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Trends and best practices in detecting and preventing global procurement fraud

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Published on : Jul 19, 2019

Procurement fraud really is a crime of opportunity and a widespread epidemic, If you get the right scenario, where you have an organization that is trying to win the business, and a procurement official who sees an opportunity for personal gain, there’s the perfect storm.

Traditional procurement fraud detection methods – manual searches; spreadsheets; and simple slice, dice, filtering and reporting from silo systems – are not keeping pace. Whatever tools and processes are in place now, there’s always the opportunity to evolve to a higher level for more accurate detection.

Each best practice and analytical technique is designed to take a different look at what’s happening in procurement:
• An accurate, up-to-date and inclusive master vendor list provides clarity into  supplier relationships.
• Due diligence at onboarding helps ensure that only qualified suppliers receive contracts.
• Peer grouping and debarment lists help you identify suppliers who should not be considered.
• Business rules screen for obvious concerns, such as employees who share a bank account with a vendor.
• Anomaly detection identifies out-of-bounds activity, such as excessive transactions for an entity.
• Predictive analysis finds patterns that are highly indicative of fraud.
• Link analysis finds potential collusion by finding relationships among entities.

Integrate multiple data sources to gain a 360-degree view of procurement activity and players. Apply a hybrid of analytical methods to find potential irregularities sooner and with greater accuracy. Know where to focus investigative resources for the biggest return. That’s the path to preserving procurement integrity and safeguarding everyone in the organization.

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    My organisation is looking for a Data Quality & Integration solution that will help us:
    Address Data Quality issue and turn information in to mission critical intelligence for strategic business decisionsFetch departmental data from various 'source' systems to create a single version of truth

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    Unable to tap into big data to surface unexpected insights for effective decision making`Lacking the ability to interact with data in a graphically driven, intuitive manner to identify new relationshipsUnable to interact with information on mobile devices for offline decision making


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