Presentation on theme: "Sean Walker, Managing Principal Ryan Merryman, Senior Manager"— Presentation transcript:
1 Sean Walker, Managing Principal Ryan Merryman, Senior Manager Using Data Analytics as a Management Tool to Identify Organizational RisksSean Walker, Managing PrincipalRyan Merryman, Senior Manager
2 ObjectivesDiscuss how data analytics can be used to better identify various risks in an organization.Encourage you to use technology to protect the organization.Demonstrate the power of data analytics using a case study.
3 What and Why Data Analytics? Data analytics is the process of accessing, normalizing, and modeling data with the intent of discovering useful informationOften consider a forensic toolMuch more can be learned about your organizationLarge organizations such as States process very large amount of data and often in a decentralized mannerRisk of misappropriationsRisk of management override of internal controlsRisk of the unknown
4 Misconceptions It’s hard. It’s time consuming. It’s expensive. It’s an audit tool.Get it done –Outsource itGet trained and get coached
5 Organizational Studies What is normal for my organization?What is abnormal?Who is working in the system?When are they working in the system?Who is making all those journal entries?What systems feed information to the financial system?
6 Types of Risks and Areas of Analysis Accounts PayableFictitious vendorsFictitious, inflated and / or duplicate invoicesStructured paymentsConflicts of interestKickbacksBid-riggingPurchase CardsDuplicate purchasing and reimbursement schemesUnauthorized and/or improper purchasesUnauthorized usersUnauthorized SIC codesPayrollGhost employeesImproper supplemental paymentsImproper bonus or incentive compensation paymentsInflated salariesInflated hoursTravel and Entertainment ExpenseFalse or inflated reimbursement submissionsImproper use of corporate credit cardPurchase for personal useForeign Corrupt Practices ActJournal EntriesUnbalanced journal entriesImproper management overrideImproper expense capitalizationImproper revenue recognitionEntries to unusual or seldom used accountsImproper or unauthorized user activityEntries during non-business hours
7 Types of Risks and Areas of Analysis Accounts ReceivableFictitious customersLappingCredit balance fraudOffsets with unauthorized or improper expensesImproper AR agingInventoryFictitious, inflated, duplicate or unnecessary purchasesTheft through improper write-offExcessive shrinkageRevenueFalse or inflated salesFictitious customersImproper commission or bonus paymentsRevenue recognition abuses including channel stuffing, liberal return policies or bill and hold schemesNon-FinancialWeblog analysisBuilding access logsComputer print reportsClient proprietary database analysis
8 State Specific Risks Internal control overrides General disbursements Avoid purchase authority escalationJournal entriesGeneral disbursementsPayrollDerived revenues (sales tax, etc.)Beneficiary paymentsHealth and Human ServiceGrant payments
10 Cash Disbursement Testing: Objective - Test 3 Cash Disbursement Testing: Objective - Test 3.1 Million Payments Totaling $7.0 BillionChallenges faced by us: Each monthly report provided by the State:Was in a massive .pdf “print report” format, each monthly files was approximately 15,000 pagesThere were 3.1 Million payments in FY 2013The files were too large to print and so large they crippled laptops123 ft360,000 pages6 ft
11 Cash Disbursement Testing: The Monthly Report was Over 15,000 Pages
12 Most Systems Provide Data in a Usable Form Time and EffortMost Systems Provide Data in a Usable Form
13 Cash Disbursement Testing: Understanding the Data and Year over Year Comparisons We were able to analyze the files and compare activity from FY 2013 to FY 2012
16 Stratification by Payment Amount FY 2013FY 2012
17 Cash Disbursement Testing: Abnormal Payments Specific Analysis that would be difficult/impossible without Forensic Data Analysis:Payees whose payments amounts varied significantly
18 Cash Disbursement Testing: Specific Analytics Specific Analysis that would be difficult/impossible without Forensic Data Analysis:Retirement Numbers that had more than one name associated
19 Cash Disbursement –Data Analysis vs. Traditional Procedures Imported 100% of dataReconciled totals to F/S for Completeness TestingSummary Results that tie to F/S balances and compared to PYRun specific queries from which to make Risk Based selections for test workMore efficient - Analysis FY 2013 procedure took only about 60% of the time of FY 2012Traditional ProcedureRandom SamplingNo Completeness TestingLess efficient
20 Consider How much data is collected in your organization? How quickly can you analyze the data for management decisions and internal risks?As Comptroller’s do you believe you have your hands around all the state’s transactions?
21 Sean M. Walker, CPA, CGFM, CGMS Managing Principal State and Local Government Ryan Merryman, CPA/CFF/CITP, CFE Senior Manager Forensic Services