Advanced techniques in AML compliance are revolutionizing the way financial institutions detect, monitor, and respond to suspicious activities, enhancing accuracy and reducing false positives.
To implement a risk-based approach to AML compliance, including know your customer (KYC), institutions are increasingly seeking to understand the customer’s professional, institutional, political, and social context by analysing large amounts of external data, including information and media, public archives, social networks, and other open-source data sources.
In complex or Big Data environments, institutions may use special languages, tools, and a combination of techniques to develop and implement efficient and improved versions of matching and screening compliance processes.
Advanced Techniques in AML Compliance
Fuzzy logic can easily be applied from manual coding scripts that are available in various programming languages and applications, which include:
Python: Python libraries can be used to run string matching in an intuitive way. Using the Python Toolkit of Record Linkage, users or compliance specialists can run several indexing techniques such as sorted neighbourhood, blocking, and identification of duplicates using python.
Java: Use of Java includes several string similarity algorithms, like the java-string-similarity package which consists of algorithms. Such algorithms include Levenshtein, Jaccard Index, and Jaro-Wrinkler. Alternatively, the Python algorithm FuzzyWuzzy can be utilized within Java to run matches.
Excel: Excel can be used in performing matching and screening. The Fuzzy Look-up add-in capabilities can be utilized to run fuzzy matching between available datasets. The add-in has a simple interface, like the option to select the output columns, number of matches, similarity threshold, etc. The functionality may also give high false positives as it may not properly identify duplicates, such as ‘ATT CORP’ and ‘AT&T Inc.’.
Institutions may combine different techniques to obtain relevant compliance results and solutions, and improve the overall screening process. Combining fuzzy matching techniques helps in performing more accurate searches and screening, and provides solutions to Big Data, including more complex data sets, and data fields.
Combining Levenshtein Distance and Hamming Distance techniques may help in measuring two strings, with the given number representing how far the two strings are from being an exact match, and determining the binary code assigned to each letter in each string to calculate the distance score.
Combining Damerau-Levenshtein and Metaphone techniques 3 may help in finding the minimum number of operations that are needed to make two strings a direct match, and converting any string into an encoding depending on the outputs and sounds present in an all-alphabet code.
Combining Name Variant methods such as common key method and list methods may help in improving the overall name matching process, which may solve different name matching challenges. Such methods reduce names to a key or code based on their English pronunciation, such that similar sounding names share the same key and list all possible spelling variations of each name component to look for matching names from the available name variation list. For example, the name John may have different name lists to be used, including John, Jon, Joan, etc.
Final Thoughts
In the evolving landscape of AML compliance, institutions are leveraging expansive external datasets to gain holistic insights into a customer’s multifaceted identity, spanning professional to social contexts. Within these big data environments, the complexity necessitates specialized tools and languages to enhance matching and screening processes. Fuzzy logic emerges as a pivotal tool, with its manual coding scripts accessible across platforms like Python, Java, and even Excel. However, the true power lies in the amalgamation of various techniques.
By intertwining methods like Levenshtein Distance with Hamming Distance or Damerau-Levenshtein with Metaphone techniques, institutions can achieve a higher accuracy in data matches. Additionally, the integration of Name Variant methods, which consider phonetic similarities and spelling variations, fortifies the screening process. As institutions strive for optimal AML compliance, these combined approaches promise enhanced precision and reduced ambiguities in data interpretation.
