Banks are like many other organizations in that they are adept at producing data yet often fall short on interpreting and applying its value. But for banks in particular, analytics are incredibly important. The ability to read and interpret collected intelligence is critical to a sound internal audit (IA) function.
An independent and objective IA function is the most reliable way to validate, interpret, and analyze bank data. Typically, an IA process involves compiling data ranging from customer transactions to day-to-day employee activities. The findings can help banks to segment and understand the effects of different types of data on their business. And understanding how to apply data to strengthen internal controls is critically important for minimizing risk.
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When data analytics is applied to auditing practices, it can increase risk coverage, make the audit cycle more efficient, offer real-time data, help auditors manage risks and build a more collaborative organization.”
- Duncan Barnard, managing director of risk assurance for Price Waterhouse Coopers
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Using analytics within IA can serve as an early warning detection system by providing management a high-level view of the organization. For example, analyzing differing NSF fee practices by branch or region can inform decisions about building consistencies in waiving fees. They can also help to improve operations significantly. In fact, combined with traditional principles and techniques, the IA data analytics function produces unprecedented clarity about the banking environment.
Let’s look at two specific areas where a strong IA function can have a measurable impact on the security and financial strength of your bank.
Fee Income
Fee income can come from overdraft fees, service charges and loan fees. These streams should be analyzed across the organization (e.g., branch, region, regional president, loan officer). The IA function is useful for uncovering policies and procedures that are (and are not) being engaged, impacting the bank’s ability to generate this important source of revenue.
The chart raises several questions. What is Branch 6 doing to create such a disproportionate amount of revenue? Could best practices be gleaned from this branch to improve revenue company-wide? Or are there factors at play at this branch (i.e., fraudulent transactions) that warrant deeper examination? Conversely, why is Branch 14 lagging? What is their customer base? What are the opportunities to increase their service charge revenues?
Data like fee income helps banks determine what types of accounts they should use, based on the amount of revenue they are generating. The analytics are useful for increasing revenue production as well as pointing out weaknesses.
Fraud Risk
The IA function is useful for both proactive and forensic fraud detection. For example, the large disparity in earnings indicates the possibility that Branch 6 could be doing something fraudulent to increase service charge revenue. By engaging a structured IA process, the bank would have a way to generate the intelligence to spot and act quickly on any illegal activities.
Banks are held accountable for loss when they neglect to conduct “due diligence” in fraud cases. This is particularly true of organizations that overlooking noticeable fraud issues in wire transfers. It is important to run analytics on accounts with high funds or volume, customers with a large number of companies attached to them, or customers with an excessive amount of overdraft fees.
For banks facing regulations, evolving customer demographics, expanding competition, and pressing economic factors, an independent and objective IA function gives your bank the ability to delve deeper into data and to uncover intelligence that perhaps was not clear upon cursory examination. In short, if your bank is seeking to strengthen the bottom line, build security, and establish competitive strategies, an analytics-driven IA function is the starting point.
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