The Compliance Cost of Poor Data in Screening

Financial firms today rely predominantly on precise and accurate data to make key decisions for their customers and business.

According to Gartner, the cost of poor data quality is tremendously colossal, with businesses losing an average of $12.9 million per annum.

The message is clear: Even if you automate your anti-money laundering (AML) or Know Your Customer (KYC) processes, that automation must follow regulations correctly (which means they must be operated, designed, and configured as per the regulations set by FATF, OFAC, FinCEN, etc).

Automation must be based on data sourced in line with AML compliance requirements. Because, for automation to correctly follow regulations, it absolutely relies on the quality and nature of the information (data) it processes.

If your systems are misconfigured, rely on poor data, or are not updated with current sanctions/watchlists, then the entire compliance program is at risk!

Which means missed red flags, failing to report suspicious activity, or even letting illicit transactions through.

Billions of cross-border transactions occur daily, yet only a small number go through active sanctions screening. Institutions are struggling with high rates of false positives in their screening processes, and increasing geopolitical tensions involving Russia, Iran, and China are raising the alert levels.

However, the low percentage of actual matches indicates that much of the effort spent on screening is wasted.

Financial institutions are adapting, whether through artificial intelligence or machine learning-powered screening engines, which is quite interesting.

As the race to modernize systems accelerates, a quieter and often more critical issue continues to sabotage progress: this is the poor data quality.

This blog will explore how sanctions screening and data quality have become a frontline defense mechanism against risks posed by financial crime.

Why Data Quality in Name Screening Is Important?

OFAC imposed approximately USD 1.5 billion in sanctions-related penalties during calendar year 2023.

This shows that even a single violation can translate into hundreds of millions of dollars in fines. With such hefty penalties, financial institutions and corporations are now under greater pressure to avoid inadvertent infractions.

FIs are therefore better able to identify and stop illegal activity when they have accurate and trustworthy data.

Therefore, they must have access to accurate, fast, and updated data in order to guarantee proper screening, prevent false negatives, and avoid the fines that follow.

Data Integrity in Sanctions Screening is a Legal Obligation

The days of “best effort” compliance are over. Regulatory bodies now mandate data integrity.

Data integrity in sanctions screening systems is a legal compliance obligation, not just a technical best practice. Institutions must design, document, test, and certify that their systems handle data accurately and completely.

Under NY DFS Part 504, institutions must:

• Validate all filtering system inputs
• Certify system effectiveness annually at the executive level
• Document logic, dependencies, and conjectures

This regulation is clear: data integrity is not optional. It’s legally binding.

Furthermore, screening systems behave like risk models: a point stressed by model governance guidelines such as U.S. SR 11-7.

Yet, many institutions fail to treat them as such, missing the opportunity to apply model validation, backtesting, and threshold regulation to performance

Why Does Accurate Data Matter in Sanctions Compliance

AML programs are driven by data. High-quality data is the backbone of effective AML compliance, yet many organizations struggle to maintain it. Accurate data helps financial institutions in the following ways:

• Helps identify high-risk customers.
• Lowers false negatives and false positives.
• Prevents terrorist financing.
• Secures the firm’s financial stability & integrity.

Three Key Pressure Points Where Sanctions Screening Often Fails

To ensure to accuracy of data, FIs must evaluate sanctions screening tools on multiple parameters, which include how accurately they match the name despite different naming conventions, nomenclatures, and cultural differences.

Sanctions compliance usually begins at the source: customer onboarding, transaction initiation, and third-party integrations. These are the first touchpoints for data and where breakdowns often begin.

If an individual’s name is entered as “M. Al-Hassan” in one system and “Mohamad Elhassan” in another, the screening engine might miss a hit, especially in languages with complex transliteration like Arabic or Cyrillic.

Core Strategies for Accurate Sanctions Screening Data

• Enforce validation code and mandatory data fields
• Standardize formats across systems and geographies
• Align KYC, screening, and onboarding logic
• Use updated sanction data

Fragmented data directly leads to fragmented compliance. AML only works when all data pieces fit together, because fragmentation creates blind spots that criminals exploit.

Choosing the Right RegTech in Sanctions Risk Assessment

Quality is not just about volume. Businesses with high exposure to Russia Sanctions risk need to ensure

• Verified identifiers
• Structured name formats
• Up-to-date nationality fields
• Comprehensive integration with authorized external databases, internal watchlists, and PEP registries.

This multi-layered approach enhances match precision while reducing false alerts.

The situational analysis or context brings new profoundness to screening decisions, which allows systems to focus on risk more intelligently.

The move toward layered compliance strategies, using enriched PEP data and contextual insights, is becoming a classic example among institutions embracing next-gen RegTech.

TD Bank faced a $3 Billion Fine, and Starling Bank was fined £29 million, Both Due to Poor Data Quality.

The global RegTech market is projected to reach $60.77 billion by 2030, primarily fueled by AI and machine learning solutions that aim to reduce manual reviews and false positives.

Recent surveys indicate that institutions believe AI and machine learning will substantially remodel the automation of sanctions compliance.

In RegTech, AI must be used strategically as regulations require precision and accuracy.

Therefore, it is necessary for AI logic and reasoning to be based on requirements defined by the regulations. In Sanctions Screening, while AI can help in reducing false positives, it is only effective if the Data it is fed is accurate and updated timely.

But technology is not always a cure, particularly when it doesn’t incorporate data in line with regulatory requirements.

TD Bank’s $3 billion fine in 2024 burst the perception that even major banks with automated procedures can fail catastrophically if entries are incorrect, alerts are overlooked, or internal warnings are disregarded.

Similarly, Starling Bank was fined £29 million for screening only a fraction of the UK sanctions lists because of a defective automation rule. In both cases, technology was in place, but data and governance were not.

Governance Is Not Simply for Compliance Teams

Data quality is an enterprise-wide concern. Organizations that succeed assign:

• Distinct data proprietary
• Formal control frameworks
• Defined escalation courses for data anomalies

Institutions that assign data ownership, outline clear growth options, and set up a loop of continuous feedback are not just more compliant; they are more agile, proactive, and Audit-ready.

How AML Watcher’s Data Empowers Sanction Screening?

AML Watcher can successfully improve compliance productivity and lower noise in sanctions screening with the following features..

– Comprehensive Global Sanctions Coverage

Integration of over 215 international and administrative sanctions regimes, including OFAC, UN, EU, DFAT, and country-specific lists such as Japan, Canada, and Australia.

-Intelligent Risk Detection Features

Advanced features include alerts for secondary sanctions risk, tagging for sectoral sanctions, and context-sensitive labeling, which massively reduces compliance review times and elevates response precision.

– Flexible Screening Capabilities

Access for organizations to perform automated, periodic, and retroactive screenings of individuals, entities, vessels, aircraft, and even crypto wallets against up-to-date sanctions data.

– Proactive Customer Risk Monitoring

Support for daily customer-level screenings (as opposed to transactional checks), which strengthens precision in risk detection.