The advanced matching algorithms, utilizing phonetic key methods such as Soundex, Metaphone, and Double Metaphone, have revolutionized the efficiency and accuracy of sanctions screening in financial compliance.
Sanctions compliance requires identification of customer or transaction relevant key profile matching factors or customer credentials, such as full and exact name matching, date of birth, nationality, jurisdiction, etc. Using artificial intelligence (AI) and machine learning (ML) enabled algorithms requires input of relevant and correct identification or matching data, to perform deep and relevant sanctions screening.
In the past, fines have been imposed on financial institutions due to poor sanctions searches and screening processes, which caused transactions with sanctioned countries.
Some of the challenges in sanctions compliance include typos, incomplete data strings, use of nicknames, spelling differences, etc. The fuzzy algorithms may help with these broader data or input challenges. Maintenance and use of correct data input factors for sanctions compliance generate fewer false positives, and positively affect the overall sanctions compliance of the institution.
The use of AI and ML linked advanced name algorithms helps in using correct data input, and avoid inadequate name screening to perform name related sanctions searches from the regulatory databases and sanction lists.
It ultimately increases the efficiency of anti-money laundering (AML) measures and enables identification of true matches timely. Use of algorithms and metadata to perform sanction screening, captures relevant and near complete screening information, such as name, address, jurisdiction, phone number, identification number, or other customer identification data points.
Inadequate name searches and screening processes can be detrimental, resulting in fines, reputational losses, and loss of customers. The use of AI and ML capabilities enables sanctioned individual detection and lowers the risks of identifying a customer who is not sanctioned, leading to the avoidance of poor customer experience and business losses.
The Advanced Matching Algorithms
Advanced matching algorithms may solve sanction search inaccuracies, such as the watchlists and international sanctions that may contain names belonging to Russians, or other nationalities that do not use the Latin alphabet, which can lead to name search inaccuracies. Such inaccuracies result in false positives and an investment of time. The problem becomes more complex with big data, hundreds of millions of names and large comparison scenarios, which can be managed through advanced matching and predictive algorithms.
The advanced matching algorithms use a common key method that reduces customer or persons’ names to a key based on English pronunciation. The Soundex key method enables sharing a single key, like the C21 key, for names that sound similar such as Candy, Condie, etc.
The other key methods like Metaphone and Double Metaphone, use advanced algorithms to convert names with similar sounds into the same key, which improves sanctions screening and name matching. Such key methods use a wider range of rules related to pronunciation.
The Double Metaphone introduces codes including a primary and secondary code for each customer name. This method includes pronunciation from different languages, including English, Slavic, Celtic, Spanish, French, German, and Chinese. For example, the Metaphone codes the name “David” with a primary code of DA0 and a secondary code of XMT. Schmidt is tagged with the primary code of DA0 and a secondary SMT code, which indicates a degree of similarity for sanction searches and screening.
Sanctions compliance necessitates accurate identification of key customer profiles and transaction credentials, with common hurdles being typos, nicknames, and spelling variances. The integration of AI and ML with advanced algorithms like Soundex, Metaphone, and Double Metaphone aids in overcoming such discrepancies by focusing on phonetic similarities across multiple languages. This ensures enhanced sanctions screening from regulatory databases, diminishing false positives and consequently boosting the efficacy of anti-money laundering initiatives.
The accuracy brought by these technologies not only curtails the potential for financial and reputational damage but also optimizes customer experience, handling intricate scenarios especially prevalent in big data.