The Investment Accounting Shift for Private Markets & AI

Key Takeaways 

  • Key Takeaways (TBD based on final approvals)

With unprecedented growth of private markets in recent years, the need for unifying investment data to achieve a total portfolio view across public and private holdings has become paramount for heads of investment operations at asset managers and asset owners. Chief operating officers are re-architecting their accounting foundation to build a data-centric strategic decision layer able to be leveraged by AI and convert accounting insights into competitive advantage.

Nearly 70% of asset managers cite data quality as the primary barrier to AI adoption, making achieving a unified ledger the critical enabler of AI insights. [1] Additionally, the challenges firms must address regarding opaque and unstructured private markets reporting data, fragmented accounting systems, and expanding global regulations continues to strain traditional investment workflows designed for liquid, publicly-traded instruments.

The barriers are well documented: entrenched organizational silos that resist data sharing across teams; technical debt from legacy systems that were never designed for real-time, multi-asset accounting; leadership misalignment on investment priorities; and cultural resistance to change that keeps firms anchored to familiar processes.

Unifying the Books of Record: IBOR and ABOR

Why a single multi-currency, multi-basis ledger matters

Firms operating siloed IBOR and ABOR systems are trapped managing reconciliation overhead and data gaps that lead to delayed decision-making. Unifying books of record on a single multi-currency, multi-basis ledger is the prerequisite for a true total portfolio view and real-time decision-making, because it eliminates the reconciliation lag between what the front office sees and what the official books record. The unification is technical consolidation that creates the foundation making downstream capabilities such as multi-basis reporting, AI-driven insights, and regulatory readiness possible.

Private Markets and Limited Partnership Lifecycle Accounting

Tracking the LP lifecycle

As private market holdings grow, firms are also grappling with tracking LP lifecycle events — commitments, capital calls, distributions — in the same system as public markets accounting, creating fragmented total portfolio views and manual workarounds. The solution is to integrate limited partnership lifecycle accounting into the same ledger as public-markets accounting, tracking initial commitment, funded and unfunded commitments, capital calls and distributions as first-class data objects on a unified platform. This integration approach can help decrease the amount of manual reconciliation between private and public holdings while also providing operations leaders a single, authoritative view of committed and funded capital across the entire portfolio.

Multi-Basis Regulatory Reporting on a Single Ledger

GAAP, IFRS, statutory and tax in one transaction

The expanding and varied global regulatory landscape for the private realm presents another universal challenge for many firms. Alternative Investment Fund Managers Directive (AIFMD) II Annex IV in the European Union (effective April 2026); Securities and Exchange Commission (SEC) reporting requirements in the United States; Financial Conduct Authority oversight in the United Kingdom; Monetary Authority of Singapore regulations and other jurisdictional frameworks make regulatory requirements cumbersome as each dictate different data fields spanning portfolio, investor, risk and fund accounting systems. Maintaining separate books for each reporting standard also creates duplication, inconsistency and potential audit risk.

The solution: a single transaction that can support Generally Accepted Accounting Principles (GAAP), International Financial Reporting Standards (IFRS), statutory and tax bases simultaneously on one ledger. Each transaction would theoretically be tagged once at the source, and the platform generates reports across all required bases without duplication or reconciliation between parallel ledgers. This approach potentially reduces regulatory risk, maintains consistency across filings and transforms compliance from a reactive, labor-intensive process into a byproduct of a well-architected accounting foundation.

AI-Enabled Accounting: From Efficiency to Insight

Intelligent document processing for private markets data

Once the fragmented data silos have been addressed in the accounting foundation, optical recognition technology can be utilized to extract structured data from the unstructured PDFs that private-markets investors receive from general partners. Automated metadata classification further streamlines document ingestion. These capabilities directly address the data-quality barrier most asset managers identify as their primary obstacle to AI adoption. The unified accounting ledger is the critical enabler, because AI insights are only as reliable as the data foundation beneath them.

“It’s not enough to say we’re using AI to improve efficiencies. It is important, and efficiency gains matter. But really, the next focus is to be able to generate novel insights, on-demand, for our clients by combining their data with ours.”
Jay Hinton
Global Head of Product for Data and Analytics

The Case for Acting Now

Regulatory deadlines are arriving, AIFMD II takes effect in April 2026 and T+1 settlement is already live in the U.S. with Europe soon to follow. Private markets growth continues to accelerate and the data-architecture work most firms need to be doing is not a next year problem because 2027’s filings will depend on data captured from April 2026 onward.

Firms that delay re-architecting their accounting foundation risk compounding reconciliation debt, missed regulatory deadlines and an AI-readiness gap that becomes harder to close. By contrast, establishing a unified accounting layer can provide the connective tissue between investment decisions and operational execution.

1. Mercer, 2026 asset manager survey (cited via FINBOURNE): 69% of asset managers cite data quality/access as the primary barrier to AI adoption. 

2. PwC, 2025 Global Asset & Wealth Management Report: https://www.pwc.com/gx/en/news-room/press-releases/2025/pwc-2025-global-asset-wealth-management-report.html 

3. PwC, Private assets operations at a turning point (2026): https://www.pwc.lu/en/press/press-articles-2026/private-assets-operations-at-a-turning-point.html 

4. EU Directive 2024/927 (AIFMD II): https://eur-lex.europa.eu/eli/dir/2024/927 

5. ESMA, AIFMD Reporting: https://www.esma.europa.eu/data-reporting/aifmd-reporting 

6. U.S. Securities and Exchange Commission (SEC): https://www.sec.gov 

7. Financial Conduct Authority (FCA), UK: https://www.fca.org.uk 

8. Monetary Authority of Singapore (MAS): https://www.mas.gov.sg 

9. BNY accounting solutions webpage: https://www.bny.com/corporate/global/en/solutions/platforms/data-and-analytics/accounting-solutions-investment-management.html 

10. Relational AI podcast featuring Jay Hinton, Global Head of Product for Data & Analytics at BNY.

Related Platforms

Data & Analytics
Cloud-native and AI enabled platform for data and operations.
Investments
Delivering global expertise and solutions for investors.

BNY is the corporate brand of The Bank of New York Mellon Corporation and may be used to reference the corporation as a whole and/or its various subsidiaries generally. This material and any products and services may be issued or provided under various brand names of BNY in various countries by duly authorized and regulated subsidiaries, affiliates, and joint ventures of BNY. This material does not constitute a recommendation by BNY of any kind. The information herein is not intended to provide tax, legal, investment, accounting, financial or other professional advice on any matter, and should not be used or relied upon as such. The views expressed within this material are those of the contributors and not necessarily those of BNY. BNY has not independently verified the information contained in this material and makes no representation as to the accuracy, completeness, timeliness, merchantability or fitness for a specific purpose of the information provided in this material. BNY assumes no direct or consequential liability for any errors in or reliance upon this material.

Business-specific disclaimers: None identified for BNY Corporate Insights web publication. Content owner to review for any AI-related qualifying language — the article references BNY’s Eliza AI platform and AI Hub as directional/roadmap capabilities; confirm no additional AI disclaimer is required with legal/compliance.

 

TAGS
  • Artificial Intelligence

READY TO TALK ABOUT
WHAT'S NEXT?

Learn more about our platforms and solutions