PROCEEDINGS IPMU '08
Optimizing a Fraud Detection Process
Nuno Homem, Joao Paulo Carvalho
Fraud in telecommunications services or
financial transactions is a major problem
as it impacts from 1% to 3% of the
revenues. This is in most cases a customer
specific behavior that companies need to
detect in order to minimize it. Detecting
specific types of behavior as soon as it
happens is critical, and to that purpose
companies deploy sophisticated detection
systems. The biggest challenge to fraud
detection systems is accurately predict in
near real time that a customer is a
fraudster or that is service is being used in
fraudulent way. As this may happen to
any customer at any time it is mandatory
to monitor the entire customer base –
sometimes several million customers
making several transactions per day.
Optimizing the detection process is
therefore critical. To model and analyze
the problem Finite State Automata and
Markov Chains were used. To solve the
optimization problem Dynamic
Programming and Stochastic Hill Climber
algorithms were chosen.
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