Artificial Intelligence (AI) in Financial Services: Strategic Intelligence

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This report provides an overview of the current state of artificial intelligence (AI) innovation in the financial services industry. It examines the forecasted size of the AI market, delving into specific types of AI spend (such as generative AI, computer vision, and agentic AI). The report lists different ways of utilizing AI to combat fraud, cyberattacks, evolving regulation, bad user experience, growing volumes of transactional data, and new attack vectors. It provides new case studies of innovative uses of AI within the industry, while providing company profiles of the leading AI adopters and specialist vendors across the insurance, banking, payments, and wealth management industries.

AI adoption in financial services has ramped up significantly in the last few years. Financial services firms have long adopted generative AI tools for customer service and improved decision making, yet sustained direct profitability remains far off. The recent boom of agentic AI investment has necessitated financial services companies to reconsider all of their AI pilots, to govern deployments like product launches: require return on investment gates, scale what demonstrably pays, and sunset what does not. Financial services firms must adopt AI to improve cybersecurity and fraud prevention in the face of adaptive cyberattacks, new attack vectors, and rising insider threats.

Scope

GlobalData estimates the total artificial intelligence (AI) market will be worth $642 billion by 2029, up from $131 billion in 2024 at a compound annual growth rate of 37.4%. Insurers, banks, and financial markets are expected to spend over $87 billion in 2029, up from just over $18 billion in 2024.

According to the Bank of England, 75% of UK financial firms use AI, with 33% of adopters using the technology specifically for fraud detection. AI fraud detection systems have intercepted up to 92% of fraudulent activities before transaction approval in some cases, while Mastercard claims it has improved fraud detection rates by up to 300% using AI.

After years of machine learning and a recent generative AI surge, autonomous agents promise the end-to-end automation that finally turns AI into a scalable profit engine for the FS industry. By automating complex workflows (including onboarding, compliance checks, data pipeline creation, fraud detection, and real-time risk scoring), agentic systems cut costs and free talent for higher-value work.

Reasons to Buy

Understand key developments in the AI space.

Identify the strategies of industry leaders and competitors.

See the impact of using AI to combat cyberattacks and new fraud attempts.

Learn how banks are using AI to comply with evolving regulations.

Find specialist vendors for specific AI needs.

See the next steps for your AI strategy.

Develop an understanding of how to get return on investment from AI pilots.

Accenture
Aon
Ant Group
Appian
Applied Systems
Atlantia
AXA
BBVA
Bank of America
BlackRock
Braintree
Bud
Cape Analytics
Claim Genius
Citigroup
Citco
Clover
Cobase
Covea
Cytora
Databricks
DeepFrauds.ai
Dell Technologies
Deloitte
Dufrain
Eika Gruppen
Eltropy
Emergent
Enterprise Singapore
eFront
EvolutionIQ
Fenergo
Featurespace
Fiserv
FiVerity
Floodbase
Goldman Sachs
Google
H2O.ai
Haodf
Hewlett-Packard Enterprise
HSBC
Humn.ai
ICBC
Impress.ai
Infocomm Media Development Authority
JPMorgan Chase
Kasisto
Kodak Alaris
Landbase
Lemonade
Liberty Mutual Re
Mastercard
McGill and Partners
Microsoft
Mitchell
Moody's Corporation
Munich Re
NatWest Group
Nvidia
NICE Actimize
Nippon Life Insurance
Norm AI
OpenAI
Pay.UK
Ping An Insurance
PKSHA Technology
Prophecy
Propel Ventures
Quantee
Quantexa
Regula Forensic Science
Revolut
RBC
Salesforce
Simudyne
Sensity AI
Shift Technology
Snorkel AI
Solera
Sprout.ai
Steadily
Swiss Re
Temenos
TD Bank
The Hartford
Tokio Marine
Tokio Marine Kiln
UOB
UBS
Veridas
Verizon
Visa
Wells Fargo
West Bend Insurance
WorldFirst
ZestyAI
Zurich Insurance

Table of Contents

Table of Contents

1. Executive Summary

2. Players

3. Value Chain

4. The Impact of AI on Financial Services

5. Industry Analysis

6. Case Studies

7. Companies

8. Sector Scorecards

9. Glossary

10. Further Reading

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