Report: 'AI and the Transformation of Asset Management Governance'

Spread of generative AI raises shadow usage and security threats

Integrated management of data, regulatory and third-party risks required

"AI and the Transformation of Asset Management Governance" report [Provided by Samil PwC]
"AI and the Transformation of Asset Management Governance" report [Provided by Samil PwC]

By Ahn Hyo-jung, The Herald Business

AI adoption is accelerating rapidly across the financial industry, extending the technology's influence into core decision-making areas such as investment judgment and risk management. Yet existing internal control frameworks and accountability structures are no longer sufficient to manage that influence, making the urgent development of a new governance model necessary, according to a new analysis.

Samil PwC released a report Thursday titled "AI and the Transformation of Asset Management Governance: Changes in Operational Structure and Response Strategies."

The report finds that the introduction of generative AI and large language models has pushed AI well beyond conventional structured-data analysis, enabling it to interpret unstructured information, draft documents and support investment decisions — directly shaping actual decision-making processes. As a result, a fundamental overhaul of human-centered operational structures and approval and accountability frameworks has become unavoidable.

The report particularly identifies "shadow AI" — employees using AI services without official company approval — and supply chain risks stemming from growing dependence on external AI services as among the most pressing governance challenges facing the asset management industry.

The report said data governance and a human-in-the-loop decision-making framework are the two pillars for addressing these risks. In an environment where internal data, external data and diverse AI services are increasingly intertwined, a data-centric control system that consistently tracks data sources, usage scope and movement is essential, it said. For high-impact areas such as investment decisions and customer-related judgments, firms should not rely solely on AI outputs but must ensure accountability through human review and approval processes, it added.

To that end, the report recommended that firms build enterprise-wide governance covering six areas: establishing AI usage policies and operational standards; building data governance centered on tracking data sources, quality and lineage; strengthening human-review-based decision-making frameworks; setting up AI usage logging, monitoring and audit-response systems; advancing security and access control frameworks; and overhauling management systems for external AI services and supply chains.

"The competitiveness of financial firms going forward will be determined less by AI adoption itself than by how responsibly and reliably they can manage it — in other words, by their AI governance capabilities," said Jeong Hae-min, a partner at Samil PwC's AX Node practice. "As AI is posing new challenges to existing operational structures and internal control frameworks, building an integrated, enterprise-wide management system is urgent."

Meanwhile, Samil Accounting Corp., whose fiscal year ends in June, posted sales of 1.1094 trillion won (approximately $729 million) and operating profit of 25.4 billion won for fiscal year 2025 (July 2024–June 2025).


an@heraldcorp.com