Detecting Fraud Through Data Analytics

Transcription

1 Detecting Fraud Through Data Analytics Jeremy Clopton, CPA, CFE, ACDA Managing Consultant July 24, 2013 To Receive CPE Credit Participate in entire webinar Answer polls when they are provided If you are viewing this webinar in a group o Complete group attendance form with Title & date of live webinar Your company name Your printed name, signature & address o All group attendance sheets must be submitted to training@bkd.com within 24 hours of live webinar o Answer polls when they are provided If all eligibility requirements are met, each participant will be ed their CPE certificates within 15 business days of live webinar 2 1

2 How Fraud Is Detected 3 Occupational Fraud in the Insurance Industry *Source: 2012 Report to the Nations - Association of Certified Fraud Examiners, Inc. 4 2

3 Reactive Responsiveness Proactive Paper-based & limited electronic testing (Sampling) 5 Problems with the Old Method Ineffective Inefficient Reactive Hindsight Prevalence of Big Data 6 3

4 The New Method: A Wish List Do more with less 100 percent coverage Increase effectiveness More insight Not overly complex 7 Reactive Responsiveness Proactive Paper-based & limited electronic testing (Sampling) Data Analytics (100% coverage, ad hoc electronic testing) 8 4

5 The New Method : Data Analytics processes & activities designed to obtain & evaluate data to extract useful information... Useful information includes o o o o o o 9 Conflicts of interest Unknown relationships Abnormal patterns of activity Errors in key processes Control weaknesses Hindsight, insight, foresight Common Data Mining Areas Vendors & accounts payable Claims Employees & payroll Expense reimbursement Travel & entertainment General ledger 10 5

6 Vendor Attribute Capture 11 Vendor Activity Assessment 12 6

7 Name Mining 1. Acronym / Initials 2. Anagrams 3. Fictitious Names Mick E. Mowse Princess Ariel George Ruth John Dough 4. Others Substitution Insertion or Omission Transposition Numb3r Subst1tut10n 13 Employee/Vendor Relationships Matching Attributes Employee ID First Name Middle Initial Last Name Vendor ID Name City State Total Payments Address Jeremy R Clopton Vendor Name Anytown MO 16,

8 Conflicts of Interest Matching Attributes Employee ID First Name Middle Initial Last Name Vendor ID Name City State Total Payments Address Beth E Davis D Davis Designs Anytown MO 5,768 Address, TIN George R Davis 15 Address Mining Mailbox Services 16 8

9 Address Mining Proximity 17 Address Mining Proximity Employee Home UPS Store Employer 18 9

10 Proximity Analysis Vendor (A) Jeremy s Design Company, 123 5th Street, Anytown, MO (Total Payments = $84,337) Employee (B) Jeremy Clopton, 4300 Oak Street, Anytown, MO 19 Proximity Analysis AP Manager Vinny s Salvage Yard 20 10

11 Proximity Analysis 21 Vendor Trending Analysis Vendor: JLM Plumbing Authorized: Janice L. McPhearson Acceleration as confidence builds Getting Greedy Test phase 22 11

12 Vendor Trending Acceleration Patterns: Vendors exhibiting a pattern of increased activity over multiple consecutive periods. Valley Patterns: Vendors exhibiting a pattern of activity characterized by long periods of inactivity between periods of activity. Spike Patterns: Vendors exhibiting a pattern of activity characterized by unusually high payments in a single period. 23 Payment Trend Analysis By Day of Week By Day of Month By Month 24 12

13 Benford s Law Analysis Expected Frequencies First Digit Expected Frequency Second Digit Expected Frequency % % % % % % % % % % % % % % % % % % % 25 Benford s Law A/P 26 13

14 Benford s Law A/P 27 Benford s Law Expense Accounts 28 14

15 Check Sequence Analysis 29 Purchasing Cards 30 15

16 Analysis of Overtime Hours (654 hrs) 31 Analysis of Vacation Hours (426 hrs) 32 16

17 Analysis of Holiday Hours (182 hrs) 33 Other Areas of Application Access log controls testing Maintenance file analysis o Vendors o Customers Credit cards & purchasing cards Employee expense reimbursements General ledger 34 17

18 Tools To Consider Microsoft Excel SQL ACL IDE SAS Analytics 35 What s Next? Automated testing Analytics at the speed of business Foresight in addition to hindsight & insight 36 18

19 Reactive Responsiveness Proactive Paper-based & limited electronic testing (Sampling) Continuous Auditing (Automated analytics, 100% coverage) Data Analytics (100% coverage, ad hoc electronic testing) 37 Example 1 Manufacturing Company Description o Revenues: $7.9 billion o Internal audit staff: 5 o Operating divisions: 20 o Vendor: 100,000 o Employees: 7,000 o Payments per year: 250,000 Application of Continuous Auditing o Risk 1: Conflicts of interest o Solution: Annual employee/vendor matching Risk 2: Duplicate payments Future goal: Monthly analysis of all invoices for potential duplicates 38 19

20 Example 2 Public University Company Description o Revenues: $1 billion o Internal audit staff: 5 o Vendors: 83,000 o Employees: 3,900 o Purchasing card users: 1,100 Application of Continuous Auditing o Risk 1: Duplicate payments o Solution: Quarterly analysis of all invoices for potential duplicates Risk 2: Split transactions Solution: Quarterly analysis of cardholder transaction details 39 Critical Information on Continuous Auditing Con nuous audi ng con nuous monitoring Continuous auditing o Owned by internal audit o Risk & control assessment o Process focused Continuous monitoring o Owned by management o Effectiveness & adequacy of controls o Control focused 40 20

21 A Plan A Plan to to Get Get you you There There 41 Assess Risk Manage Results Define Objectives Analyze Results Obtain Data Develop & Apply Procedures 42 21

22 Challenge to Consider Data Quality , (417) , , Missoura, MO, Mis, Miss, MZ, MS, Miz, Mizz PO Box 34, P.O. Box 34, Box 34, Bx 34, P.O Box 34 Clopton, Clapton, Clompton, Clampton, Cloptin 12345, 12345a, , Progression of Procedure Development Repetitive Individual Tests Single-Purpose (Individual) Tests Groups of Repetitive Similar Tests Automation of Groups of Tests Groups of Similar Tests 44 22

23 Resources IIA Global Technology Audit Guides o Continuous Auditing o Fraud Prevention & Detection in an Automated World o Data Analysis ISACA White Paper o Data Analytics A Practical Approach Continuing Professional Education (CPE) Credits BKD, LLP is registered with the National Association of State Boards of Accountancy (NASBA) as a sponsor of continuing professional education on the National Registry of CPE Sponsors. State boards of accountancy have final authority on the acceptance of individual courses for CPE credit. Complaints regarding registered sponsors may be submitted to the National Registry of CPE Sponsors through its website: The information in BKD webinars is presented by BKD professionals, but applying specific information to your situation requires careful consideration of facts & circumstances. Consult your BKD advisor before acting on any matters covered in these webinars

24 CPE Credit Up to 1 CPE credit will be awarded upon verification of participant attendance; however, credits may vary depending on state guidelines For questions, concerns or comments regarding CPE credit, please the BKD Learning & Development Department at training@bkd.com 47 Jeremy Clopton, CPA, CFE, ACDA Managing Consultant Forensics & Valuation Services Phone: jclopton@bkd.com Blog: bkdforensics.com LinkedIn: 24