By Saiqa Anne Qureshi, Staff Writer, FWSF MarCom Committee
AI or specifically Generative AI (GenAI) and Machine Learning (ML) are hot topics currently, offering the opportunity to boost efficiency and create value. This includes the financial space and specifically, the impact of GenAI in finance and regulatory efforts.
The impact has already been felt, including work in banking and insurance, generating text, images, audio, and code. It is most prevalent in fraud detection, credit decisions, risk management, and compliance. There is some evidence that AI is now being used for portfolio balance and trading, according to the OECD. While the idea of full end-to-end automation without human intervention is at the development stage, wider deployment could amplify risks. In a survey of 49 jurisdictions regarding the use of AI in finance, the highest use was in customer relations, process automation, fraud detection, and fraud prevention.
There is direct innovation in the GenAI space specifically aimed at financial institutions. This includes the accounting field for tasks such as crafting disclosures and more efficiently summarizing data into a narrative. Auditors can also use the same technology to analyze evidence and create draft flow charts for processes and SOPs. In addition, AI can be effective in supporting peer benchmarking work. However, in all these cases, the work must be checked as GenAI can still hallucinate citations, infer details incorrectly, and replicate bias.
AI is growing in importance in credit scoring and underwriting, where machine learning models can assess creditworthiness, including the use of data sources like utility payments or even consider social media activity, to allow for more accurate risk assessment. While this has the potential to expand credit access to underserved populations, it also has the potential to replicate existing bias in decision making in the banking sector.
There are both benefits and challenges to the use of AI in the finance sector. It has the potential to increase efficiency and reduce operational costs. AI can improve accuracy and reduce human error, as well as enhance fraud detection and more accurately assess risk. It could fundamentally allow for faster and better-informed decision making. However, there are also challenges and risks. There are concerns about data quality and bias, with potentially poorly trained models and biased data leading to discriminatory outcomes. There is unease about security risks and regulatory uncertainly as well as a growing disquiet regarding job displacement, particularly in entry-level roles.
SOURCES
https://www.nationaldaychttps://www.oecd.org/en/topics/sub-issues/digital-finance/artificial-intelligence-in-finance.html
https://www.goldmansachs.com/insights/artificial-intelligence
https://www.workiva.com/landing/ai-adoption-blueprint-how-get-ai-you-actually-need
From Connections Newsletter (Food for Thought): December 2025
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