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Explaining and Predicting Abnormal Expenses at Large Scale Using Knowledge Graph Based Reasoning

20 Pages Posted: 2 Jul 2018 First Look: Accepted

See all articles by Freddy Lecue

Freddy Lecue

Université de Nice Sophia Antipolis - INRIA - Institut National de Recherche en Informatique et Automatique

Jiewen Wu

Accenture Labs

Abstract

Global business travel spend topped record-breaking $1.2 Trillion USD in 2015, and will reach $1.6 Trillion by 2020 according to the Global Business Travel Association, the world’s premier business travel and meetings trade organization. Existing expenses systems are designed for reporting expenses, their type and amount over pre-defined views such as time period, service or employee group. However such systems do not aim at systematically detecting abnormal expenses, and more importantly explaining their causes. Therefore deriving any actionable insight for optimizing spending and saving from their analysis is time-consuming, cumbersome and often impossible. Towards this challenge we present AIFS, a system designed for expenses business owner and auditors. Our system is manipulating and combining semantic web and machine learning technologies for (i) identifying, (ii) explaining and (iii) predicting abnormal expenses claim by employees of large organisations. Our prototype of semantics-aware employee expenses analytics and reasoning, experimented with 191; 346 unique Accenture employees in 2015, has demonstrated scalability and accuracy for the tasks of explaining and predicting abnormal expenses.

Keywords: Semantic Web, Reasoning System, Intelligent System, Smart Expenses, Intelligent Operation System, Knowledge Graph

Suggested Citation

Lecue, Freddy and Wu, Jiewen, Explaining and Predicting Abnormal Expenses at Large Scale Using Knowledge Graph Based Reasoning (2017). Journal of Web Semantics First Look 44_0_5. Available at SSRN: https://ssrn.com/abstract=3199302 or http://dx.doi.org/10.2139/ssrn.3199302

Freddy Lecue (Contact Author)

Université de Nice Sophia Antipolis - INRIA - Institut National de Recherche en Informatique et Automatique

250, rue Albert Einstein
Sophia Antipolis
France

Jiewen Wu

Accenture Labs

Dublin
Ireland

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