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BRINGING SANCTIONS AND ADVERSE MEDIA SCREENING INTO THE MODERN ERA

Jeffrey F. Ingber
14 June 2024

The process of screening natural persons, legal entities, and transactions applies in a variety of AML contexts—with two key ones being sanctions and adverse media screening. It’s integral to a satisfactory AML program, but rife with errors, delays, improper decisioning, inadequate recordkeeping, and outsized costs, and increasingly difficult for financial institutions to manage properly. These institutions are struggling to bring more efficiencies and better risk management to their screening systems, understanding that throwing human resources at the problem is not the answer.

The benefits of innovation

The benefits of employing modern artificial intelligence (AI)-based tools to enhance the sanctions and adverse media screening processes are compelling. AI can greatly assist by retrieving relevant information, executing researches, analyzing data, making decisions on alerts (with a hand off to a human analyst in complex situations), and generating and publishing detailed reports and an audit trail. This brings greater speed, accuracy, and comprehensiveness, as well as reduced false positives.  Also, with AI-based technology, each screening process can readily be transformed into a continuous one in which the financial institution is notified in real-time when a relevant new regulatory requirement must be assessed or a development of interest about a customer occurs, enabling immediate action to reduce AML risk exposure.

“In effect,” notes Manish Chopra, Capgemini Executive Vice President—Global Risk and FCC Business Leader, “implementing an automated screening analyst is like hiring a bevy of employees who are immediately productive, work at high speed, never get tired, and are available 24/7. This represents a cost-effective alternative to offshoring, outsourcing, or temporary labor.”

Human productivity also is a problem that AI can address. The traditional performance of screening reviews and alerts is an arduous process that, over time, can wear down and demoralize human analysts. “Enabling individuals to collaborate with an AI-based system,” explains Art Mueller, WorkFusion Vice President—Financial Crime, Banking, and Financial Service, “frees them from performing menial tasks such as copying, pasting, and data gathering and review, allowing them to work on higher-value investigations and, thus, be used in a more productive, strategic way.” 

Addressing implementation considerations

Introducing an AI system into a legacy process can be done relatively quickly and efficiently, and on a reasonable budget, but it must be performed carefully and thoughtfully. Starting slowly with modest initial goals is often the best approach.

The process of incorporating AI into a transaction monitoring system includes a number of steps:

  • Data collection and preparation (including ensuring data cleanliness and structure);
  • Model selection;
  • Model training and validation;
  • Integration of models with existing systems;
  • Deployment and testing;
  • User training and adoption; and
  • Ensuring continued compliance with all applicable laws and regulations.

As with any AI system, a huge consideration is comprehensive, quality data, given that the models rely so heavily on them. Data accessibility, sourcing, quality, consistency, privacy, and security all are critical, along with integrating end-to-end workflows to allow for a seamless stream of information.

Overcoming implementation challenges

There are various inherent challenges posed by AI-based tools that should be kept in mind when implementing them, including resistance to change, skills gaps, legacy system compatibility, data accessibility, scalability, security concerns, and user training and adoption. 

Useful implementation principles are that the AI tools should be able to support multiple lines of business, live with other technology investments, and change only what needs to be changed. A financial institution should test the new technology as much as it needs, but ideally avoid parallel runs in production, which add cost and concerns. Also of great importance is that every relevant area across the lines of business—such as Risk, Compliance, IT, Operations, Independent Audit, and HR—buy into and assist in the transformation process.

As with any significant change, senior management buy-in is vital factor for the success of innovation initiatives. This involves the active participation, endorsement, and ongoing support of top-level executives (including resource allocation) aligning the entire organization toward the innovation implementation goal.

Obtaining regulatory acceptance

Given the highly regulated nature of the financial industry, another key consideration is ensuring regulatory acceptance. “The good news,” notes Joe Robinson, Co-founder & CEO of Hummingbird, “is that financial regulators globally have, in recent years, embraced AI-driven innovation as an appropriate if not necessary development in addressing financial crime.”

In this regard, there are important aspects to regulatory acceptance that also must be taken into account. These include ensuring explainability, transparency, accountability, and proper model risk and data management. Also important is a comprehensive, up-to-date set of metrics that can readily be shown an examiner or supervisor is critical (e.g., timeliness of alert decisioning, rate of false positives, AI determinations overturned by human supervisors) to demonstrating the effective functioning of the AI system. Moreover, a thorough set of documentation should be maintained of the AI model’s development, training process, and deployment, which should be readily accessible to regulators, auditors, and other stakeholders.

In conclusion

In sum, the benefits of employing modern AI-based tools to enhance the sanctions and adverse media screening processes are compelling, and have been embraced by financial industry regulators. However, implementing these tools presents certain challenges that require careful planning, internal support, collaboration between IT and business units, attention to regulatory imperatives, and a strategic approach to ensure a smooth integration.


Later this month, Capgemini, Hummingbird and WorkFusion will be hosting the executive panel discussion Screening in 2024: challenges and opportunities, in NYC. We look forward to connecting with clients, partners and business leaders to explore how technology-driven screening is key in an environment of emerging threats and institutional challenges.

Meet our experts

Manish Chopra

Global Head, Risk and Financial Crime Compliance
Manish is the EVP and Global Head for Risk and Financial Crime Compliance for the Financial Services Business at Capgemini. A thought leader and business advisor, he partners with CXOs of financial services and Fintech/payments organizations to drive transformation in risk, regulatory and financial crime compliance.

Jeffrey F. Ingber

Senior Advisory Consultant, Risk and Financial Crime Compliance
A former ex-Senior Fed Official, Jeff runs Capgemini #RegDesk that helps clients stay abreast of developments in the FCC landscape and demystifies complex regulations into clear actionable insights. He provides a rage of advisory services to clients across the FCC lifecycle and helps them tackle the ever-changing global risk landscape.

Supriyo Guha

Senior Director, Financial Crime ComplianceCapgemini
Supriyo is the practice lead for financial crime compliance at Capgemini. He leads strategic industry-first initiatives to help clients transform their anti-financial crime functions and heads Go-to-market for Capgemini’s marquee FCC clients.

Peter Weitzman

Practice Lead, FCC Compliance and Risk Analytics