The impact on the development of the national economy is mostly dependent on the flow of money in the country. The suspicious transactions of money within financial institutions affect the national economic system and compromise the impression of country’s reputation. Identifying an individual suspicious transaction and individual suspicious customers within transactions are evident by using basic statistical rules. Other than such traditional rules, this paper proposes a model that discusses the identifications of association and relationships within transactions and customers using Social Networks Analysis (SNA). With the help of this model, groups and gang mafia can be identified who mainly play a vibrant role in money laundering activities.