Record linkage using fuzzy sets for detecting suspicious financial ransactions

Identifying suspicious financial transactions and linking relevant records is an important data related problem. An appropriate identification may improve fraud detection and international security. The main problems in this linking process are the missing data, errors in the entries or out of date entries. This study aims at developing an efficient method for identifying the suspicious transactions. This proposed methodology compares the information provided in the financial transactions with the black lists, and links similar entries. Different distance measures, including extensions of fuzzy sets can be used with the proposed method.

[1]  Zeshui Xu,et al.  Hesitant fuzzy multi-attribute decision making based on TOPSIS with incomplete weight information , 2013, Knowl. Based Syst..

[2]  Peter Christen,et al.  A taxonomy of privacy-preserving record linkage techniques , 2013, Inf. Syst..

[3]  V. Torra,et al.  A framework for linguistic logic programming , 2010 .

[4]  Zeshui Xu,et al.  Distance and similarity measures for hesitant fuzzy sets , 2011, Inf. Sci..

[5]  Matthew A. Jaro,et al.  Advances in Record-Linkage Methodology as Applied to Matching the 1985 Census of Tampa, Florida , 1989 .

[6]  Vladimir I. Levenshtein,et al.  Binary codes capable of correcting deletions, insertions, and reversals , 1965 .

[7]  Krassimir T. Atanassov,et al.  Intuitionistic fuzzy sets , 1986 .

[8]  Francisco Herrera,et al.  Hesitant Fuzzy Sets: State of the Art and Future Directions , 2014, Int. J. Intell. Syst..

[9]  G. Wei,et al.  Extension of VIKOR method for decision making problem based on hesitant fuzzy set , 2013 .

[10]  Cengiz Kahraman,et al.  Strategic Decision Selection Using Hesitant fuzzy TOPSIS and Interval Type-2 Fuzzy AHP: A case study , 2014, Int. J. Comput. Intell. Syst..

[11]  Murat Kantarcioglu,et al.  Quantifying the correctness, computational complexity, and security of privacy-preserving string comparators for record linkage , 2012, Inf. Fusion.

[12]  Xiaoyi Wang,et al.  Multiple valued logic approach for matching patient records in multiple databases , 2012, J. Biomed. Informatics.

[13]  Vicenç Torra,et al.  On hesitant fuzzy sets and decision , 2009, 2009 IEEE International Conference on Fuzzy Systems.

[14]  António Couto,et al.  A Conceptual Algorithm to Link Police and Hospital Records Based on Occurrence of Values , 2014 .

[15]  Humberto Bustince,et al.  Using the Choquet Integral in the Fuzzy Reasoning Method of Fuzzy Rule-Based Classification Systems , 2013, Axioms.

[16]  Cengiz Kahraman,et al.  Multi-criteria evaluation of alternative-fuel vehicles via a hierarchical hesitant fuzzy linguistic model , 2015, Expert Syst. Appl..

[17]  Guillermo Navarro-Arribas,et al.  Improving record linkage with supervised learning for disclosure risk assessment , 2012, Inf. Fusion.

[18]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .