Financial data often lacks explicit country information, creating challenges in assessing cross-border risks. This issue becomes more complex when information from different sources varies or contradicts. To address this problem, we need a structured approach to extract and standardize country-related data effectively. I outlined key strategies to tackle this challenge.

Key Considerations in Country Information Derivation

1. Identifying Trends and Data Formats
   Financial transactions contain information from various sources, such as the originator, sender, intermediary, and beneficiary. Depending on the transaction type—such as SEPA, SWIFT, or local payment systems—the fields and formats used to capture this information can vary significantly. As a result, retrieving country information requires tailored methods for each format. Recognizing and adapting to these variations is essential for accurate country derivation.
   
2. Two-Layered Approach
   To address the diversity of data formats and ensure reliable results, a combination of rule-based and automated methodologies is recommended:
   • Rule-Based Techniques
     • Pattern Matching and Regular Expressions: By analyzing transaction data, we can often extract country information using regex or string-matching techniques. For example, postal codes, city names, country codes, and identifiers like BIC/IBAN numbers can serve as reliable indicators of a country. These patterns provide a foundational layer for structured data extraction.
     • Automated Approaches
       • Geocoding Services: External geocoding services, such as Google Geocoding API, QGIS, or Azure Maps Geocoding Services, can map partial address elements to specific countries. These services enhance accuracy by leveraging large datasets and established algorithms.
       • Natural Language Processing (NLP): Advanced NLP models, such as SpaCy or other pre-trained tools, can identify geographic entities within unstructured text. These models are particularly effective for processing addresses written in natural language.
       • External Databases and Fuzzy Mapping: Maintaining external databases that map partial elements (e.g., city names, provinces, non-standard ISO country codes) to their corresponding countries is crucial. Fuzzy mapping techniques can further align incomplete or ambiguous data with the most probable country match.
       • Large Language Model (LLM): The currently popular large language model can also tackle this task by using simple prompt such as ‘Normalize the given address and provide the country’
         
3. Integration and Validation
   No single solution is sufficient for comprehensive country derivation. Instead, these methods are often used in combination to produce ensemble models. For instance, results from rule-based methods, geocoding, and NLP can be weighted based on reliability or combined to highlight high-risk countries in Anti-Money Laundering (AML) scenarios.
   
4. Addressing Multilingual Challenges
   Another challenge in international banking is that data is not always presented in English or even the local language of the transaction’s origin. Models must be fine-tuned and tested across different languages to ensure validity. This includes adapting algorithms to handle varying linguistic structures and ensuring coverage for non-standardized formats.

Deriving country information from financial data demands a precise blend of rule-based and automated techniques. This approach is vital in AML, where even low-probability detections with high risks must be considered. By integrating robust methods and adapting to diverse formats and languages, organizations can ensure accurate country identification and effective cross-border risk management.

Written by: Juiyun HSU