Download to read offline
More Related Content
Similar to Merging forensics w data analytics
![Matrix and determinant URT [Autosaved].pptx](https://cdn.slidesharecdn.com/ss_thumbnails/matrixanddeterminanturtautosaved-251018190340-9e6a6deb-thumbnail.jpg?width=600ounds&width=560&fit=bounds)
![RTP_AR_Basic_Learners' Workbook_KS2 [FOR REPRODUCTION] (1).pdf](https://cdn.slidesharecdn.com/ss_thumbnails/rtparbasiclearnersworkbookks2forreproduction1-251016024943-e51a16ac-thumbnail.jpg?width=600ounds&width=560&fit=bounds)
Merging forensics w data analytics
- 1. Merging Forensics withData Analytics Monday, September 28, 2015 Time Josh Shilts, CPA/CFF/CGMA, CFE
- 2. Presentation will beavailable at: www.misti.com/download Download password is available in your Show Guide
- 3. MIS Training InstituteHoldings, Inc. Slide 3 © Big Data Phenomenon and its’ Effect on Organizations & Audit Departments Data Analytic Program Landscape Forensics + Data Analytics = Mitigated Risk Continuous Auditing Evolution Data Analytics Future: Organization and Audit Department Affects Key Points
- 4. MIS Training InstituteHoldings, Inc. Slide 4 © The BIG Data Phenomenon “Information is the oil of the 21st century, and analytics is the combustion engine.” - Peter Sondergaard, Gartner Research
- 5. MIS Training InstituteHoldings, Inc. Slide 5 © The BIG Data Phenomenon “Ninety percent of the world’s data has been created in the last two years… The ultimate question is really what insight and value can we draw from that data.” - George Lee, Chief Information Officer, Investment Banking Division, Goldman Sachs, Sept. 2014
- 6. MIS Training InstituteHoldings, Inc. Slide 6 © The BIG Data Phenomenon “Big data is not about the data.” - Gary King, Harvard University, making the point that while data is plentiful and easy to collect, the real value is in the analytics “Hiding within those mounds of data is knowledge that could change the life of a patient, or change the world.” - Atul Butte, Stanford School of Medicine
- 7. MIS Training InstituteHoldings, Inc. Slide 7 © The BIG Data Phenomenon “Figures often beguile me, particularly when I have the arranging of them myself… …there are three kinds of lies: lies, damned lies, and statistics.” - Mark Twain, 1906
- 8. MIS Training InstituteHoldings, Inc. Slide 8 © Employee & Customer Behavior Supply Chain Efficiency Faster Decision Making Push to understand Data Structure and Advanced Analytical Tools Identify Data Analytics Talent Embedding Big Data This creates a lot of unanswered questions…How is big data going to improve our performance as a business? What will the company focus on? etc… Effect on Organizations
- 9. MIS Training InstituteHoldings, Inc. Slide 9 © “Trickle Down” to Audit Departments Employee & Customer Behavior Supply Chain Efficiency Faster Decision Making Push to understand Data Structure and Advanced Analytical Tools Identify Data Analytics Talent Embedding Big Data This creates a lot of unanswered questions…How is big data going to improve our performance or the audit plan? What will the audit plan focus on? etc…
- 10. MIS Training InstituteHoldings, Inc. Slide 10 © Use of “Scripts” defined by software companies, large internal audit consulting shops and well known professional associations (i.e. IIA, ACFE) Excel, ACL and IDEA used to identify duplicates, quantitative outliers (e.g. aberrations, deviations) No defined method for script implementation No use of existing resources Audit Management “forcing” use of analytics with no other reason to do so than meet professional association guidance Current Data Analytic Landscapes
- 11. MIS Training InstituteHoldings, Inc. Slide 11 © “If you want to give yourself a fair chance to succeed, never expect too much too soon” – Po Bronson Data Analytic Program Landscape
- 12. MIS Training InstituteHoldings, Inc. Slide 12 © - Don’t put the cart before the horse…
- 13. MIS Training InstituteHoldings, Inc. Slide 13 © To succeed you must utilize existing knowledge as well as existing tools to build the “base” of your program… Magnitude Major >$50M 5 Substantial >$25M 4 Moderate >$ 10M 3 Minor >$1M 2 Insignificant <$1M 1 Define how Financial Impact is measured (i.e. Net Income, Revenues, etc.) 1 2 3 4 5 Remote Unlikely Possible Likely Almost Certain Likelihood 12 11 3 10 4 6 5 14 13 15 9 8 1 7
- 14. MIS Training InstituteHoldings, Inc. Slide 14 © Before even the discussion of a analytic tool consider using existing information…
- 15. MIS Training InstituteHoldings, Inc. Slide 15 © Forensics + Data Analytics Magnitude Major >$50M 5 Substantial >$25M 4 Moderate >$ 10M 3 Minor >$1M 2 Insignificant <$1M 1 Define how Financial Impact is measured (i.e. Net Income, Revenues, etc.) 1 2 3 4 5 Remote Unlikely Possible Likely Almost Certain Likelihood 9
- 16. MIS Training InstituteHoldings, Inc. Slide 16 © Some forensic accountants specialize in forensic analytics which is the procurement and analysis of electronic data to reconstruct, detect, or otherwise support a claim of financial fraud. The main steps in forensic analytics are (a) data collection, (b) data preparation, (c) data analysis, and (d) reporting. For example, forensic analytics may be used to review an employee's purchasing card activity to assess whether any of the purchases were diverted or divertible for personal use. Wikipedia How Does Forensics Relate
- 17. MIS Training InstituteHoldings, Inc. Slide 17 © Equals Mitigated Risk Continuous audit, much like the disciplines currently employed within forensic audit, focuses on the prevalence of a risk and/or the effectiveness of a control… …with automated, frequent analyses of data, you will be able to perform control and risk assessments in real time or near real time. Thus, giving you the ability to analyze key systems for anomalies at the transaction level.
- 18. MIS Training InstituteHoldings, Inc. Slide 18 © Representative Examples 1. Invalid Tax ID – generate a report to identify all expense payments sorted by Tax ID. Investigate all payments wherein there is no Tax ID, Tax ID is all 9s, or contains alpha characters. 2. Duplicate Payments – Captures payments either same $ paid to the same payee and/or check # 3. PO Box Payments – Captures all payments made on an insurance claims that included a PO box as the address. The majority of “canned” scripts focus on cash misuse and/or larceny fraud schemes…
- 19. MIS Training InstituteHoldings, Inc. Slide 19 © Representative Examples However; focus should be on “real” fraud risks and not utilizing resources to complete “common” tests… Scenario Fraud Risk Potential Test Company has a significant # of employees who travel. Abuse of company credit card. Identify how many charges per employee are incurred in their hometown. Technology company incurs high percentage of employee turnover below the manager level. Inappropriate access with non-existent employee IDs Run a report to compare employee leaves vs. change management activity. Insurance company who prides itself on making timely payments to customers. Payments are made without appropriate approvals or support. Run a report to identify claims made within 5 days of endorsement to identify if payment is made on an excluded loss.
- 20. MIS Training InstituteHoldings, Inc. Slide 20 © Forensics has long been a discipline that focuses on a specific risk or situation and utilizes tools and knowledge to pinpoint its cause… My Point …data analytics builds on the “Forensic Objective” by allowing professionals to decipher mounds of data and information and focus on more than just fraud within a set of data.
- 21. MIS Training InstituteHoldings, Inc. Slide 21 © Continuous Audit Process Implementation Graph Continuous Audit Process Implementation Steps 1 Establish Priority Areas 6 Report Results 5 Manage Results & Follow Up 4 Configure Parameters & Execute 3 Determine Process Frequency 2 Identify Audit Rules 7 Assess Emerging Risk & Add to Register
- 22. MIS Training InstituteHoldings, Inc. Slide 22 © Continuous Auditing Evolution
- 23. MIS Training InstituteHoldings, Inc. Slide 23 © CONTINUOUS AUDIT RISK/ CONTROL ASSESSMENT FACT FIND DATA ANALYTIC INITIATION CONTINUOUS AUDIT ACTIVITY PLAN DATA ANALYTIC STORAGE REPORTING Qn REVIEW & ASSESSMENT Intake Assess Emerging Plan Changes Resource Allocation Process & System Reviews Other Activity Integration 1st & 2nd Lines Monitoring Ratios / Trends Script Initiation Evaluation Periodic Process Mngt. Tools Quarterly Annual Continuous Audit Process Implementation Steps 1 Establish Priority Areas 6 Report Results 5 Manage Results & Follow Up 4 Configure Parameters & Execute 3 Determine Process Frequency 2 Identify Audit Rules 7 Assess Emerging Risk & Add to Register
- 24. MIS Training InstituteHoldings, Inc. Slide 24 © The Future Improve Existing Products and Services Improve Internal Processes Building New Products or Services Transforming Business Models The business of Forensics by improving the determination of priorities and picking the right angle to review.
- 25. MIS Training InstituteHoldings, Inc. Slide 25 © Big Data is in fact a phenomenon that has a lot of theory behind it. Problem is most of it is theory… Some problems the theorists forgot to think about is: Data Integrity Core Value and Missions Continuous Control Monitoring is complex and requires the appropriate number of resources and allocated time. You need very creative individuals to implement. The Realities
- 26. MIS Training InstituteHoldings, Inc. Slide 26 © Don’t let the Big Data phenomenon distract you from your core values and mission Focus on “easy” wins by using ‘canned’ scripts Build slowly: know what works for you and don’t get caught up in an “arms race” Audit the Data!!! Summary
- 27. THANK YOU! Please RememberTo Fill Out Your Session Evaluation Forms!
Editor's Notes
- #2 MIS Training Institute Section # - Page 1 XXXXXX XXX ©
- #3 MIS Training Institute Section # - Page 2 XXXXXX XXX ©
- #4 MIS Training Institute Section # - Page 3 XXXXXX XXX ©
- #5 MIS Training Institute Section # - Page 4 XXXXXX XXX ©
- #6 MIS Training Institute Section # - Page 5 XXXXXX XXX ©
- #7 MIS Training Institute Section # - Page 6 XXXXXX XXX ©
- #8 MIS Training Institute Section # - Page 7 XXXXXX XXX ©
- #20 Fraudsters are better than that
- #27 MIS Training Institute Section # - Page 26 XXXXXX XXX ©
- #28 MIS Training Institute Section # - Page 27 XXXXXX XXX ©