Implementing Analytics in Internal Audit. Jordan Lloyd Senior Manager Ravindra Singh Manager

Transcription

1 Implementing Analytics in Internal Audit Jordan Lloyd Senior Manager Ravindra Singh Manager

2 What does Success Look Like To deliver successful analytical insight as an everyday part of the audit process means IA must focus more broadly than on data and technology. The goal is to develop cost effective solutions that are targeted and underpin the audit process to achieve a more efficient and effective audit delivery model. Becoming analytics-enabled relies on the fundamental building blocks of People, Process, Data and Technology being informed by an Analytics Strategy. This enables the embedding of analytics into the audit lifecycle, the focusing on the right risks at the right time, while aligning analytics to the IA strategy and value drivers of the business.

3 Benefits of Analytics Perform the same audit faster Perform the same audit cheaper Perform more targeted audits Make innovation a centerpiece

4 Maturity of internal audit analytics 4

5 Planning the journey Building Blocks Key Questions to Consider Strategy Process Data Analytics vision for audit services Identifies key projects/programs, stakeholders and metrics Aligned with corporate and information technology strategies Organizational structure and program sponsor/manager People Capability / competency acquisition or development Collaboration and communication planning Operating model for developing and consuming insights Analytics prioritization and project management Methodologies and approaches for analytics Solution requirements, evaluation, selection and set-up Technology Technology vendor / license management Technology architecture and solution optimization Data acquisition and enrichment Data model architecture Data governance and security Building a sustainable analytics function requires a foundation of the fundamental building blocks of People Process, Data and Technology, informed by an Analytics Strategy. Who is the accountable IA owner? What organisational structure do we need to put in place to support our analytical strategy? Who do we need to engage in other departments and what are their roles? What other talents do we need and what is the plan for getting them? How do we identify the right projects on which to focus our efforts? What are the steps we need to take to ensure that these projects are a success? How will we comply with relevant regulations? What are the risks and how do we mitigate them? How will we measure our progress and test the validity of the insight? What data do we need to answer the important questions? From where is it sourced internal, external, licensed, open? How do we bring it together and what are the challenges in transforming, linking and publishing it? What about quality and accuracy? What tools do we need to process the data? How do we scale up the technology when we need to roll out to the rest of the function? Do we use a self-service or bespoke solution model? 5

6 Organizing for Success Operating Models Interaction Models = Analytics capability 6

7 Multi-dimensional team 7

8 Multi-Disciplinary Approach 8

9 Audit Interaction Model The data analytics team supports the overall IA mission through the successful deployment of team resources at the engagement level. For engagements that require analytic support, there are numerous instances where data specialists and analysts can influence processes to yield successful audits. The following graphic demonstrates the high-level timeline and key interactions for audit analytics. 9

10 CRISP DM: The Model Development Framework What is the operating culture? Where have controls failed in the past? When is data captured in the system? How can data be merged and cleansed in order to establish records to analyze? Analytics Process Business Data Data Modeling Evaluation Deployment Understanding Understanding Preparation Analytics Tools /Solutions What analytic techniques can be used to identify known high risk scenarios in the data? Are there other scenarios that look similar? Which analytic techniques implemented are most reliable in identifying potential risks in the data? How can analytic techniques be leveraged to identify potential risks on an ongoing basis? What does the end-state solution look like? What are the key business processes in the operation? What are the vulnerabilities? Where is data stored and in what format? Materials Master Requisitions Purchase Orders What analytic techniques can be used to identify potential unknown scenarios of control failures in the data? Movements FAMILIARIZE ANALYZE OPERATIONALIZE

11 Reducing false positives with advanced Analytics A noticeable ROI from advanced analytic techniques is the reduction in cost, time and effort, to review transactions and assess if they are truly of concern Model maturity development 99.99% 99.5% 99.25% Random Sampling General Rules Tailored Rules False Positive Rate 60% Risk Aggregation 30% 25% Profile Risk Aggregation Machine Learning Unusual Transactions Identified per period 11

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