Abstract
Transactional data be most economical concern in banking sectors for financial prospects. Due to criminal activities and tolerant they had many issues specifically in money laundering. The illegal transaction, robbery, malpractice non related source of access, are tedious to identifying legal transactions. By the higher transactions using the big data analysis to identify possible money laundering activities. To resolve the issue to make effective future selection approach in transactions and classify the resultant under data cleaning, partial information from traditional statistical analysis, and data mining process. To propose an collaborative relational data screening model (CRDSCM) using decision classifier in transactional database. Autocorrelation function with screening cluster approach were conducted to examine the relationship between the attribute similarities on each transaction collaborative data analysis method. With support of time serious data including predictive analytics for decision making the detection of attitude and money laundering activities that are used to deliver classified result in efficient way
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