{
  "id": 8206229,
  "title": "AI boom raises data quality concerns for hedge funds",
  "url": "https://urgent.news/2026/09/18/ai-boom-raises-data-quality-concerns-for-hedge-funds",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-18T08:09:25.000Z",
  "source": {
    "name": "Hedgeweek",
    "slug": "hedgeweek",
    "url": "https://www.hedgeweek.com/ai-boom-raises-data-quality-concerns-for-hedge-funds/"
  },
  "original_language": "en",
  "account": "The rapid adoption of artificial intelligence (AI) across the data industry is causing concerns about data quality for hedge funds, according to a report by Business Insider. Research from data consultancy Neudata found that around one-third of investors purchasing external datasets report a decline in information quality over the past two years, with some attributing at least part of the decline to the increasing use of AI. As alternative data becomes more integral to investment processes, hedge funds use datasets covering consumer behavior, corporate activity, web traffic, and other indicators to identify potential market signals. AI has lowered barriers to collecting, processing, and packaging such information, leading to a proliferation of data providers. However, reliance on AI can introduce new problems when used to generate, classify, or interpret datasets without proper human oversight. Daniel Entrup, co-founder of AggKnowledge, noted that providers are increasingly using AI to produce datasets or as a reason to reduce staff responsible for data quality. A senior investor highlighted concerns about newer providers relying heavily on large language models, raising reliability issues. For hedge funds, data quality involves not just the accuracy of individual data points but also understanding how information is collected and processed, especially in scenarios with regulatory or data-privacy implications. Daryl Smith, head of research at Neudata, emphasized that funds may face difficulties when vendors cannot adequately explain their dataset production methods. There are also worries about the quality of AI systems used by vendors. Hedge funds with substantial technology budgets and in-house data-science capabilities may prefer to process raw information through their own infrastructure rather than depend on a vendor's proprietary AI models. This approach allows managers to retain control over the transformation of raw information into trading signals, which is a crucial aspect of their investment process. Smith noted that while funds trust AI with their workflow, they are less confident in its ability to generate alpha. The trend towards AI-led data processing has also coincided with an increase in basic data-quality issues, according to Entrup. Even minor errors can significantly impact models and trading strategies, as a flawed observation can distort signals and lead to misleading indications incorporated into investment strategies.",
  "summary": "The rapid adoption of artificial intelligence across the data industry is creating a new problem for hedge funds: the growing quantity of information available to investors may be coming at the expense of data quality, according to a report by Business Insider.",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}