Focus on AI in Investment Banking: Reconciliations

Using AI to Address Data Reconciliation Challenges

Reconciliation in financial services is growing increasingly challenging as the volume of data on which institutions rely continues to grow. Higher data volumes and complexity exert significant pressure on reconciliation processes and technologies, and it is essential that they evolve to keep up with the demand. In this report, GreySpark Partners explores whether AI might provide a solution to these growing challenges.

Published on: 6 Mar, 2026somdn_product_page

Description

In this report, GreySpark provides answers to these key industry questions:

  • Why is reconciliation becoming increasingly critical for financial services firms?
  • What types of reconciliations do financial institutions rely on to maintain data accuracy across systems and the trade lifecycle?
  • How are reconciliation processes traditionally managed, and what role do automation and human expertise play?
  • Where can AI add the most value in the reconciliation process, particularly in data ingestion and exception management?
  • What risks and challenges do financial institutions face when introducing AI into reconciliation workflows?
  • What is the optimal balance between rules-based automation, AI technologies and human oversight in modern reconciliation processes?

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Table of Contents

  • 1.0 Reconciliations in 2026
    • 1.1 Why Reconciliations Matter
    • 1.2 How Reconciliation is Traditionally Achieved
  • 2.0 Why Reconciling Data is a Challenge Today
    • 2.1 Risks of Poor Reconciliation Systems and Processes
    • 2.2 Why Legacy Solutions Can Be Lacking
  • 3.0 Technical Solutions from Third Parties
    • 3.1 Current Deployments of AI to Enhance Reconciliations
    • 3.2 The Risks of using AI for Reconciliations
  • 4.0 Practical Solutions
    • 4.1 Replication of Human Agents
    • 4.2 AI Reconciliation in the Trade Lifecycle
  • 5.0 Appendix
    • 5.1 Table of Figures