Connecting the Dots: Your Role in Corporate Performance Management Part 2 Analytics Where Audit Meets Performance Stephen Wang Ernst & Young

Transcription

1 Connecting the Dots: Your Role in Corporate Performance Management Part 2 Analytics Where Audit Meets Performance Stephen Wang Ernst & Young

2 Stephen Wang EY ShinNihon Senior Manager, Advisory Services Financial Services Enterprise intelligence, data analytics

3 Agenda Current state poised for revolution The journey continuously balancing risk and performance Future state charting a revolutionary course Questions

4 POISED FOR REVOLUTION There is disparity between needs and skills around data analytics

5 Data analytics a critical component of IA Per EY s 2013 Global Internal Audit Survey: Few (12%) Internal Audit functions use data analytics throughout the entire audit cycle from risk assessment and identification to testing to continuous auditing The majority (55%) use it only for testing For those who do use analytics, 64% use them for fewer than 30% of their audits. Data analytics skillsets are key to IA success, but competent resources are lacking. Data analytics was ranked as the #1 competency lacking in IA departments 30% 27% Effective data analytics will confer competitive advantage in the future. More and more leading organizations are implementing data analytics projects in an attempt to remain competitive. Data analytics is a key skillset Analytics competency is lacking

6 Current trends for IA Analytics 2013 roundtable of internal audit leaders found: Many internal audit functions are embarking on multi-year transformation projects focused on implementing continuous monitoring and analytics Embedding the use of data analytics into the audit process is one of the more difficult work streams for IA organizations to achieve As a part of the analytics initiative, organizations would like to: Incorporate CAATs and data analytics into the standard skills and tool set of the IA group Integrate analytics into the annual audit plan and quickly deliver tangible results and measurable value Provide risk-based audit coverage for all relevant legal vehicles, locations, and business units Perform horizontal/thematic audits as appropriate Audit functions are looking for personnel with the requisite internal audit, technical and industry knowledge to help facilitate the changes in auditor mindset, methodology and infrastructure needed to enhance the value of audits through the use of data analytics.

7 Takeaways from the EY IA Analytics Roundtable Emerging Themes Key Takeaways 1. Strategy and Framework 2. Use in Annual & Audit Planning Inconsistent vision and/or direction for analytics Lack of methodology and trained resources Reliance on legacy audit and end user tools (such as ACL and Excel) for analytics Incorporated into plan roughly 60% of the time Lightly used in driving decisions Rarely leveraged for management reporting and substantiation of residual risk Formalize the analytics framework and methodology for the department Develop strategic roadmap for incremental use of analytics on audits Consider pros and cons of core analytics team vs. embedded resources Promote visual analytic tools to minimize time and effort spent ingesting data rather than analyzing it Analyze prior year metrics to inform planning decisions Define quantitative key performance indicators (KPIs) and key risk indicators (KRIs) Integrate audit risk, findings, documentation and project management data sources to gain deeper insight into current trends Develop monthly, quarterly and annual reports and dashboards for management 3. Use in Audit Execution Mostly used for audit coverage and depth Utilized inefficiently and only somewhat effective Significant challenges include budget and time constraints, and unreliable sources of data Build the case for analytics by quantifying risks associated with findings Leverage analytics in performing pilots or proof of concept audits Make analytics repeatable to gain efficiencies in executions over time Incorporate data quality testing into analytics methodology Enhance capabilities in controls testing in focus areas such as trading controls

8 THE JOURNEY Continuously balancing risk and performance

9 Think bigger Convergence of data availability, processing power, and tools brings the data revolution to the individual level Consistency, repeatability, efficiency Power of visualization More exposure has led to familiarity and acceptance Data consumers are more convinced of the value of analytics

10 Bridging the risk-performance gap Traditional point-in-time analytics like AR/AP, Inventory, Payroll Still valuable, but are now just the tip of the iceberg Often insufficient to tell the whole story about risks and performance Risk is constantly evolving why limit yourself to one audit a year? Emerging areas for audit analytics Analytics for external audit Turning the risk discussion upside down Custom transactional analytics Workforce analytics Who s working too much or charging differently? Who s headed for issues?

11 Risk: traditional vs. data-centric

12 Data integration into audit process

13 One small step Japan securities subsidiary of a global bank Mix of required procedures and custom analytics Results being shared externally to client s regional CFO, and internally to Global Audit Partner Using these results as the launching point of a giant leap in audit analytics

14 Trades by status

15 Daily trade volume

16 Cancels by trader

17 Daily cancel volume

18 Net of back-to-back transactions -6B -4B -2B 0B 8B Purchase

19 Fee vs. trade amount

20 Fees vs. trade amount cancelled trades

21 Purchase vs. repurchase price -1000B -500B 0B 1500B Purchase Pr

22 Purchase vs. repurchase price -50M -40M -30M -20M -10M Running S

23 Charged hours by team member

24 Total hours by level

25 Average hours by person at each level Avg Hrs/P

26 Automating the process

27 CHARTING A REVOLUATIONARY COURSE An Analytical Framework for the Future

28 Charting a revolutionary course Develop analytics execution strategy Assess the current state of analytics capabilities from a people, process, and tools perspective and define a future state roadmap to outline the vision for Internal Audit Analytics. Update audit methodology Define an updated audit methodology that promotes the usage of analytics, pulls auditors away from basic sample controls testing, and focuses on all stages of the audit lifecycle. Execution roadmap Build a execution roadmap, tested through a pilot approach, for each key area that highlights what analytics are to be performed, where analytics should be focused, and what is required to reach the desired future state.

29 Strategy maturity model roadmap Year 1 Methodology Methodology Purpose Purpose and And Mandate Mandate Competency Competency Development Development Resourcing Sustaining Resourcing People Excellence Operations Tools Technology and Operations Technology Knowledge Management Quality Year 2 Methodology Methodology Purpose Purpose and And Mandate Mandate Competency Competency Development Development Resourcing Sustaining Resourcing People Excellence Operations Tools Technology and Operations Technology Knowledge Management Quality Rating Basic Evolving Established Advanced Description Limited activities exist for this performance factor Some parts of this performance factor exist, application on different levels is inconsistent Performance factor is pragmatically defined, consistently applied on some of the levels involved Performance factor is defined in more detail, consistently applied on many levels involved Year 3 Methodology Methodology Purpose Purpose and And Mandate Mandate Competency Competency Development Development Resourcing Sustaining Resourcing People Excellence Operations Tools Technology and Operations Technology Knowledge Management Quality Leading Performance factor is defined in more detail and consistently applied on all levels involved

30 Critical success factors Clear support for the project and related messaging from the Chief Audit Executive Buy-in from leadership team quick hit wins key to drive this Dedicated project team from IA team with performance incentives aligned to the success of the project Effective teaming between IA CAATs team and the audit team is critical to: transfer knowledge develop the data analytics champions that will serve as the cornerstone for sustaining the use of data analytics in the future. Investment in infrastructure and training tools, data repository

31 THANK YOU QUESTIONS?