- 1.0 Assessing the Investment Banking Industry AI Applications Utilisation Landscape
- 1.1. Assessing the AI Applications Landscape: Achieved Cost Savings, 2019 to H1 2021
- 1.2. Assessing the AI Applications Landscape: Profitability (‘Value-add’) Improvements, 2019 to H1 2021
- 1.3. Assessing the AI Applications Marketplace: Achieved Competitive Advantage Differentiation, 2019 to H1 2021
- 2.0 Mapping Investment Banking Industry AI Applications & Use Cases in 2022
- 2.1. Buy & Build Trade Automation: Front-office Risk Management & Trading Applications
- 2.2. Financial Crime Prevention: Anti-money Laundering & Fraud Prevention Applications
- 2.3. Intelligent Automation: Combining the Benefits of AI, Machine Learning & Robotic Process Automation Applications
- 2.4. Post-trade Automation: Middle- & Back-office Trade Processing, Reconciliation, Reporting and Settlement Applications
- 3.0 A Strategic Framework for Ideating & Qualifying Investment Banking Industry AI Use Cases
- 3.1. Setting the Table: The Necessity of Effective Design Process Thinking
- 3.2. Developing a Proof of Concept: Ideating, Selecting & Scaling Applications
- 4.0 Appendices
- 4.1. Table of Figures
A Framework for Ideating & Qualifying Solutions to Solvable Problems Created by Machines
This report presents an analysis of the global investment banking industry’s on-going attempts to develop and incorporate in-house built and technology vendor-provided software solutions capable of producing artificial intelligence or AI-like outcomes across a variety of wholesale capital markets front-, middle- and back-office business and IT operational areas.
