Real Time Fraud Detection System for Mobile Banking: Based on Experiential Paradigm
Abstract
The current banking industry is characterized by hyper-competition driven by technological
innovations that revolve around provision of ubiquitous access to banking services especially
through mobile banking. Proliferation of mobile phones in Kenya acts as a substrate for the
increased adoption of mobile banking in Kenya. Frauds perpetrated through mobile banking
platforms have become prevalent eroding the hard-earned profits by banks. This research therefore
was aimed at developing a case-based reasoning framework that would do real time fraud detection
in mobile banking. Case-based reasoning problem solving technique which makes use of prior
knowledge and specific problem scenarios (cases) to solve new problems by identifying similar
past problem episodes and applying them to the new problem situations. The research employed
an incremental prototyping model in which the overall architectural design was done upfront but
the detailed design and developments of the subcomponents were done in incremental manner.
The research used a four-step approach for building the Case Based Reasoning engine which
included features calibration, case stabilization, and implementation and finally the evaluation
process. The research relied on both primary and secondary data to collect the past fraud incidences
to build a reference case library. The research design was in form of interviews done to the target
population comprising of individuals drawn from the bank’s risk, forensics, digital channels
support and information systems security. The Case Based Reasoning algorithm implemented
incorporated a threshold retrieval mechanism combined with K-Nearest Neighbor algorithm. The
system prototype was built and trained using a data set of 120 transactions with system evaluation
done in three iterations of 40 transactions in every iteration revealing an average classification
accuracy of 84.17%.
Publisher
University of Nairobi
Subject
Real Time Fraud Detection SystemRights
Attribution-NonCommercial-NoDerivs 3.0 United StatesUsage Rights
http://creativecommons.org/licenses/by-nc-nd/3.0/us/Collections
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