Unbiased Temporal Machine for General-purpose Times series-based Fraud detection

SCHEME: Industrial Fellowships

CALL: 2017

DOMAIN: IS - Information and Communication Technologies

FIRST NAME: Eric Aislan

LAST NAME: Antonelo

INDUSTRY PARTNERSHIP / PPP: Yes

INDUSTRY / PPP PARTNER: CHOICE Technologies Holding Sarl

HOST INSTITUTION: University of Luxembourg

KEYWORDS: fraud detection, reservoir computing, covariate shift, unsupervised learning, sample selection bias

START: 2017-03-01

END: 2019-02-28

WEBSITE: https://www.uni.lu

Submitted Abstract

In the context of energy distribution networks, frauds are non-technical losses (NTL) that may account for up to 40% of the total distributed energy in some developing countries. The fraudster alters the eletricity meter in order to pay less than the right amount. In this context, the discovery or detection of frauds is necessary in order to decrease the non-technical losses of the energy distribution networks, consequently enhancing the stability and reliability of the network.This project proposes the use of Recurrent Neural Networks (RNNs) for projecting a times series into a spatial dimension such that it can be used as a universal temporal feature for fraud detection predictive models. The particular problem tackled here jointly with the partner company is to predict whether a given time series of monthly energy consumption data is likely to indicate a fraud (NTL) or not.Two main approaches are planned to be used with RNNs: supervised learning with bias correction techniques, and self-organized models for unsupervised learning of new fraud (anomaly) patterns.Finally, a last step is to integrate both of the previously developed models into an unified architecture that learns the responsibilities of each model in an online way by feedback from the environment using the results of the inspections of the fraudsters – the ground truth for some of the predictions.This project has potential not only for generating significant technological and commercial value for the industrial partner, but also outstanding scientific output, being applicable in the long-term to other fields such as monitoring, prognosis/diagnostics in robotics, medical systems and security applications.

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