HLBNGA Adverse Media Screening: Revolutionary Breakthroughs in False Positive Management

These days, the importance of thorough adverse media screening can’t be overstated. Unfortunately, this crucial process is often hampered by the high incidence of false positives, especially in ongoing screening scenarios.

False Positives in Continuous Screening

Traditionally, adverse media screening has been a staple during client onboarding but has seen less emphasis in continuous monitoring. The primary challenge? The overwhelming number of alerts and false positives that emerge. This has led many institutions to sidestep ongoing screening, leaving potential risks unchecked.

Before we explore some of the latest technical innovations, let’s delve into some common challenges experienced with Adverse Media screening as it relates to the management of false positives.

Common Challenges in Dealing with False Positives

• Filtering out the noise: Analysts often struggle to filter relevant information from vast data pools, especially when common names generate millions of search results. For example, the presence of a non-implicated entity in the same sentence or article as a negative report can lead to false positives in adverse media screening. This proximity often results in innocent parties being mistakenly associated with adverse activities. Distinguishing between actual involvement and mere coincidental mention in interconnected news stories is a complex task, crucial for avoiding misleading interpretations, false positives, and unjust consequences for those wrongly implicated.

• Screening against irrelevant media: The Financial Action Task Force (FATF) advises businesses to conduct adverse media screening, focusing on areas like criminal activities, financial fraud, corruption, and terrorism involvement. However, The FATF emphasizes the importance of a risk-based approach, ensuring that the screening is both efficient and relevant to the specific risks faced by the business. It’s therefore important for businesses to align this screening with their specific industry risks. For example, a company in the financial sector should be more vigilant about financial fraud and money laundering, while a manufacturing firm might focus more on corruption, supply chain disruptions, and environmental violations.

• The absence of consistent scoring mechanisms: Unlike the standardized and widely accepted nature of credit scores, the absence of a consistent adverse media score makes quantifying an entity’s media risk challenging. This lack of a universal metric leads to subjective and varied assessments, much in contrast to the uniformity seen in credit scoring. Such inconsistency in media risk evaluation can lead to either overestimation or underestimation of risks, underscoring the need for a more standardized approach akin to credit scoring for more accurate and reliable assessments of media-related risks.

• Persistent Article Flagging: Handling new adverse media alerts for every article, rather than only for significant changes in a case or new instances of involvement, presents a major challenge. This approach can lead to an excessive number of alerts, causing alert fatigue and potentially burying important information under less relevant notifications. It strains resources and diminishes the effectiveness of the screening process, as critical updates might be missed or delayed amidst the constant influx of alerts. A more targeted alert system is needed to maintain efficiency and accuracy.

• Prove and disprove: Disproving allegations in adverse media screening is often challenging. It requires extensive investigation to find evidence of innocence, which can be more complex and time-consuming than confirming negative reports. The lack of clear evidence often complicates the process of definitively disproving claims. Additionally, overcoming initial negative impressions in public opinion can be difficult. This task demands high diligence and fairness, as inaccuracies can lead to significant reputational damage and legal consequences.

Considering the complexities of adverse media screening, the adoption of Machine Learning and Artificial Intelligence (AI) is essential. These technologies are key in effectively managing false positives and the surge of new alerts, offering advanced data analysis for more accurate and efficient screening processes.

AI’s ability to analyse vast amounts of data with speed and precision enables more accurate identification and categorization of relevant information. When used correctly, this technology significantly reduces the risk of information overload and alert fatigue, intelligently filtering out irrelevant data/entities, re-flagging only entities with significant changes in cases, standardizing scoring mechanisms and taking probable absolution into consideration.

By leveraging AI, organizations can enhance the efficiency and effectiveness of their screening processes, ensuring that they focus on genuinely pertinent risks and maintain compliance with regulatory standards.

HLBNGA’s Technological Leaps

Over the past few years, HLBNGA has achieved significant advances in AI technology, addressing challenges across deployments ranging in size from a few thousand to millions of customers.

Here’s how HLBNGA is revolutionizing traditional adverse media screening methods:

• Entity Identification: HLBNGA leverages several different Natural Language Processing (NLP) techniques for precise entity identification, differentiating between individuals and organizations with high accuracy. Utilizing contextual analysis, semantic understanding, and entity resolution, it accurately identifies and categorizes entities in complex texts. This approach minimizes misidentifications and enhances the understanding of each entity’s relevance and relationships, ensuring more accurate and informed decision-making.

• Advanced Categorization: HLBNGA revolutionizes adverse media screening by categorizing (tagging) the nature of individual sentences from already-relevant articles, rather than just categorizing entire articles which speeds up review times. This approach not only aligns with FATF and 6-AMLD guidelines but goes further, including categories like corporate controversies, financial issues, and political associations. This ensures that screening is tailored to specific industry risks.

• COMFORT™ Scoring and contextual analysis: The COMFORT™ score, a patented feature of HLBNGA’s AI system, quantifies the level of adversity surrounding entities detected in adverse media. It evaluates factors such as the frequency, severity, and recency of negative mentions, as well as the credibility of the sources. This score helps in setting screening thresholds, distinguishing high-risk entities from lower-risk ones, thereby enhancing screening accuracy and reducing false positives. The COMFORT™ score has become a crucial tool for efficient RBA (Risk Based Approach) risk assessment in media screening processes.

• Re-flag Suppression: HLBNGA’s technology further streamlines ongoing adverse media screening by suppressing re-alerts for entities already flagged, unless there’s a significant change in their risk profile, such as new adverse associations. This approach effectively reduces false positives, ensuring that users are alerted only when there are meaningful developments in an entity’s risk status, thereby enhancing the efficiency and accuracy of the screening process.

• Prove and disprove: HLBNGA effectively identifies instances where an entity is exonerated or absolved in adverse media, such as being found not guilty or having charges dismissed. This capability is vital in adverse media screening, where disproving allegations is often more complex than confirming them. By highlighting evidence of innocence, HLBNGA ensures a balanced and accurate assessment, crucial for maintaining fairness and mitigating the impact of initial negative impressions in the screening process.

Significant AI Advancements on the Horizon

HLBNGA’s AI has made significant progress toward precisely identifying the focal entities in complex narratives, even in paragraphs dense with multiple references and irrelevant details. This is a leap towards a more nuanced and accurate adverse media screening workflow.

How you can access this data

HLBNGA provides adaptable adverse media screening solutions, including real-time APIs, a user-friendly web-based SaaS platform, and options for batch and ongoing screening. These solutions cater to client-specific risks and preferences, emphasizing HLBNGA’s mission to reduce false positives and deliver effective adverse media research tools.

CONTACT US for a free consultation.

Related Reading:

What is Adverse Media screening and how can you modernize yours? 

RiskSecure: Revolutionizing risk management with HLBNGA’s next-generation adverse media screening

Why Adverse Media Screening Services Outshine Chatbots like ChatGPT.