Once Upon a Time in Rural China...

In a small town in Jiangxi Province, Zhang Xiaojian—an official in the local radio and television bureau—was exposed for decades of corruption. Allegations included accepting bribes and embezzling public funds. These details were meticulously reported in local Chinese newspapers and websites.

Would your adverse media screening tools have flagged this case?

The Two Paths to Screening—and Their Problems

Global businesses have two primary methods for adverse media screening: open-source platforms (like Google) and international adverse media databases. Both come with significant challenges, especially when dealing with non-English sources or regions outside the Western world.

1. Open Source: Searching for Needles in a Haystack

Using tools like Google for open-source searches might seem like the perfect solution. After all, it’s accessible, free, and comprehensive—right? Not quite.

Let’s take the example of Zhang Xiaojian. Searching for his name in Mandarin (张晓建) brings up multiple local news sources. But there are challenges:

• Translation Gaps: Many smaller outlets publish content only in their native language. Machine translations often miss nuances, context, or cultural references, making it difficult to fully understand the scope of the case.
• Time and Resources: Reading through untranslated or poorly translated articles requires hiring analysts who are fluent in the language—a resource-intensive process.

This method works, but only if your team has the linguistic capabilities and the bandwidth to dig through hundreds of articles and interpret them correctly.

2. International Adverse Media Databases: A Lot of Nothing

For those without the resources to manage open-source searches, international adverse media databases are the next best thing. These databases aggregate risk-relevant news from around the world and provide companies with pre-screened results.

The problem? They often underperform in regions outside the West.

• Local News Gaps: Databases prioritize news sources in English and major European languages, leaving significant blind spots in Asia, Africa, and Latin America. Local reporting—like the Zhang Xiaojian case—is rarely captured.
• Outdated or Irrelevant Data: Databases frequently rely on older reports or summaries, meaning real-time developments in local languages might not appear for weeks or months.

For instance, searching for Zhang Xiaojian in a standard international database often yields no results—because his case was reported primarily by Chinese outlets in Mandarin. These systems often return “a lot of nothing” regarding nuanced or localized issues.

The Real Risk of Missing the Story

Language barriers and limited data sources create vulnerabilities for businesses:

• Regulatory Risks: Failing to uncover adverse media can lead to compliance failures, fines, or regulatory scrutiny.
• Reputational Damage: Partnering with individuals or organizations tied to corruption or fraud can harm your brand.
• Operational Inefficiencies: Teams spend excessive time and resources piecing together incomplete or unreliable information.

At the heart of the problem is a simple truth: the world isn’t standardized in English. Local languages, regional nuances, and non-Western reporting hold vital clues to understanding risk.

So, How Can We Do Better?

Effective adverse media screening in foreign languages requires:

1. Access to Local Media: Incorporate tools or systems that surface news from local outlets, even if they’re not in English - for example through a Google search.
2. Reliable Translation: Use advanced AI or human resources to ensure translations maintain the nuance of the original reporting.
3. Real-Time Capabilities: Focus on solutions that track live developments, not just outdated databases.
4. AI-Driven Risk Assessment: Use AI to automatically classify risks and flag relevant results, saving time and reducing missed threats.

The Zhang Xiaojian case is a reminder that the most important risks often lie hidden in plain sight—buried in a language or source many tools simply can’t handle. However, with AI-driven analysis, these hidden risks can be identified and assessed quickly, ensuring that businesses stay ahead of potential threats.