ability to swiftly extract and process data from a plethora of sources, institutions can amplify their anti-money laundering (AML) and counter-terrorism financing (CFT) measures. Traditional methods often fall short in detecting hidden transaction patterns or reconciling discrepancies like typos and non-Latin characters.
However, ML fills these gaps, understanding intricate screening parameters, refining client identification, and rectifying name search inaccuracies. Furthermore, ML continually adapts, evolving its accuracy with each new data input. This continuous learning ensures that compliance programs remain dynamic, up-to-date, and one step ahead, turning vast data into actionable insights for secure and efficient decision-making.
