New CFA Institute Report Urges Financial Sector to Prioritize Explainability in AI Adoption

As financial firms accelerate the adoption of artificial intelligence (AI), opaque decision-making systems could undermine public trust, regulatory compliance, and risk management warns a new report from CFA Institute, the global association of investment professionals.

The report, titled Explainable AI in Finance: Addressing the Needs of Diverse Stakeholders, examines the growing complexity of AI systems such as those used in credit scoring, investment management, insurance underwriting, and fraud detection. It makes the case for "explainable AI" (XAI), a class of techniques designed to make AI decision-making transparent, auditable, and aligned with human understanding.

“AI systems are no longer working quietly in the background—they are influencing high-stakes financial decisions that affect consumers, markets, and institutions,” said Dr. Cheryll-Ann Wilson, CFA, the report’s author and a senior affiliate researcher at CFA Institute. “If we can’t explain how these systems work—or worse, if we misunderstand them—we risk creating a crisis of confidence in the very technologies meant to improve financial decision-making.”

The report emphasises that different stakeholders—regulators, risk managers, investment professionals, developers, and clients—require different kinds of explanations. By mapping specific explainability needs to distinct user roles, the study introduces a novel framework to embed transparency into AI deployment across the financial value chain.

The report also breaks down the different types of tools used to make AI decisions more understandable. Some of these tools are built into the AI system from the start; these are known as “ante-hoc” methods, and they use simple, transparent rules that humans can easily follow. Others are applied after the AI has made a decision. These “post-hoc” tools help explain what influenced a particular outcome, using methods like highlighting which data points mattered most, or showing how a decision might have changed if certain details were different: for example, if a borrower had a higher income. The report shows how these tools are being used in areas like risk assessment, investment decisions, and for meeting regulatory requirements.

Among the report’s key recommendations are:
•    Developing global standards and benchmarks for measuring the quality of AI explanations.
•    Tailoring XAI interfaces to meet the needs of both technical and non-technical users.
•    Promoting real-time explainability in AI systems used for fast-paced financial decisions.
•    Investing in human–AI collaboration, including user training and redesigned workflows.

The report also explores cutting-edge alternatives, such as evaluative AI, which presents evidence for and against decisions to reduce automation bias, and neurosymbolic AI, which combines logical reasoning with deep learning to improve interpretability.

As regulatory momentum builds, with frameworks like the EU AI Act and the UK’s own regulatory initiatives in development, CFA Institute calls on financial institutions to move proactively.
“This is not about slowing down innovation; it’s about implementing it responsibly,” added Rhodri Preece, Senior Head of Research at CFA Institute. “We must ensure that AI systems not only perform well but also earn the trust of those who rely on them.”

A full copy of the report - Explainable AI in Finance: Addressing the Needs of Diverse Stakeholders - is available to download here.