Energy Future Holdings (EFH)

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1 Energy Future Holdings (EFH) Inclusion of Data Analytics into the Internal Audit Lifecycle June 3, 2015

2 Starting Place Baseline Questions Pertaining to the utilization of data analytics in the internal audit lifecycle, where are we currently? What is our desired end state? Where do we want to use it: Planning? Execution? Reporting? Which Business Units? Which systems will we use? How soon do we want to get there? How do we get there? Existing Resources, Dedicated Resources, Additional Resources How do we measure success? Existing Data Analytics Likely Easy Wins 1

3 The 2013 PwC State of the Internal Audit Profession Survey indicated that the most commonly underperforming IA attributes are Promoting quality improvement and innovation Obtaining, training and/or sourcing the right talent to match the organization s risk profile Leveraging technology such as automation, data and advanced analytics 2

4 So What is Data Analytics? A process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision making. 3

5 CAE s Expectations for Data Analytics 1 59% of CAEs plan to add data analytics skills in the next 18 months Among those who intend to expand the use of analytics, 71% lack a well-developed plan to do so. - Biggest barriers: Inefficient data collection processes and tools Finding capabilities and resources to support vision 1 Source: 2013 PwC State of the Internal Audit Profession Survey 4

6 CAE s Thoughts on the Use of Data Analytics 1 Percent of CAEs who agree 31% 71% 74% 81% 85% Data analytics are used regularly Plan to expand use of data analytics but do not have a welldeveloped plan Data analytics are important to gaining a better understanding of risks Data analytics are important to improving the quantification of issues Data analytics are important to strengthening audit coverage 1 Source: 2013 PwC State of the Internal Audit Profession Survey 5

7 Common Benefits of Data Analytics 1 Increased efficiency (often after year 1) Productivity and cost savings (broaden assurance scope without increasing staff size) Reduced audit risk (stratifying the population, thereby honing risk assessment) 100% testing (providing greater assurance) Standardization (less reliance on individual super auditors ) Enhanced understanding (data profiling/trending) Streamlined data correlation (automatic match) Reduced travel (data accessed from anywhere) 1 Source: MIS Training Institute internal survey 6

8 Common Roles Associated with Data Analytics 1 Data Analyst - Access, prepare and make data available - Perform advanced analysis and create analytics and results for sharing Staff Auditors - Review and interpret results - Perform simple analysis independently Audit Directors - Oversight of analytic-enabled audit activities 1 Source: Global Technology Audit Guide 16 7

9 Data Analytics Across the Audit Process 1 Planning & Preparation Fieldwork & Testing Reporting Identify data-driven risk indicators - Current - Emerging Use to refine risk-oriented audit plan Prepare automated test routines in advance of fieldwork Make the above available to audit team so able to hit the ground running Provide trending and profiling Provides consistency because same factors are applied across all audit areas Increased assurance through 100% testing When sampling is required, ability to more intelligently sample More easily re-create and automate tests in subsequent periods Increase auditor understanding of process(es) More accurately quantify findings Reduce subjectivity and interpretive judgment Ability to dig through data already acquired to mine for other issues and trends 1 Source: Global Technology Audit Guide 16 8

10 Data Analytics Capability Levels 1 Level 1: Basic Level 2: Applied Level 3: Managed Level 4: Automated Level 5: Continuous Supports specific audit objectives Ad-hoc, often unplanned use Limited audit staff Fully integrated into target audit processes Includes both audit planning and fieldwork Structured approach Tests re-used and added in subsequent audits Centralized and structured knowledge management: - Data - Audit tests - Results - Supporting docs Collaboration Controlled, secure access Well-documented procedures Established data access protocols Suites of tests available to audit team Concurrent ongoing auditing of multiple areas Structured issue reporting Management monitoring of own process IA assesses monitoring activities 1 Source: Global Technology Audit Guide 16 9

11 Common Data Analysis Tools Microsoft Excel - Quick, ad-hoc analysis of small to moderate file size - Easy formatting and graphing of results; easy distribution to others - Basic automation through macros Microsoft Access - Relates/combines information from multiple tables - Handles larger file sizes than Excel - Complex analysis possibilities ACL - Desktop and Exchange packages - Heavily used in audit community - Handles very large data volumes - Good for automated analysis for continuous/repetitive use Idea / Spotfire - Handles data from multiple systems - Handles very large data volumes - Visual and graphical tools 10

12 Common Data Analysis Tools (cont d) IBM Cognos - Handles data from multiple systems - Handles very large data volumes - Visual and graphical tools SQL - A standard computer language for relational database management and data manipulation - Used to query, insert, update and modify data from various systems 11

13 Keys to Success 1 Align strategy with audit plan, risk, goals and objectives Manage like a program Develop uniform practices and procedures Assign responsibility Document the intent of analysis Understand the process Understand the data Document the content of analysis Establish a peer or supervisory review process Standardize procedures and tests in a central repository Safeguard source data from modification or corruption Develop strategies to address potential impact on production processing Treat training as a continuous process Aim for constant improvement Ensure results are accurate and appropriate 1 Some data analytics are performed within processes at EFH 12

14 EFH Data Analytics Vision & Objectives Vision: Integrate data analytics as a standard audit practice and support the expanded utilization of continuous auditing and monitoring practices across EFH. Included will be: Objectives: Embed data analytics into audit processes and procedures to enable: 1) Integration of expanded data analytics into the annual risk assessment and audit planning processes, 2) Assessment of data analytics applicability during the planning and testing phases for designated audits, and 3) Leveraging of data visualization to enhance audit reporting. Establish a technical data analytics environment with a targeted business data model to support an ongoing data analytics program. Identify opportunities to expand continuous auditing in high risk and recurring audit areas to increase audit coverage. 13

15 Implementation Project Overview EFH IA decided to develop and implement an information management and data analytics capability to support EFH s entire audit spectrum (fieldwork, reporting, planning, & risk assessment). The following tasks and deliverables were completed from a functional and technical perspective for this effort: Current State Assessment Gap Analysis Vision and Roadmap Proof of Concept Develop Processes & Procedures 14

16 Strategy, Roadmap, and Plan Processes and Deliverables Definition Information Gathering Analysis and Planning Execution Project Management Business Audit Plan Conduct Audit Leadership Interviews Determine Audit & Information Processes Perform Business Need & Value Assessment Need & Value Summary Executive Summary Project Planning & Kick-Off Document Existing Capabilities & Landscape Current State Assessment Vision & Roadmap Program Planning Master Plan POC Pilot Phase 1 Completed Technical Project Management Analyze and Document Current State Architecture and Technology Perform Current / Future State Gap Analysis Duration Estimates TBD (e.g., 6-8 weeks) Gap Analysis Summary Planning & Support Documents 2 Weeks 4-6 Weeks Current Next Process 3* Months Develops vision for the organization based on key business drivers Current state assessments identify areas for improvement and effectiveness from a cost and productivity perspective Roadmap is built upon business need and value while also addressing possible technology gaps Roadmap defines an iterative / phased approach to implementation Incorporates proof of concept to prove technology and analytic benefit Develops multi year plan for future releases based on business need 15

17 Industry Maturity Level Where is EFH today and where we want to be High Predictive Industry Leader Scale is achieved for department specific teams improvement methodologies are implemented monitoring occurring for metrics and controls Program Maturity Mitigated Managed Reactive Advance Capabilities are well developed and practiced with appropriate governance Data sources are readily available Activities begin to become repeatable and CM metrics are developed Defined Capabilities developed and adopted Capabilities used to drive audits Defined goals and standardized processes and tools Initial Developing Limited capabilities Ad hoc activities resulting in unpredictable and inefficient performance Success based on individual competence EFH EFH Current Future State Low As Needed Return on Investment, Effeciency, Insight 16

18 Recommended Phases & Timelines 2015 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC IA Road Map Phase 1 Current State Future State Road Map POC - AP Functional Data Technology Measurement Phase 2 Functional Data Technology Phase 3 Functional Data Process/ Practice Measurement Technology Foundation AP (Non PO PO) EFH - LUMP Contract Compliance T&E Plant Operations Process/ Practice LUMP - EFH Mine Operations Incentive Plans IT LUMP- TXUE Reporting Data Quality Environment and Safety Continue Next Page Systems FIM / MAX. / MyExpense Systems FIM / Max. / Workday Systems Epsilon / CATs / Lodestar Audit Spectrum Audit Policies/Procedures **Technology includes Data Model, Data Capture and Interfaces. **Measurement includes Data Quality, Dashboards and Data Analytics. ** Process/Practice include Data Integrity, Data Standardization and Master Data Management. Planning, Testing and Reporting associated with each phase 17

19 Current Data Analytics at EFH Name Description Business Area 1 T-Card Late Payment Notification Review late payments of Travel Card transactions to reinforce policy. Maintain a log of late notices from 2011 to allow historical review. Travel and Expense 2 Improper T-Card Use Review late payments of Procurement Card transactions to identify unusual usage transactions. Follow-up as appropraite based on fraud potential. Travel and Expense 3 Unassigned T-Card Transactions 4 Deal Confirmation Validation Review Travel Card transactions which have not been submitted on an expense report for manager review. Follow up with manager and employee as appropriate. Match Deal Confirmation Spreadsheet with the deal tickets to test that deals are approved by counter-party. Travel and Expense Trading 5 Zainet Deal Update Review deal changes in Zainet to ensure that any changes were made by a trader are consistent with the books he/she operates in. 6 Deal Change Monitoring Analyze deal key attribute changes (after 3-day entry period) to identify any patterns which could represent an inappropriate change. Any identified anomalies are reviewed with the business owner. Trading Trading 7 Inter-Desk Deal Review Review transactions between the various deal books to ensure that they are in balance and that they have not been improperly changed between "Accrual" and "Mark to Market" to manipulate earnings. Power Sales 8 Market Power Price Review Correlate wholesale electricity prices at 4 load centers closest to our plants and investigate any correlation between prices spikes and Luminant plant outages. 9 Controls Monitoring Analyze and report against the Compliance System controls by area and type to provide management with a view of the overall control coverage and compare to historical coverage to identify changes to the controls environment during the period. 10 Duplicate Invoice Compare Invoice (Vendor, Invoice Number, and Invoice Date to the voucher before and after to see if it is a duplicate. Set flag to 1 if it is a duplicate. Power Sales Compliance Account Payable 18

20 Current Data Analytics at EFH Name Description Business Area 11 Invoice Number Order Compare Invoice Number to the voucher before to see if it is ouot of order (INVOICE_ID < previous INVOICE_ID. Set flag to 1 if it is out of order. Account Payable 12 Invoice Entry Day of Week Set Flag to 1 (True) if voucher was an Online Entry and entered on a Saturday or Sunday. Account Payable 13 Invoice ID Pattern Mismatch Set Flag to 1 (True) if the voucher number pattern is inconsistent with the voucher before or after. 14 Invoice Amount Check Set Flag to 1 (True) if the amount is more that one Standard Deviation form the average for that vendor. Account Payable Account Payable 15 Invoice Amount Round NumbSet Flag to 1 (True) if the invoice amount is an even dollar amount and not a prepay. Account Payable 16 Invoice Amount Benford TestSet Flag to 1 (True) if the first 2 digits are inconsistent with the expected Benford occurrence percentage by a factor > Invoice Date Receipt Period Set Flag to 1 (True) if the Invoice Date occurrence timing is more than one standard deviation less than normal date occurrence time. Account Payable Account Payable 18 Invoice Number Spread Set Flag to 1 (True) if the average spread between invoice numbers is < 3. Account Payable 19 Late Payment Set Flag to 1 (True) if the voucher Payment Date is greater than the invoice due date (and not a canceled voucher). Account Payable 19

21 POC - Executive Dashboard 20

22 POC - Duplicate Payments 21

23 POC - Early Payment 22

24 EFH Internal Audit & SOX Compliance Data Analytics Request Execution Process Origination Assessment Execution Audit Project Investigation Audit Team Data Requirements Identification Data Analytics Team Data Analytics Assessment & Evaluation New Analytics Required Develop New Analytic Execute Data Analytics Assess / Tune Generate Data Reports, Visuals, and/or Exceptions Management Request OR Analytics Already Exists OR Data Analytics not applicable Tune Existing Analytic Schedule Data Analytics Utilize Data Analytics Finalize Capture End Transition to Client Document / Archive Data Analytics Objects Install Data Analytics System Package Configure Optimize Document Review Analytics with Client Assess Data Analytic Future Use One Time Use No Future Plans OR Implement as a Continuous Audit Stream OR Support Client s Continuous Monitoring Usage

25 Next Steps for EFH Modify audit procedures to include data analytics considerations throughout the audit lifecycle (i.e., during familiarization & planning, risk assessment, fieldwork and reporting) Revise TeamMate audit programs to reflect new processes associated with data analytics Continue to develop analytics that can be used for continuous auditing (not associated with audits in the annual plan) Work with the businesses to develop analytics that can be used for continuous monitoring 24

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