BBVA has created an artificial intelligence assistant with ChatGPT Enterprise to support data analysis at Internal Audit, from validating and designing tests to interpreting results. „The solution—always with a human overseeing it – enables more consistent use of analytics and is expected to increase productivity by around 10% in audits that call for large-scale data analysis.” – according to the press release. It also reduces manual and repetitive tasks and frees up time for activities where human professional judgment is essential, such as analyzing anomalies and assessing risks.
Internal audit is a key function for ensuring the soundness and trustworthiness of any organization. As a ‘data-driven’ bank, BBVA’s professionals generate immense volumes of data through their work, all of which must be rigorously analyzed by Internal Audit. Meanwhile, regulatory, privacy, and governance requirements continue to escalate.
BBVA’s Internal Audit teams conduct hundreds of audits globally each year, regularly facing the following challenges:
. Analyzing large volumes of data from multiple sources and in heterogeneous formats.
. Designing and running increasingly complex analytical tests.
. Verifying the quality of the data used.
. Properly documenting assumptions, criteria, and professional decisions.
. Ensuring full traceability from the original data through to the final conclusions.
Traditionally, some of these tasks have relied on spreadsheets and/or analysis code created ad hoc by auditors, applying their best professional judgment. However, reliance on individual skills can lead to inefficiencies, increase audit risk, lead to inconsistent judgment across audits, and make analyses harder to replicate.
To address this challenge, BBVA’s Internal Audit team has developed a specific GPT within ChatGPT Enterprise designed to help auditors systematize data analysis. This solution does not replace human judgment but rather fortifies it by guiding auditors within a consistent framework that incorporates best practices in analysis and documentation. As a result, it is expected to help increase productivity by around 10% in each data-driven audit.
The assistant guides auditors step by step through verifying the data initially included for each audit, performing quality and consistency checks, designing specific and complex analytical tests, and interpreting the results. It also generates reproducible code in programming languages commonly used in the audit function, such as Python or SQL, as well as structured analytical explanations ready for auditors to use.
At each stage, the system documents the constraints and record counts used, giving auditors full visibility into the analysis performed by the assistant so they can make well-informed decisions. Validation, professional judgment, and final conclusions always remain in the hands of the auditors, with the assistant acting more as a catalyst rather than an automated decision-maker.
More consistency and more time for what matters
This approach enables more consistent, controlled, and scalable use of data analytics in audit engagements. By smoothing the learning curve for auditors when working with large datasets and reducing time spent on repetitive tasks (such as manual data preparation or documentation), it frees up time for higher value-added activities.
“By reducing manual programming and documentation work and providing a common analytical foundation, we anticipate productivity improvements of around 10 percent in audits that require intensive use of data, as auditors can then apply their professional judgment and identify risks based on robust and comprehensive results,” remarks Carlos Sanz-Pastor, Global Head of Internal Audit at BBVA.
The time freed up can then be devoted to tasks where professional judgment is crucial for improving audit quality, such as interpreting anomalies or assessing risks.
This case goes to show how artificial intelligence, when properly applied and suitably governed, can enhance critical functions—not by automating decisions, but by making professionals’ work clearer, more traceable, and more robust. At Internal Audit, this enhancement equates to broader and deeper validation of the Group’s processes, helping make them clearer, simpler, and more robust (which has a positive impact on the client experience) and making the team more adept at advising on the implementation of the bank’s strategic priorities.
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