Data analytics is the next big thing for bank internal audit (IA), but internal audit data analytics projects often fail to yield a significant return on investment because many banks run into one or more of the following fundamental challenges during implementation.
Data analytics can introduce a lot of new variables and unknowns in the internal audit process, which might make it difficult to know where to start. Many banks begin with good intentions but lack a logistical project plan that details exactly how they will implement data analytics in a way that solves real problems and advances their business goals.
A practical data analytics project plan should answer questions such as:
Internal conversations can help clarify answers to these questions. Additionally, your team may want to talk with people in other internal audit departments and at peer banks where a data analytics plan is in place to inquire about what they’re doing, how they’re doing it, what’s been successful, and what lessons they’ve learned.
Before starting a large-scale data analytics project, bank internal audit departments need to ask: What do we want data analytics to achieve for us at a high level? If a meaningful answer to this question doesn’t emerge, or if the data analytics you’re implementing don’t serve the answer that emerges, it’s easy to end up with an analytics output that looks nifty but delivers little value.
When the internal audit team at Crowe created our solution for banking data analytics, we asked that high-level question. The answer that ultimately emerged was that data analytics should make internal audits more meaningful and efficient.
From there, we targeted specific elements of the audit process for improvements that would support the larger goal. We decided to move from sampling essentially at random to selecting specific auditable areas, work program steps, and samples that carry a higher risk of error or impact.
Regardless of how a bank approaches the specifics of its data analytics implementation, stakeholders and clients should be involved through an iterative process. Consulting with a diverse group and staying adaptable throughout the data analytics journey can help produce practical solutions that create real value.
Bank internal audit shops tend to run lean, so there’s not a lot of excess capacity available for the next big data analytics project. Most likely, additional resources will be necessary for any large-scale implementation of internal audit data analytics.
Before assessing gaps in resources and expertise, banks need to decide which path to take. One important question to ask: Should we buy a data analytics solution or develop one in-house?
Purchasing and implementing a solution requires time, money, and expertise, but the lift is a lot lighter than developing software in-house. Larger banks with abundant resources or shared services might decide to build out a solution from scratch. Most small and mid-sized banks probably will choose to purchase.
After choosing the best path, banks then need to develop a resource plan and dedicate a team. Once the required resource needs have been assessed, the decision boils down to cost versus benefit. Is it worth committing these resources to have data analytics in play?
If your bank decides to move forward with internal audit data analytics, it might be possible to cut costs through efficiency. Still, your bank ultimately will need to back up the data analytics vision with the resources required to get the job done.
The internal audit data analytics team at Crowe understands these challenges because we tackled them as we created our solution, Crowe Analytics Advisor for banking.
Whatever your bank is looking to accomplish, whether you need to outsource development of a data analytics solution or consult with specialists to help formalize and advance a data analytics project, we’ve got smart, experienced people who would love to step in and assist.
Let’s get in touch and talk about your current internal audit data analytics challenges and goals. You can drop us a line anytime.
