Adverse Media in the Age of AI: Insights from Industry Experts
We have more data at our fingertips than ever before, but compliance teams continue to make critical reputational risk decisions based on incomplete and outdated information. The good news is it doesn't have to be this way. In our recent webinar, Castellum.AI CEO Peter Piatetsky spoke with industry experts Sarah Beth Felix (Founder & CEO, Palmera Consulting), Shabbir Hussain (Head of Financial Crime Compliance Systems, CFSB), and Ashley Farrell (Financial Crimes Solutions Leader, Baker Tilly US).
The panel explored how AI is reshaping adverse media screening and what compliance teams need to know about implementation best practices. Here are the key takeaways from the session:
Why Adverse Media Screening Matters for Financial Institutions
While adverse media screening isn't explicitly mandated by the US regulations, it operates in what one panelist calls "the spirit of the law".
Think of it this way: Without adverse media screening, you're making risk decisions about customers while wearing a blindfold. You might have their transaction patterns and basic KYC data, but you're missing the public record that could reveal crucial context such as pending investigations, regulatory actions or reputational issues that directly impact their risk profile.
Alert investigations take longer without context. High-risk customer reviews become mere guesswork. Most critically, institutions miss early warning signs of fraud, money laundering and sanctions evasion that timely media intelligence would catch, often costing them severe financial penalties.
How AI is Transforming Adverse Media
Large language models excel at addressing adverse media's core challenge: processing massive volumes of information quickly while maintaining accuracy.
Instead of having analysts sift through irrelevant alerts, AI-based solutions provide accurate, context-based risk signals.
Contextual analysis: Understanding whether someone is a victim, perpetrator or bystander in news stories
Summarization: Condensing large amounts of media data into actionable, easy-to-ingest insights
Translation: Processing global news sources across multiple languages
Relevancy scoring: Applying logic that weighs additional information such as extracted names, negative news severity and sentiment analysis to intelligently rank results
Why Customization is Critical
Generic adverse media tools create more problems than they solve. For example, a community bank's risk concerns differ vastly from those of a global cryptocurrency exchange. Cookie-cutter screening generates irrelevant alerts that waste analyst time and obscure real threats.
Your adverse media tool should work the way your business operates, not force you to adapt to its limitations.
Panelists emphasized using solutions that separate general media noise from truly adverse content and provide insights tailored to your institution's unique risk exposure. At a minimum, it must offer:
Risk parameter tuning: Systematic classification of negative news (financial crimes, corruption, human rights violations and more) with advanced keyword filtering
Priority-based filtering: Separating high-priority threats like financial fraud and serious convictions from low-risk mentions like unproven allegations or traffic violations
Importance of Data Quality and Source Authority
More sources don’t mean better screening. Source quality and authority trump quantity every time. The best adverse media solutions prioritize source credibility over volume, ensuring analysts work with verified information rather than noise.
One panelist highlighted the importance of local media coverage. Local sources often provide more relevant intelligence than major publications. For instance, a local corruption scandal involving a Maine government official might never reach New York Times headlines, but it's critical intelligence for any institution doing business in that region.
When evaluating vendors, ask the hard questions: What editorial controls exist? How much human oversight guides the AI? Solutions relying purely on automated content ingestion without editorial safeguards risk incorporating unreliable sources or AI-generated articles that masquerade as legitimate journalism.
Managing Misinformation and Bias
With the rise of AI-generated, mass-produced content, financial institutions must develop strategies to combat misinformation. Best practices include:
Corroboration: Seek multiple sources from different perspectives before making decisions
Human authorship: Look for articles written by identifiable journalists
Source authority: Prioritize established publications with long track records over newer outlets
Editorial oversight: Choose screening vendors that incorporate human editorial judgment in source ranking
Moving Beyond Willful Blindness
Financial institutions should implement adverse media screening as a minimum requirement in high-risk customer review processes. Even if adverse media screening isn't explicitly required by regulation, it provides crucial contextual information that could determine whether to file a Suspicious Activity Report.
The panelists emphasized that doing nothing approaches "willful blindness", knowing information might be available but choosing not to look.
Dispositioning Adverse Media Alerts
Alert disposition starts with one critical question: Is this adverse media relevant to the suspicious activity under investigation? Panelists also warned that compliance teams need to avoid tunnel vision. When adverse media reveals different potential financial crimes, even unrelated to the current alert, that intelligence should be escalated to high-risk customer review teams. Today's irrelevant information might be tomorrow's smoking gun.
Moreover, thorough documentation is non-negotiable. Every decision needs clear reasoning explaining why certain information was considered relevant or dismissed. This protects institutions during regulatory audits
Should Social Media Be Part of Adverse Media Screening?
Social media plays a growing role in fraud schemes, but it's problematic for formal adverse media screening. The information is often unverifiable, potentially falsified and requires substantial human judgment to interpret correctly.
Think of social media as supplemental intelligence. Use social media to add context to existing investigations, but don't let it drive your screening decisions. Stick with verified, authoritative sources for alert generation.
How to Build the Business Case for Better Adverse Media Tools
To justify investment in advanced adverse media solutions, find out the effectiveness of your current approaches: What's the ratio of alerts to eventual suspicious activity investigations? How much analyst time goes to reviewing irrelevant alerts?
Alert-to-SAR ratios under 5% mean 95% of reviewed alerts don't warrant SARs, essentially wasting team efforts. AI-powered systems enable teams to improve screening precision and speed up operations. The goal is to demonstrate how improved adverse media screening results in reduced operational costs and improved risk detection.
For a comprehensive guide on evaluating adverse media solutions, download our Adverse Media Screening Buyer’s Guide.
Final Thoughts
The takeaway from our expert panel was clear: Don't let perfect be the enemy of good when it comes to adverse media screening. Even basic AI-powered tools beat manual Google searches, and the cost of inaction far outweighs the investment in modern screening solutions.