Fuzzy Matching is a method that helps detect different text or strings that are approximately similar. An approximate String-Matching search is a process utilizing a fuzzy matching program, which returns a list of results based on likely relevance even though search argument words and spellings may not match. Exact and highly relevant matches appear near the top of the list. Subjective relevance ratings, usually as percentages, may be given. This study tries to facilitate the Anti-Money Laundering and Counter Financing of Terrorism (AML/CFT) to identify caution type lists and detect suspicious customers who have duplicate Names and match the name using Fuzzy matching algorithms. The general objective of this research is to experiment with the existing algorithm and propose the best one that can identify and detect Suspicious customers on AML Hence to determine the result of the customer, 421, 2010 federal government, 109, 2010 public enterprises, and 1049, 2011 government, residents from each appointees’ officials were chosen to complete a survey. Two algorithms (Levenstein Distance and N-gram string matching) were implemented to predict the best algorithm. Results of the N-gram search similar names based on the distance between each character and Levenstein Distance match not only the similar name but also recommends the names with the potential to have similarities.
THE DETECTION OF SUSPICIOUS CUSTOMERS ALGORITHM ON ANTI-MONEY LAUNDERING (AML) IN NIGERIAN BANKS, GET MORE COMPUTER SCIENCE PROJECT TOPICS AND MATERIALS
