Which of the following statements best describes the evidence collection process carried out by the audit team at Finalogic? Refer to Scenario 4.
Scenario 4: Finalogic leads the application of artificial intelligence in the financial services sector, which is used to improve risk assessment, fraud detection, and
customer service. The company has implemented an artificial intelligence management system AIMS based on ISO/IEC 42001 to ensure operational quality, ethical Al
use, regulatory compliance, and transparency, allowing for consistent oversight and structured governance.
This month, Finalogic is undergoing an audit to obtain certification against ISO/IEC 42001, a critical step in demonstrating its commitment to responsible Al. To
evaluate Finalogic's conformity to the audit criteria, the audit team adopted a comprehensive, evidence-based approach. The gathered evidence ranged from analyses
of unquantifiable information to analyses of samples related to determining the audit criteria-including internal reports generated by Finalogic's own Al system-which
assert successful integration and compliance with the standard.
Additionally, presentations by the company’s Al team during the audit highlighted the system’s success in customer service enhancements and fraud detection,
emphasizing improved efficiency, decision making accuracy, and user trust. An evaluation report prepared by an independent third party firm specializing in Al systems
also provided an objective review of Finalogic's AIMS. It assessed the system's effectiveness, bias, and compliance through a thorough examination.
During the audit, the audit team applied the same level of effort and utilized the same techniques across all audit areas, regardless of their risk level. This strategy
ensured a consistent and thorough evaluation of the AIMS, uncovering any latent weaknesses or inefficiencies that might otherwise go unnoticed.
Despite Finalogic's advanced AIMS and adherence to ISO/IEC 42001 for ethical Al practices, there remains a risk of Al algorithms inadvertently perpetuating bias or
making inaccurate predictions due to unforeseen flaws in training data or algorithmic models. This could lead to unfair loan rejections or approvals, potentially causing
financial losses or damaging the company’s reputation for fairness and accuracy in its financial services. By acknowledging these risks. Finalogic remains committed
to refining its Al governance, implementing bias mitigation strategies, and enhancing transparency to uphold its reputation as a leader in Al driven financial services.
