Traditionally, verifying hedge fund records has required independent verification in the form of a manual audit, but that’s changing thanks to blockchain technology and artificial intelligence. These technologies are arriving at an apt time – just as the Securities and Exchange Commission is starting to question hedge fund managers’ track records.
According to Sean Wilke, head of Growth Strategy at IQ-EQ, the issue of investment performance is increasingly in the crosshairs of the Securities and Exchange Commission.
“The staff of the SEC has identified numerous ways in which fund sponsors can manipulate performance in order to inflate their numbers, especially when marketing ‘composites,’ which are collections of funds, portfolios, sleeves and/or accounts managed pursuant to the same strategy,” he told Hedge Fund Alpha via email. “Inappropriate trade allocation practices and cherry-picking are consistently fodder for enforcement actions.”
IQ-EQ acts as a consultant operating on the fringes of dozens of examinations at any given time, and Wilke said they generally see a sense of comfort from examiners when a manager can demonstrate that their numbers have been independently verified.
Blockchain and AI being used to verify data in various ways
Blockchain technology and AI are coming to the rescue for fund managers in a variety of ways when it comes to data verification. Angie Walker, head of Commercialization at Apex Group, says fund managers are increasingly leveraging blockchain and cryptographic proofs.
“By tokenizing fund units and recording key data — such as subscriptions, redemptions, and NAV updates – on a blockchain, they can create an immutable, time-stamped record of transaction,” Walker said. “… A growing focus is on on-chain identity, which can represent both assets and stakeholders directly on the blockchain. To start, tokens can be deployed to both represent an asset as well as each stakeholder’s position within the asset. From there, stakeholders are linked to a verified on-chain identity, confirming their eligibility to hold a given asset. Finally, each asset can be tied to a smart contract “AssetID” that acts as a definitive record, with authorized parties – rating agencies or administrators – able to certify information directly within it.”
Walker also said AI is a powerful complement to blockchain for data verification. She explained that machine-learning models can cross-check on-chain fund data with off-chain records, automatically flagging anomalies in positions, valuations or investor transactions.
“AI can also enhance investor onboarding by verifying KYC/AML data against multiple trusted sources at scale, improving both speed and compliance,” Walker added. “When applied to tokenized assets and on-chain identities, AI can interact with certified blockchain data to surface insights, detect patterns, and even recommend next steps. Meanwhile, AI agents could propose investment opportunities, optimize portfolios, or perform transactions.”
How blockchain is being used to verify track records
Part of data verification at hedge funds involves being able to prove track records. In fact, some fund managers are already using blockchain technology to verify their performance. For example, validityBase helps validate track records and results, proving that a particular model or algorithm works – or showing if the results were gotten another way.
“We realized there actually is a way to distinguish the two,” Averbukh told Hedge Fund Alpha in an interview. “And the way you can do it is by allowing somebody to create a live record of their predictions or of their data as they’re doing it… And from the perspective of an allocator or from the perspective of somebody evaluating my predictive data or my predictive model, it’s almost as if I’d be presenting them with a live movie of the data.”
Using validityBase’s tools, managers can create a fingerprint of their portfolio or predictive data in such a way that no information about the portfolio is revealed, but it can be uniquely tied back to it. The tools then create an independently verifiable timestamp by writing that fingerprint to a blockchain. This can be especially useful for managers without an auditor.
“For folks who don’t have an audited track record, there’s not really a great way to credibly present that information, and the economics of it are such that building an audited track record costs about $50,000 per year per strategy, and that’s if you’re really cutting the cost of the bone,” Averbukh added.
How AI is helping managers verify track records
In addition to blockchain technology, AI is also being used to verify fund managers’ performance, particularly those that lack the infrastructure needed to verify their track records. Dan Miller, senior managing director of Outsourced Middle Office Services at IQ-EQ, explained how AI is being used to track performance.
“Managers that don’t have the proper infrastructure in place have the difficult task of taking a shoebox full of trade confirms and compiling it into a track record of daily performance, recreating tax lot relief methodology and manually pulling prices from a terminal,” he said. “In the days of yesteryear, this would be a summers-long project for an unlucky analyst. Now AI is making strides at streamlining and automating a once exceedingly manual workload.”
Although Miller said we haven’t yet reached the point where managers can “turn AI loose” to independently calculate their returns, he added that the technology is certainly cutting down the amount of effort involved – and AI is only getting better and better at doing this.
The problems with commercial databases
Track record verification is also important in an age of commercial databases that are supposed to track hedge fund performances. Many allocators rely on such databases, but Jon Caplis of PivotalPath highlighted the issues with them – demonstrating the need for new technologies to verify hedge funds’ track records.
“Commercial databases cobble together unreliable fragments of data scraping inconsistent and unclear regulatory filings rather than sourcing directly funds,” he said. “They misuse FOIA requests for performance data that can be misinterpreted without verification or the fund’s knowledge and consent. Many commercial databases have internal media arms whose in-house journalists recycle and amplify the same flawed data generating further mistrust across hedge fund managers.”
As a result, Caplis said many of the best-performing funds choose not to report their performance, which reinforces the incompleteness of commercial data sets used to create indices.
“Indices riddled with negative selection bias do not accurately reflect hedge fund performance, which not only misinforms investors but systemically reduces allocations to hedge funds,” he added.
The future of blockchain and AI in data verification
As time goes on, blockchain and especially AI technology is improving dramatically, which only serves to improve the prospects for data verification in the hedge fund industry. Looking forward, Walker expects blockchain and AI to converge into a unified “digital trust layer” for the asset management industry. She said blockchain enabled by compliance infrastructure can act as the single source of truth for fund data, while AI delivers ongoing monitoring, predictive analytics, and automated decision-making.
“Capabilities such as smart contracts that auto-execute compliance checks or instant investor reporting from verified on-chain records will become standard,” Walker said. “This will make hedge funds more transparent, operationally efficient, and better equipped to meet rising investor expectations. Integration between blockchain and AI technology can also enable features like tokenized fund access, smart contract-enabled transactions, and fractional ownership – all through a single, compliant framework.”
She also sees powerful applications in post-trade and asset servicing, including processing massive volumes of unstructured data and applying consensus algorithms to create a unified “golden record.” Walker also expects hedge funds to leverage blockchain technology to distribute their records across all parties.
“This eliminates the need for data replication, ongoing reconciliation, and reliance on costly third-party data providers,” she added. “Recent initiatives from Euroclear, SWIFT, and DTCC – including CALM (Corporate Actions Lifecycle Management) and Smart NAV projects – show how these technologies are already reshaping how complex trades are processed.”
Walker predicts that institutions will gain the flexibility to expand product shelves, enhance investor engagement, and capture new revenue streams as the investment landscape evolves.