Visible results.White paper How to use data mining to reduce risk, error, co...
Visible results. How to use data mining to reduce risk, error, complexity an...
Visible results.Overview of this white paperSupplier behaviour and risks associated with the supply base• uppliers with h...
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Preview White Paper | ‘How to use data mining to reduce risk, error, complexity and cost in Accounts Payable’

This white paper explains how data mining can be used to identify anomaliesand risks in Accounts Payable (AP). Topics are illustrated with the resultsof a benchmark study of AP data delivered by 250 companies worldwide,representinga consolidated spend amount exceeding 1150 billion euro.
Published on: Mar 4, 2016
Source: www.slideshare.net


Transcripts - Preview White Paper | ‘How to use data mining to reduce risk, error, complexity and cost in Accounts Payable’

  • 1. Visible results.White paper How to use data mining to reduce risk, error, complexity and cost in Accounts PayableThis white paper explains the most commonly observed anomalies and risks in Accounts Payable(AP), deduced from historical AP data. It highlights the value of AP data from multiple perspectives. ‘How to use data mining to reduce risk, error, complexity and cost in Accounts Payable’ | 01
  • 2. Visible results. How to use data mining to reduce risk, error, complexity and cost in Accounts PayableThis white paper explains how data mining can be used to identify ano­malies and risks in Accounts Payable (AP). Topics are illustrated with the resultsof a benchmark study of AP data delivered by 250 companies worldwide,r­epresenting a consolidated spend amount exceeding 1150 billion euro.IntroductionIn today’s volatile business environment, Fi- • istorical and current AP process analysis Hnance strives to have an agile and responsive • ayment terms; DPO Pfinance function in order to support the business • xposure to fraud Ein making the smartest and fastest decisions. • xtent of contamination of the vendor master file ERecent research by Open University Amsterdami • upplier behaviour and risks associated with Sshows that agile organizations: the supply base• xercise control over a limited number of KPIs; e The ERP systems of organizations contain huge• mplement rolling forecasting; i amounts of detailed information that has no• se trend information and relative performance u special meaning in its bulk state, but that holds indicators; trends and important facts – in the above-• alue knowledge sharing, learning and v mentioned areas – that can be discovered using collaboration within the finance organization. data-mining techniques.From this perspective, finance professionals want Based on hard, objective data this white paperto review and improve their performance based explains the most common anomalies and riskson findings and trends that are distilled from in AP. The study is illustrated with results from ahard, objective data. According to research by benchmark study of 250 randomly selectedthe Aberdeen Group (2009)ii , current challenges companies with a total of more than 1150 billionthat CFOs are facing in improving financial euro in financial data in various industries andperformance are: countries worldwide.reducing costs (73%), optimizing working capital(70%), forecasting financial performance (53%), Some of the applied data-mining techniquesand reducing anomalies (43%). are straightforward, while others are more complex. In this white paper we do notFinancials therefore have an inherent interest elaborate on the specifics of the techniques,in management information in the following but instead focus on showing what value theyareas: can deliver. In the near future we will issue a series of white papers that will cover more in- depth information. ‘How to use data mining to reduce risk, error, complexity and cost in Accounts Payable’ | 02
  • 3. Visible results.Overview of this white paperSupplier behaviour and risks associated with the supply base• uppliers with high numbers of small invoices (below €200) S• uppliers with the same (i.e., duplicate) bank account number S• nactive suppliers I• ayment term analysis P• pend analysis S• isk associated with the supply base RAccounts Payable process issues• ayment errors P• redit issues C• raud FAbout the authorFounded in 2000, Transparent is an international financial-services provider specializing in datamining of Accounts Payable (AP). The company has rapidly grown into a global organization, withoffices in the Netherlands, Germany, Belgium, India, France, the United Kingdom, Italy and theUnited States.Our services include the analysis of outgoing payments and associated processes with the aim toconvert AP data into detailed management information. Alongside the analysis, we identify, verifyand collect undue payments on a no-recovery, no-fee basis. The results are presented in an easy-to-read dashboard (SaaS) and used by Transparent to provide clients with management informationand advice regarding their AP processes.CFOs of blue-chip and medium-sized companies around the globe rely on Transparent to­provide them with enterprise-level transparency of their AP processes and with sensiblei­mprovement recommendations. Moreover, many of our clients have seen an immediate profitincrease after using our contingency-based recovery services. The analysis is solid, fast, risk-free, and doesn’t consume our clients’ resources, due to our excellent use of technology andfully industrialized process. For more information about this white paper, our services and/or other inquiries, please contact us: 0031 (0) 20 468 4648 | E sales@transparent.eu | W www.transparent.eu ‘How to use data mining to reduce risk, error, complexity and cost in Accounts Payable’ | 03

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