Socure, the leading provider of artificial intelligence for digital identity verification, sanctions screening, and fraud prevention, unveiled the industry’s most accurate AI-powered global watchlist screening and monitoring solution. The solution enables financial institutions to accurately screen, monitor, and assess new and existing customers against the Office of Foreign Assets Control (OFAC) sanction lists and politically exposed persons (PEP) databases, adverse media, and custom watchlists.
Organizations have long been challenged with outdated legacy watchlist tools that can increase risk of regulatory enforcement action from inaccurate results while also having high false positives that require error-prone manual reviews. These results require exceedingly large staffing costs associated with manual reviews in an environment of limited resources and budgetary constraints. Compliance analysts waste time chasing irrelevant alerts while organizations face legal and reputational risks from potential enforcement actions. OFAC violations for example, face monetary fines ranging from thousands of dollars to several million along with severe impacts to business operations.
Leveraging AI and machine learning (ML), Socure solves for these challenges and sets the new standard for compliance, achieving a 20% lift in sanctions screening accuracy compared to legacy solutions, a 30% reduction in false positives, and a 75% reduction in manual review time from an industry average of 10 minutes to just 2 ½ minutes.
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“As organizations struggle with high false positives and costly manual reviews emanating from many current watchlist screening tools, there is significant value to the industry in transforming and automating these systems,” said Chuck Subrt, Fraud & AML Practice Research Director at Datos Insights. “With its new global watchlist screening with monitoring solution, Socure has introduced innovations that enable compliance leaders to reduce the noise, sharpen accuracy, and elevate operational efficiency.”
Industry leading accuracy and performance with two-stage scoring
To drastically improve accuracy and reduce investigation time, Socure developed the industry’s first two-stage scoring system, paired with strong operational controls. First, the solution surfaces potential matches within customer-controlled risk tolerances and produces a name match score. Returned matches are intelligently grouped into comprehensive entity profiles enriched with supplementary customer information creating a “candidate pool” to clearly demonstrate sanctions compliance.
From these potential matches, each personal identifiable information (PII) element is scored for relevance to elevate the correct profile and generate an overall entity correlation score. This second score helps answer the likelihood that the customer and the source list match are indeed the same person, answering the same question that an analyst tries to match in a manual review.
This approach not only minimizes false positives, but also reduces the need for manual intervention resulting in a 60% savings in resource and staffing costs from fewer reviews and less time spent for remaining reviews.
As an example, a high name match score and low entity correlation score is indicative of a false positive, and Socure’s customers can choose to automate dispositioning of irrelevant matches with customizable tolerance thresholds saving time and resources.
“Sanctions and KYC risk processes have traditionally been imprecise and created subjective, manual processes. In today’s dynamic environment, organizations need dependable automation tools to drive both consistency and efficiency. Socure is introducing the first product in the sanctions and watchlist space that aligns with regulatory expectation, while creating automation to improve efficiency and reduce noise in the process,” said Debra Geister, VP Compliance Solutions at Socure.
Using machine learning to revolutionize sanctions screening and risk identification
The use of ML algorithms also offer a solution to compliance challenges. Through the use of ML, Socure can identify intricate patterns, adapt matching techniques, and correlate contextual information such as addresses, dates of birth, and national IDs to a name match, significantly reducing false positives and negatives.
Streamlined case management powered by generative AI
Socure employs generative AI within its watchlist solution to automatically filter, rank, classify, extract, or summarize various information from processed data to highlight relevant insights. This eliminates unnecessary manual intervention so customers receive results that are easier to understand and more useful for making risk decisions.
For cases requiring review, Socure’s intuitive case management system expedites analyst review with automated case generation and assignment paired with generative AI for a detailed entity resolution analysis, explainability for audits, and a system generated recommended course of action. With just one click, analysts can accept the disposition. If the need arises to revisit a prior decision, the solution also offers self-service custom reporting and audit capabilities to generate reports on performance, adherence to policy, or in the case of a regulatory exam for effortless compliance.
Natural Language Processing (NLP) and deep learning revolutionizes adverse media screening
Risk screening through adverse media has long been plagued by overwhelming false positives, driven by simple boolean string match criteria used by legacy solutions. Socure has solved this problem with a novel approach leveraging natural language processing and deep learning to perform contextual risk assessment. For example, an article published about a large number of shots in a basketball game can accurately be classified by Socure as not relevant and an article related to a sporting event, instead of an incorrect match for a violent crime as is the case with legacy keyword matching.
Ongoing customer status monitoring
To meet the demands of today’s dynamic regulatory environment and volatile geopolitical landscape, Socure also delivers continuous and fully-automated customer monitoring alerting for customer status changes such as inclusion on a sanctions screening list after the initial screen has been completed.
This approach helps ensure immediate response to potential customer risk and regulatory status changes compared to traditional periodic batch processing which could allow a customer added to a list between batch screens to transact. Socure’s continuous screening not only avoids costly fines and consent orders, it helps prevent illicit funds from entering the US financial system.
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