Data Analytics in Internal Audit. Elizabeth Dunkerley

Transcription

1 Data Analytics in Internal Audit Elizabeth Dunkerley

2 Who Am I? Born in Bermuda Master s degree at King s College London Joined KPMG 2014 Technology Risk Data group 1

3 What is Data Analytics? Why is Data Analytics important? Where are you now? 2

4 What is Data Analytics? - Information in raw or unorganized form - Big Data - Metadata - Qualitative vs. Quantitative - Volume, Variety, Velocity and Veracity - Examining raw data with the purpose of drawing conclusions from the information. 3

5 4 Box Model Ad-hoc Repeatable Data Analytics Mature Predictive 4

6 Why Should my Business Care? - Catches risks - Encourages compliance - Develops insights and knowledge Adds value to business 5

7 A Management Perspective Source: KPMG international survey: Going beyond the data: turning data from insights into value A survey based on data collected from 830 senior business executives across more than 15 countries. 6

8 How Relevant is Data Analytics for Internal Audit? But Data Analytics is not just a focus area itself, it is also a capability that can support all of the other 9 areas! 7

9 Benefits of Using Data Analytics for Internal Audits Benefits to IA: More robust and focused audit evidence and more insight by analysing full populations Pressure to be lean Enhance audit quality Risk assessments and dynamic audit plans Create efficiencies - and consistencies through automation (less manual testing) Audit efficiency and consistency Quality of audit evidence A more focused audit Providing better coverage Identifying large or unusual transactions and items more accurately for further investigation Insight to Management Increased relevance to Management Insight into internal control deficiencies and actual financial impact Combine data to create new insights Identifying trends, exceptions and potential areas for improvement 8

10 Maturity Framework Data Analytics aspects Ad-hoc Repeatable Mature Predictive Type DA DA DA DA Basic use of DA to execute audit procedures Increased use of DA, e.g. dynamic risk assessments, audit planning, dynamic visualisation, etc. Advanced use of DA throughout the IA process cycle Scope DA DA DA DA Basic use of DA, e.g. controls, payroll, SoD Increased use of DA, e.g. P2P, O2C, R2R, Intangibles - controls as well as substantive Advanced use of DA and continuous monitoring/ auditing Audit integration DA DA DA DA Basic use of DA to automate certain aspects of existing audit programmes Increased use of DA through re-designing existing audit approaches and audit programmes Advanced use of DA through a DA-tailored IA strategy and by using end-to-end DA enabled audit programmes Frequency DA DA DA DA DA extracts and analysis once a year DA based on regular, e.g. quarterly, extracts and analyses Maturity of the D&A enabled audit approach Continuous auditing using CMM and (close to) realtime DA procedures 9

11 Are you Ready for Data Analytics? Management is embracing data analytics across the different business areas. Are you as IA function with them on that same journey? 1. Are you providing assurance over such business initiatives and e.g. over reliability of DA solutions? 2. Are you clear on how IA can benefit from DA? Are you able to articulate your requirements to your IT/ BI function? 3. Are you participating - or leading - in the conversations about using a single DA capability across all three lines of defence? Or do you prefer your own capability? 4. Do you have what you need to integrate DA in to your audit strategy, methodology and approach on all business areas? 5. Do you have the skills in your teams to build the right capability, to get DA truly embedded, to execute, and to turn DA results in to insight? 10

12 The Data Process Data strategy Data quality Data management Data remediation / assurance Information strategy Decision and planning modelling Tactical solutions and work arounds Data governance and controls Technology programme assurance Information protection, business resilience and data security 11

13 Payroll - Data Quality 12

14 Payroll - Fraud Risk 13

15 Payroll - Legal Employment 14

16 Stock Analysis For an outdoor goods retailer, we performed analysis of their stock at year end, to understand the stock days for each product. We used a simple tool such as MS Excel PowerView as a stepping stone for developing their data analytics capability. 15

17 Who are the Key Players? 16

18 What are the Challenges of Data Analytics for Internal Audit? A varied business and IT landscape/ mixed maturity Integration, e.g. with business analytics and CCM* Data Security Assurance over DA and CCM* solutions Data extraction Dynamic Stakeholder expectations Knowing where to focus/ to start Alignment between 3 lines of defence Cost of IT solution to the auditor Overload of DA results/ exceptions Interpreting/ Making sense of DA results Skills / combination of skills Quality and Flexibility of DA solutions/ routines Blurring the line between audit and advisor *: CCM; continuous control monitoring, i.e. technology to monitor controls through system settings (General IT and automated controls) 17

19 What causes global warming? 18

20 Correlation vs. Causation 19

21 Without data you re just another person with an opinion W. Edwards Dening 20