---
id: ai-in-finance
title: "AI in Finance: Trading, Risk, and Fraud Detection"
schema_type: TechArticle
category: ai
language: en
confidence: medium
last_verified: "2026-05-28"
created_date: "2026-05-24"
generation_method: ai_structured
ai_models:
  - claude-opus
derived_from_human_seed: true
conflict_of_interest: none_declared
is_live_document: false
data_period: static
atomic_facts:
  - id: af-ai-ai-in-finance-1
    statement: >-
      The Financial Stability Board identifies AI and machine learning as technologies with
      financial-services applications and potential financial-stability implications.
    source_title: Artificial intelligence and machine learning in financial services
    source_url: >-
      https://www.fsb.org/2017/11/artificial-intelligence-and-machine-learning-in-financial-services/
    confidence: medium
  - id: af-ai-ai-in-finance-2
    statement: >-
      The Bank of England and FCA survey reports machine-learning use cases in UK financial services
      such as credit underwriting, fraud prevention, AML, and insurance pricing.
    source_title: Machine learning in UK financial services
    source_url: https://www.bankofengland.co.uk/report/2022/machine-learning-in-uk-financial-services
    confidence: medium
  - id: af-ai-ai-in-finance-3
    statement: >-
      Federal Reserve SR 11-7 provides supervisory guidance for model risk management, including
      model development, validation, governance, and controls.
    source_title: "SR 11-7: Guidance on Model Risk Management"
    source_url: https://www.federalreserve.gov/bankinforeg/srletters/sr1107.htm
    confidence: medium
completeness: 0.9
primary_sources:
  - id: ps-ai-ai-in-finance-1
    title: Artificial intelligence and machine learning in financial services
    type: policy_report
    year: 2017
    institution: Financial Stability Board
    url: >-
      https://www.fsb.org/2017/11/artificial-intelligence-and-machine-learning-in-financial-services/
  - id: ps-ai-ai-in-finance-2
    title: Machine learning in UK financial services
    type: government_report
    year: 2022
    institution: Bank of England and Financial Conduct Authority
    url: https://www.bankofengland.co.uk/report/2022/machine-learning-in-uk-financial-services
  - id: ps-ai-ai-in-finance-3
    title: "SR 11-7: Guidance on Model Risk Management"
    type: government_guidance
    year: 2011
    institution: Board of Governors of the Federal Reserve System
    url: https://www.federalreserve.gov/bankinforeg/srletters/sr1107.htm
known_gaps:
  - Explainable AI in credit decisions
  - Market manipulation by autonomous agents
disputed_statements: []
secondary_sources: []
updated: "2026-05-28"
---
## TL;DR
AI in finance includes machine-learning support for underwriting, fraud prevention, anti-money-laundering monitoring, insurance pricing, trading, and operational workflows. The key quality issue is governance: financial models need validation, monitoring, documentation, and human accountability.

## Core Explanation
Financial institutions use AI and machine learning where large data sets, pattern detection, or prediction are useful. Regulators track both benefits and risks, including model opacity, data quality, bias, operational dependency, and financial-stability concerns. Model risk management guidance remains relevant because an AI system used for financial decisions is still a model that must be governed.

## Further Reading

- [FSB AI and ML in financial services](https://www.fsb.org/2017/11/artificial-intelligence-and-machine-learning-in-financial-services/)
- [Bank of England/FCA ML survey](https://www.bankofengland.co.uk/report/2022/machine-learning-in-uk-financial-services)
- [Federal Reserve SR 11-7](https://www.federalreserve.gov/bankinforeg/srletters/sr1107.htm)