Case Study

Simplifying Sanctions with Enigma Compliance

Sanctions Blog Image

The regulatory compliance landscape is rapidly changing. Financial services organizations are faced with a growing set of specific requirements that legacy data infrastructure and workflows are not well-suited to handle. With sanctions lists from the Office of Foreign Assets Control (OFAC) shifting constantly, flexible technologies and rapid decision-making are now critical in order to avoid conducting business with high-risk individuals and companies.

With a substantial number (often in the millions) of sanctions alerts awaiting processing for false positive determination, multinational financial services organizations need an alternative to their highly manual and time-consuming case investigation processes. In partnering with Enigma, organizations will be able to leverage all available data to improve screening accuracy and introduce new operational efficiencies to reduce the cost of achieving compliance.

The challenge

Fragmented investigations and false positives

Organizations are facing a surge in volume of sanctions alerts coupled with a fragmented investigation process. This generates a large number of false positives and a significant amount of manual work.

Verification of a potential customer’s match to an entity on a sanctions watch list often requires cross referencing multiple disparate systems and data sources. For financial services organizations, of the millions of alerts generated each year, only a fraction are likely to be correct matches.

New alert profiles often lack the information necessary for investigators to make an accurate determination of risk. As a result, highly-skilled investigators with extensive subject matter expertise spend the majority of their time on data fetching and preparation rather than analysis and decision-making. Building adequate customer profiles for case investigation drains not only time and valuable resources, but also the team’s motivation.

The solution

Streamlined integration and automation

With access to the right data, financial services organizations could drastically improve matching accuracy.

Enigma developed an integration and automation framework that leverages data pre-fetch and algorithmic prioritization to cut down on an organization’s alert backlog and accelerate investigation of new incoming alerts.

This approach was designed to drive two primary accuracy and efficiency lifts:

  1. Information-rich starting point: Our solution delivers all data necessary to assess a false positive immediately upon case opening, automating data collection to provide investigators with a contextualized customer profile.

  2. Case prioritization: In collaboration with our client’s subject matter experts, Enigma develops algorithms that rank alerts using a match confidence scoring schema in order to identify likely false positives. This enables prioritization of high-risk cases and rapid (or automatic) closure of low-risk/low-match alerts.

Additionally, the Enigma Financial Services Compliance Solution leverages our broad repository of public data to augment an organization’s existing data with the public data footprint of each prospective customer. This provides new metadata to identify additional relationships between entities and improve the accuracy of matching algorithms.

The results

Increased efficiency, reduced cost

With the automation and aggregation of cleansed and de-duplicated decision data, Enigma is able to significantly lessen manual busy work and reduce investigation time. Centralized access to critical data and metadata improves match accuracy and facilitates machine learning prioritization, enabling sanctions teams to focus on analysis and decision-making instead of research and data preparation.

Overall, organizations will be able to realize greater return on compliance resources, accelerated time-to-decision, and increased accuracy, all of which contribute to better compliance and lower risk at a reduced cost.

Learn more about the Enigma Financial Services Compliance Solution here.