FDSeminar Reporting & controlling
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The document discusses the evolution of data-enabled auditing, highlighting the significance of big data in enhancing risk assessment and decision support. It outlines the advantages of data-driven process analysis, such as improved understanding of operations, identification of inefficiencies, and the ability to conduct full population testing. The future of auditing is presented as being heavily influenced by AI and machine learning, leading to more objective and comprehensive analyses of financial transactions.
FDSeminar Reporting & controlling
- 1. Data-enabled audit www.pwc.be Didier Delanoye Director Financial Assurance Services PwC Jasper Kerremans Manager Risk Assurance Services PwC
- 2. Agenda Changing context and developments1 Data-enabled audit2 Data-enabled process analysis3 What’s next4 2
- 3. Access to data has radically changed Big Data = Transactions + Interactions + Observations Text • Purchase detail • Purchase record • Payment record • Segmentation • Offer details • Customer Touches • Support Contacts • Web logs • Offer history • A/B testing • Dynamic Pricing • Affiliate Networks • Search Marketing • Behavioral Targeting • Dynamic Funnels • Sensors/RFID/Devices • User Click Stream • Mobile Web • User Generated Content • Social Interactions & Feeds • Spatial & GPS Coordinates • External Demographics • Business Data Feeds • HD Video, Audio, Images • Speech to Text • Product/Service Logs • SMS/MMS Big Data Web CRM ERP Increasing Data Variety and Complexity Petabytes Terabytes Gigabytes Megabytes Doing things differently Doing different things 3
- 4. Business Value increases together with Complexity Descriptive What happened? Diagnostic Why did it happen? Predictive What will happen? Prescriptive What should I do? Decision Support Decision Automation Decision ActionData Analytics Human Input Feedback 4
- 5. Data-enabled audit Is someone outside of the finance department posting journals? Big data Small data 5
- 6. How can we reduce or remove the need to perform substantive tests of detail? Data-enabled audit of Revenue How can we identify higher risk transactions to focus our detailed testing? Big data Small data 6
- 7. Data-enabled audit How does seasonality of revenue impact profit? Are the drivers of profit changing over time? Big data Small data 7
- 8. Is there a three- way match proces in operation for all invoices? Data-enabled audit Where was segregation of duties breached? Big data Small data 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 -1,23 19,84 0,00 -7,77 Purchase Document and Vendor Good Receipt and Vendor Invoice Receipt and Vendor Clearing and Vendor Purchase Requisition Purchase Requistion and Purchase Document Purchase Document Purchase Document and Goods Receipt Purchase Document and Invoice Receipt Goods Receipt and Invoice Receipt Invoice Receipt Invoice Receipt and Clearing 0,0 0,5 1,0 1,5 2,0 2,5 3,0 3,5 -10,0 -5,0 0,0 5,0 10,0 15,0 20,0 25,0 Line Items AmountLine Items (x1000) Amount (x1Mio) 8
- 9. Approach to process analysis Traditional Elementary Process Description System: Order Management System Sub-system Author: Date: Proc ID Process Name Description 2.1 Take enquiry details Enquiries are received by telephone, post, fax of e-mail. The details of the enquiry will be entered on an enquiry form, which is then passed to the quotations clerk. 2.2 Prepare quotation The quotations clerk will prepare a quotation using the standard procedures. If the customer has requested an urgent quotation, brief details will be given to the customer by telephone, email or fax. A standard quotation document, including a copy of th company’s terms and conditions of trading, will in all cases be sent to the customer by post or fax. 2.3 Update quotation file This quotation and all related documentation will be files in the quotation file. Brief details of the enquiry, includinf the quotation numer, will be entered in the quotation register. Big data Small data Data-enabled: Process mining 9
- 10. Data-enabled approach to process analysis Activity-related data extracted from IT system Data including information about: • Behaviour: activity executions, document creations, approvals • User executing behaviour • Time of behaviour execution 1 Reconstruction of links among activities 2 Visualisation and detailed analysis of the process 3 10
- 11. Advantages of data-enabled approach Allowing for high quality audits • Detailed understanding of end-to-end processes based on factual data. • Identification of cases deviating from the regular flow for a better risk assessment. • Full population testing for all key control activities in the process. • Identification of manual controls executed in less than a minute. • Drill-down capabilities and full details available for easier follow-up. Providing business insights • Measurement of process standardisation level. • Identification of redundant activities and duplicate transactions. • Identification and root cause analyses for long-lasting activities and transactions. • Identification of potential inefficiencies in the process (e.g. transactions stopped late in the process, duplicate effort). • Multiple view angles on the process – users, work load, lead times, transaction types, automation, etc. Initial investment is balanced by low effort for performing future analyses (re-runs) Can be applied to any digital process, independent of systems and activities involved 11
- 12. Outliers are easily isolated for further investigation 8% outliers 12
- 13. We compare and benchmark entities, countries, teams,… Entity CEntity BEntity A 13
- 14. 14 (Censored) (Censored) We visualise data and improvement opportunities (Censored) (Censored) Big data Small data
- 15. Teamwork and Engagement Big data Small data We perform organisational analyses 15
- 16. We assist in process improvements Big data Small data This animation shows the flow of transactions for an organisation's top 20 suppliers for a period of 6 months. 16
- 17. Artificial Intelligence / Machine learning Blockchain What’s next Know what’s right and find what’s wrong across your general ledger on demand, with the objectivity of AI. Analyse expected Unexpected Analysed population Uses periodic sampling, mostly manual Data auditing analyses a whole data set using algorithms to determine expected and unexpected transactions The unexpected transactions can be broken down and analysed further, resulting in greater insight and an enhanced risk-focused audit A more significant portion of selected population is analysed Sample size Further analyse unexpected Traditional Trend 17
- 18. jasper.kerremans@be.pwc.com Jasper Kerremans Manager Risk Assurance Services PwC Process Mining Thank you! didier.delanoye@be.pwc.com Didier Delanoye Director Financial Assurance Services PwC Audit Transformation 18
Editor's Notes
- #4: ERP, SCM, CRM, and transactional Web applications are classic examples of systems processing Transactions. Highly structured data in these systems is typically stored in SQL databases. Interactions are about how people and things interact with each other or with your business. Web Logs, User Click Streams, Social Interactions & Feeds, and User-Generated Content are classic places to find Interaction data. Observational data tends to come from the “Internet of Things”. Sensors for heat, motion, pressure and RFID and GPS chips within such things as mobile devices, ATM machines, and even aircraft engines provide just some examples of “things” that output Observation data.
- #10: * workshops * interviews * observations * document analysis Those methods: * do not guarantee that the discovered model represents reality * they require a lot of resources and time * information gained using these methods may not be objective
- #12: * it is based on objective information * it is based on the complete data set describing employee actions * it allows you to discover many aspects of the process (main areas of focus, process models, interactions among people, flow of amounts and quantity) * the results are obtained quickly * the analysis can go deep into details as far the data is available
- #15: Payment delays & working capital improvements Potential duplicate invoices paid