By Amit Srivastav, Executive Director, Morgan Stanley and Dhaval Sheth, Head of Corporate Investigations and Analytics, CIT
What, for you, are the benefits of attending a conference like the Audit Risk Forum and what can attendees expect to learn from your session?
Amit:
Usage of Data Analytics within Audit teams is growing in importance and is an area of focus and a top priority for all firms. The benefit of attending this conference is that practitioners can learn about common challenges and roadblocks in applying data analytics which will beneficial to everyone given the early stage of adoption of data analytics within audit departments. Attendees can hear about common practices that help in the adoption of data analytics as well as some practices to avoid.
There are new opportunities and risks in using data analytics and making the right decision early on is crucial for success and will benefit audit departments.
Dhaval:
The conference will provide an opportunity for the Audit community to come together and share ideas and best practices. It’s always helpful to know how others are identifying and tackling ongoing issues within their business. The attendees should expect to learn the latest trends that impact audit risks, identify emerging audit risks and how to deal with them.
Can you provide a brief overview of the importance of data analytics in auditing?
Amit:
There is a recognition and need from Board and Senior Management that Audit needs to provide more deeper and end to end insights across the Firm. Using data analytics can help Audit departments in performing more holistic assessments and can be used to get thematic insights across a broad dataset instead of a sample based opinion. Also, as Firms get more digital in their controls and processes, it is inevitable that Audit teams will need to follow. Secondly, using data analytics will help Audit teams in establishing independent, data driven continuous monitoring to supplement periodic audits. Third, use of data analytics if done correctly and when mature will allow audit teams to perform the work cheaper and faster.
Dhaval:
Everything today is about how you gather, dissect and consume data. Every business generates large sets of data daily and determining how to analyse data is the key to long term success and sustainability. Data analytics in auditing is a key piece of a company’s audit strategy. The ability to gain insights into the data helps deliver more effective and efficient audits.
In your opinion, how can we look to effectively use data analytics across audit teams at an enterprise level?
Amit:
In order to be effective, data driven usage by Audit teams should be done in an integrated manner and should not be driven by data scientists or analytical teams. The focus for data analytics should be based on an understanding of the key controls and processes that audit teams are concerned about. To start with, Audit teams should focus on known, repeatable and mature processes with large amounts of available data e.g. journal entries and data reconciliations. Also, Audit teams need to set up controls to make sure that the data being sources is protected and secure and not exposed.
In order to be effective, data analytics should not be viewed as something done on the side to support traditional audit testing but it should be the integrated into the overall audit approach and data analytics should be considered as part of the audit objectives/scope decisions.
Dhaval:
Effective use to data analytics across audit teams at an enterprise level is a key factor to successful implementation of a data analytics strategy. Insights gained from one audit area/team can assist other audit areas/teams. Often we find ourselves auditing certain processes that are a piece of the end to end process. Leveraging data analytics from audit to audit helps an auditor put together a complete picture.
What are the key considerations that need to be made when monitoring risks using data?
Amit:
An overall framework needs to be developed instead of doing piece-meal usage of data analytics starting with a developing a strategy and creating a roadmap with tactical (proof of concepts) and strategic plans. The goal should be focus on few key risks at enterprise level and start with practical proof of concepts by integrating business and technology audit teams with the data analytics team.
It is also important to set expectations and not set them too high as getting insight from data analytics will be an iterative process and the results will not be ready when done for the first time. Audit departments need to realize that an initial investment will need to be made in terms of the time and effort involved and success will not be apparent in the first few months and even year.
Dhaval:
When monitoring risks using data, some of the key considerations include: i) ensuring that the data set is accurate and complete; ii) ensuring that the data set is relevant to the exercise at hand; iii) identifying and executing the proper analytic to meet the objectives of the audit test; and iv) having the correct skill set to interpret the results of the analytics. One objective of a data analytics strategy is to ensure a repeatable process. This will help with monitoring key risks on a journey to a continuous auditing model.
What challenges and opportunities can arise from using data analytics when auditing big data?
Amit:
The typical challenges are in rushing into data analytics and doing an ad-hoc approach or just using data analytics to support fieldwork. Using data analytics also needs a re-thinking of audit approach and a different mindset and so training and integrated sessions need to be held and data analytics cannot be just grafted onto audit teams.
There are lot of opportunities using data analytics for audit conclusions; it can help audit teams ask different questions (which was not possible earlier due to lack of data or lack of analytics to process data) and can provide more valuable insights to Board and Senior Management. It can improve confidence in the audit results and also provide clues to the appropriate actions by root cause analysis.
Dhaval:
Auditing big data can seem like a daunting task. The key is the ability to dissect large sets of data to meet the stated objectives. I often find that along with large sets of data, you often get data that you do not need. Staying focused on the objectives of what you are trying to achieve is important. Collaborating with the audit team every step along the way will help the data analyst stay focused on the task at hand. There are tremendous opportunities that can arise from using data analytics including making the audits more effective and efficient.
How do you see the impact of data analytics on auditing evolving over the next 6-12 months?
Amit:
Data driven audit testing will become more pervasive and will widespread. There will be increased reliance and demand to support audit conclusions based on data analytics. This will of course increase demand for data analytics personnel also and audit departments will be competing for the limited pool of talent. Audit departments in future will be using data analytics as a core capability similar to how technology audit teams are viewed now.
Dhaval:
I see more and more audit teams moving towards a defined data analytics strategy. It is important to have dedicated resources who can assist audit teams with obtaining, interpreting and assessing large sets of data. The move towards continuous monitoring and auditing is well under way but still has a long way to go in terms of proper implementation.
