Mining Big Data in statistical systems of the monetary financial institutions (MFIs)

The financial crisis prompted a number of statutory and supervisory
initiatives that require great disclosure by financial firms of their data to a
central system. Recently, core banking and payment systems data as a main
big data sources of monetary financial Institutions (MFI’s) have been used to
monitor different kind of risks, however distress situations for MFI’s are
relatively infrequent events and as the same time under the pressure of rapid
changes in compliance and rules. The very limited information for
distinguishing dynamic fraud from genuine customer or monetary and
financial institution behavior in an extremely sparse and imbalanced big data
environment with probable change points in data distribution is making the
instant and effective fraud detection and banking Big Data management more
and more difficult and challenging. Being still a recent discipline, few
research has been conducted on imbalanced classification for Big Data. The
reasons behind this are mainly the difficulties in adapting standard
techniques to the MapReduce programming style and inner problems of
imbalanced data, namely lack of data, small disjuncts and data distribution
changes. These are accentuated during the data partitioning to fit the
MapReduce programming style and data mining process. This paper is going
to summarize some technical problems of imbalanced data and artificial data
for the adjustment of big data for MFI’s and to investigate how it can be
made ready for implementation