Digital Transformation: Artificial Intelligence Applications & Use Cases in Investment Banking 2022

£1,650.00

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.

 

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This report explores the challenges that investment banks face in utilising AI solutions across four of the most important and current use case categories:

  1. Buy & Build Trade Automation Solutions AI Use Cases;
  2. Financial Crime Prevention Solutions AI Use Cases;
  3. Intelligent Automation Solutions Use Cases; and
  4. Post-trade Automation Solutions AI Use Cases.

To assist in overcoming these challenges, this report also presents a GreySpark-developed framework to support AI applications use case ideation processes within investment banks such that they can be qualified before they are fully developed to maximise the potential of a successful implementation or integration effort.

Published on: 14 Apr, 2022
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Description

  • 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

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