🔁 New to this topic? Read our foundational post: What Are NLP, NER & AI in Risk Management?
In 2025, risk management has become more intelligent, more contextual, and significantly more automated than ever before. What was once a slow and largely manual process is now being redefined by advances in artificial intelligence, particularly through the use of large language models (LLMs), real-time search, and multilingual understanding.
These changes are not just incremental. They represent a fundamental shift in how risk and compliance professionals discover, understand, and act on risk-related information. In this follow-up to our original explainer on NLP and NER, we take a deeper look at the technology behind this shift and how Business Radar is staying at the forefront of the transformation.
1. From Rule-Based Checks to Context-Aware Reasoning
Traditional compliance systems have long relied on rigid rule sets. These systems would trigger alerts based on simple thresholds or binary flags, such as identifying transfers over a specific amount or highlighting name matches with minimal contextual validation. While useful in certain cases, this rule-based approach often produced large volumes of false positives.
In 2025, many organizations are moving to systems that use context-aware reasoning. This means the AI is no longer just checking for simple criteria. Instead, it evaluates the entire picture. It considers behavior patterns, recent media exposure, relationships with other entities, and more.
At Business Radar, we now use LLM -powered reasoning to validate compliance matches more intelligently. We generate justifications for flagged results, helping users quickly understand why something was flagged and whether it truly needs attention. This improves decision-making and reduces alert fatigue.
“Rather than outputting simplistic alerts, AI models now reason like analysts, assessing whether a transaction is truly suspicious in its context.”
Quick summary:
- Extended ruling with both static rule-based alerts and dynamic, context-aware decisions
- LLM’s used to justify and triage alerts
- Full audit trails for clear picture and controlled output
- Significant reduction in false positives and manual review burden
2. LLMs in Adverse Media Screening Pipelines
Large language models have proven especially valuable for making sense of unstructured data like news articles and online content. For risk teams, this is particularly relevant in adverse media screening. Instead of asking analysts to manually scan multiple sources, LLMs can now retrieve, summarize, and highlight key risks.
At Business Radar, we leverage LLM’s to summarize relevant media coverage, explain why a particular mention matters, and help users determine if the content indicates reputational or compliance risk. For example, when a client is mentioned in the context of fraud, bribery, or regulatory investigation, our platform generates concise summaries with supporting sources.
A report by The Fintech Times noted that by mid-2023, 86% of compliance teams were exploring LLMs for this purpose. Although adoption was still early at the time, we now see these tools being embedded into many real-world compliance workflows.
Quick summary:
- LLM summarizes negative media into actionable insights
- Relevance and risk context are automatically identified
- Analysts can skip noise and focus on real threats
3. Multilingual NER and Real-Time Entity Resolution
Risk is global, and so are the data sources compliance teams rely on. That’s why risk management tools must be able to understand and resolve information across multiple languages and data formats. In recent years, multilingual named entity recognition (NER) models have become increasingly sophisticated. These models allow systems to accurately extract names, places, organizations, and other entities from global news coverage, regardless of the language in which the content is written.
At Business Radar, our multilingual NER models are trained to extract entity-level data from sources in multiple languages. This ensures comprehensive coverage and reduces the chance of missing a red flag that appears in a foreign-language publication.
In addition, we have implemented deep matching logic to match entities even when their names appear in different forms or scripts. This approach helps detect relationships between similar entities that would otherwise be difficult to identify. For instance, it can recognize that “Peter Johns” and “Pete Johns” may refer to the same individual, depending on context.
Research from Google and Stanford confirms that embedding-based approaches improve entity resolution across languages and scripts. These methods outperform traditional string-matching techniques and are especially effective for names, organizations, and multilingual datasets.
This combination of multilingual NER and vector search helps Business Radar eliminate false negatives, strengthen cross-border compliance coverage, and ensure consistent, accurate screening even in high-noise environments.
Quick summary:
- NER now works across languages with high precision
- Vector-based matching improves entity resolution
- Business Radar catches name variants and transliterations with confidence
4. Timeline Intelligence: From News Volume to Actionable Insight
Understanding what happened is just as important as identifying that something happened. To support that goal, we introduced the Event Timeline feature in Business Radar. This feature maps risk-related events over time, giving users a chronological view of how a situation has developed.
The timeline highlights key developments by clustering related articles and identifying unusual spikes in media attention. We compare article volumes to a company’s historical baseline to detect outliers and signal escalation.
This helps users identify when an issue began, whether it has intensified, and how it may evolve. By visualizing activity in a clear timeline, teams can spot patterns, report with confidence, and act proactively.
Quick summary:
- Visualizes event clusters and media spikes
- Compares article volume to a company’s baseline
- Provides fast understanding of timeline and escalation
5. Explainability and Auditability: Making AI Accountable
As AI plays a greater role in compliance workflows, regulators and risk officers alike demand transparency. It is no longer acceptable for a system to simply return a result without showing how or why it arrived at that conclusion.
This is why we have prioritized explainability and auditability within Business Radar. When GPT gives information, it also generates a human-readable justification. These justifications are stored alongside audit trails, so users and auditors can trace what criteria were applied.
This approach not only builds trust but also satisfies regulatory expectations. The EU’s AI Act requires that AI-based compliance systems provide transparent, explainable decisions. We believe this kind of visibility is essential to modern risk tools.
Quick summary:
- Every GPT-based decision is logged and justified
- Human-readable explanations support auditability
- Compliant with emerging regulatory frameworks
6. Agentic AI for Smarter Workflows
We’ve also added intelligent automation features that assist with improving the accuracy and structure of monitored company data. While we’re careful not to automate decision-making fully, we do use agentic AI to enhance results for company monitoring.
These behind-the-scenes agents help ensure cleaner records and better matching across our datasets. The result is more precise risk profiles and fewer missed insights, without disrupting the user’s control or workflow.
Quick summary:
- AI agents improve data accuracy and consistency
- Users benefit from cleaner entity profiles and better results
- Enhancements operate quietly in the background
7. Trends Shaping the Market in 2025
Several broader trends are shaping the future of RegTech and risk management:
- Generative AI in compliance: Organizations are using LLMs to summarize regulations, draft policy changes, and act as conversational compliance assistants
- Cloud-native compliance platforms: Most compliance tools are moving to the cloud for greater scalability, faster updates, and easier integration
- Self-service onboarding with AI: Customers can now onboard digitally through fully AI-powered workflows that verify IDs, screen watchlists, and detect anomalies
Final Thoughts: A Smarter Risk Future Is Here
Risk management in 2025 is not just more automated. It is more adaptive, more intelligent, and more context-aware. By combining advanced AI with human oversight, compliance teams can now focus on what truly matters, while staying confident that nothing critical is being overlooked.
At Business Radar, we believe that AI should empower risk professionals, not replace them. That’s why we build tools that prioritize accuracy, transparency, and efficiency. Letting you make faster, better decisions.
Bonus: A picture of us discussing this blog article as a cross-functional project.
Want to see how Business Radar works? Book a demo