Financial Services — IT Controls, Audit, and Data Integrity

The challenge

Financial services organisations deploying AI systems face overlapping regulatory obligations — from internal audit requirements to emerging AI-specific frameworks including the EU AI Act and NIST AI RMF. Many mid-market firms lack the in-house expertise to assess their exposure, classify their AI systems accurately, or design controls that will withstand scrutiny.

Common gaps include undocumented AI model inventories, no formal risk classification against regulatory frameworks, weak data governance for AI training sets, and insufficient human oversight mechanisms for automated decision-making systems.

Our approach

VeridianTech Co. applies the same structured methodology used at Big 4 engagements across the financial sector:

• AI system inventory and classification: Cataloguing all AI systems in production or development and classifying each against the EU AI Act's risk tiers and NIST AI RMF categories.

• IT general controls assessment: Reviewing access management, change management, operations, and security controls using industry-standard frameworks including COBIT and ISO 27001.

• Automated controls testing: Using ACL and data analytics tools to test controls at scale, identifying exceptions and anomalies that manual testing would miss.

• Data integrity review: Assessing the quality, completeness, and reliability of data used in AI and automated decision-making systems.

• Governance framework design: Building policies, roles, escalation procedures, and monitoring processes tailored to the organisation's regulatory context.

Experience base

Prior engagements at EY and KPMG included IT audit and controls testing for Lloyds Banking Group and Prudential PLC, among others. VeridianTech Co. brings this institutional experience to clients who need the same quality of assessment without the associated fees.