Investec
Data Analytics and AI support for Investecs Internal Audit team.
The Challenge
Investec's Internal Audit function needed technical depth beyond what traditional audit firms provide. Their team was reviewing complex data platform migrations, building analytics capabilities, and assessing AI risks across the bank. The challenge was accessing specialist expertise that could translate technical complexity into audit-relevant insights, without the overhead of hiring full-time data scientists or relying on expensive consultancy retainers.
Our Approach
We provide embedded technical specialists who work as part of the IA team rather than external advisors. The model combines three elements: advisory on specific audit engagements, practical training for building team capability, and on-demand technical expertise when complex questions arise. Advisory work focuses on translating technical complexity into audit-relevant language. When IA teams review data platforms, AI implementations or analytics tools, we provide the specialist knowledge that turns technical architectures into risk and control assessments. The output is findings and recommendations that leadership can act on, not theoretical technical reports. Training builds practical capability across the IA function. Sessions focus on what auditors actually need rather than generic technical courses: understanding data architectures well enough to identify control gaps, asking the right questions during technical reviews, and knowing when specialist support is required. On-demand support means the team has access to technical expertise when they need it. Whether it's reviewing a data migration, assessing AI model risks or understanding analytics frameworks, specialist knowledge is available without recruiting full-time technical staff or engaging expensive consultancy retainers.
The Results
Investec's IA team gained on-demand access to specialist data and AI expertise without recruiting full-time technical staff. Complex technical reviews moved faster because the team had someone who could translate platform architectures, model risks and data flows into audit-relevant findings. Training sessions built practical capability across the IA function. Auditors developed the technical literacy to review data platforms, assess AI implementations and identify control gaps in analytics workflows. The team became more confident auditing technical areas that previously required extensive external support.
What Made This Work
Our specialist works as part of the IA team, available when technical questions arise during audits. Training reinforces what people learn by working on real reviews. We focus on making technical complexity accessible. Data platform architectures, AI model risks and analytics frameworks are explained in audit and control language, not computer science terminology. The result is an IA function that can audit technical areas confidently without constant external dependency.
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