---
title: Curated Insight: CCAF’s 2026 Global AI in FS Report
description: CCAF's 2026 report on AI in financial services, highlights risks (data privacy, unreliable outputs) and an alarming lack of bias monitoring.
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---

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 23 Jul 2026

# Curated Insight: CCAF’s 2026 Global AI in FS Report

![Picture of Yusuf Moolla](https://riskinsights.com.au/hs-fs/hubfs/Yusuf_Moolla_Headshot-transformed.png?width=50&name=Yusuf_Moolla_Headshot-transformed.png) [Yusuf Moolla](https://riskinsights.com.au/blog/author/yusuf)

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**TL;DR**

• CCAF (Cambridge), with the BIS, IMF and others, surveyed 628 organisations worldwide on AI adoption and risk.

• Three key risks from industry respondents: data privacy/protection, hallucinations & unreliable outputs, loss of human oversight.

• Most striking: 2/3 of industry respondents are not monitoring for bias.

 

The Cambridge Centre for Alternative Finance (CCAF) has published its [2026 Global AI in Financial Services Report](https://www.jbs.cam.ac.uk/faculty-research/centres/alternative-finance/publications/2026-global-ai-in-financial-services-report/), produced with partners including the Bank for International Settlements and the International Monetary Fund. It covers 628 organisations, including 352 FS industry respondents (the rest are AI vendors and regulators).

It’s 140 pages, interesting but long, although there’s a good summary.

Here are two findings of interest:

### **Industry respondents prioritised 3 risks**

1. data privacy and protection
2. model hallucinations and unreliable outputs
3. loss of human oversight and collective forgetting
   
   (the report defines “collective forgetting” as “A systemic risk tied to automation, where organisations can lose the institutional memory and capabilities to execute processes manually if required.”)

### **A disappointing statistic**

About two-thirds of industry respondents are not monitoring for bias, discrimination or exclusion in their AI. There will be various reasons for this, including the measurement problem from [last week’s article](https://riskinsights.com.au/blog/can-we-use-customers-names-to-infer-ethnicity). And it seems as though only a subset (263) of the industry respondents answered this question, but it’s still quite a striking ratio. (page 103)

 

*Source: The 2026 Global AI in Financial Services Report: Adoption, impact and risks, Cambridge Centre for Alternative Finance, University of Cambridge ([https://www.jbs.cam.ac.uk/wp-content/uploads/2026/05/ccaf-2026-04-28-global-ai-in-financial-services-report-2.pdf](https://www.jbs.cam.ac.uk/wp-content/uploads/2026/05/ccaf-2026-04-28-global-ai-in-financial-services-report-2.pdf))*

---

Disclaimer: The info in this article is not legal advice. It may not be relevant to your circumstances. It was written for specific contexts within banks and insurers, may not apply to other contexts, and may not be relevant to other types of organisations.

---

 

 

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