Market Reports

AI in Banking – Industry Value Chain Analysis

This report provides an end-to-end value chain analysis of artificial intelligence (AI) adoption in the banking sector, covering infrastructure, middleware, applications, risk and governance, operating models, economics, and vendor ecosystems. It examines key drivers such as hybrid cloud patterns, GPU constraints, regulatory pressures (EU AI Act, DORA), and integration bottlenecks. The findings are informed by dozens of structured expert interviews with banking CIOs, risk leaders, and platform buyers, whose insights help validate adoption patterns, challenges, and investment priorities. The study outlines priority use cases, market structure dynamics, ROI benchmarks, and phased strategic recommendations for banks, vendors, and investors. A detailed appendix maps technical layers, representative vendors, regulatory obligations, and KPIs.

Table of Contents:

1. Key Takeaways

2. AI in Banking: End-to-End Industry Value Chain Framework

3. Priority Banking Applications

4. Bottlenecks, Margin Pools, and Control Points Across the Chain

5. Regulation, Risk, and Responsible AI Governance

6. Operating Controls and RACI

7. Phased Compliance Roadmap

8. Market Structure, Economics, and Competitive Landscape

9. Demand and Adoption Signals

10. Build-vs-Buy and Cloud-vs-On-Prem Patterns

11. ROI Benchmarks for Priority Use Cases

12. Strategic Recommendations by Stakeholder and Horizon

13. Expert Interview Insights: What We Heard and Implications

14. Appendix (Value Chain Tables & Vendor Maps)

 

List of Tables:

Table 1 – Value chain layers, core capabilities, and representative vendors

Table 2 – Economics, KPIs, cost drivers, and control points

Table 3 – Regulatory timelines and obligations

Table 4 – AI Risk Management – Three Lines of Defense

Table 5 – Risk heatmap by value chain layer

Table 6 – Demand segmentation by bank tier/use case

Table 7 – Market map and vendor taxonomy

Table 8 – Market map and vendor taxonomy (Horizon view)

Table 9 – KPI scorecard (execution indicators)

Table 10 – Key insights and implications

Table 11 – Evidence-to-action checklist

Vendors/Companies Mentioned:

• Snowflake

• Google BigQuery

• Apache Kafka

• AWS

• Azure

• GCP

• CoreWeave

• OpenAI

• Anthropic

• Cohere

• DataStax

• Milvus

• Pinecone

• Weaviate

• Qdrant

• Arize

• Galileo

• LangSmith

• Fiddler

• WhyLabs

• Datadog

• LangChain

• LangGraph

• Onfido

• Jumio

• Trulioo

• Prove

• Emailage

• IDEMIA

• Parcha

• NICE Actimize

• BAE Systems

• Quantexa

• Lucinity

• Feedzai

• Featurespace

• LexisNexis

• Napier AI

• Verafin

• ComplyAdvantage

• SEON

• BioCatch

• Five9

• NICE

• Genesys

• Talkdesk

• Verint

• nCino

• Blend

• Instabase

• Gateless

• MeridianLink

• Origence

• Temenos

• FIS

• Oracle

• SAP

• Finastra

• Avaloq

• Thought Machine

• Mambu

• Databricks

• H2O.ai

• DataRobot

• Dataiku

• Accenture / Avanade

• IBM / Bluewolf

• TCS

• Wipro

• Cognizant

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