Investment banks welcome the average Joe and Jill – and their data

New products and customer segments are introducing layers of complexities, compounding the necessity for centralized, scalable investment data infrastructure. When the U.S. opened the doors to affluent retail inflows to private credit, it began changing the nature of these investor cohorts, raising questions about liquidity and suitability for less savvy investors.ⁱⁱⁱ

Further, banks are introducing new product wrappers to capture retail inflows. They will have to adjust their risk modeling, liquidity simulations, and valuation practices. As retail investing in alt assets becomes more mainstream, transaction volumes will skyrocket, pushing older, non-cloud data platforms to their limits and causing bottlenecks and errors.

Blockchain extends business hours, adds more retail investors

Policymakers also put the crypto pedal to the metal this summer, with the SEC opening compliant avenues for digital asset trading. The coming increase in blockchain trading pushes the needle on the idea of 24/7 (or, really 24/5) market operations, unrestricted by 9-5 banking hours. The automation of trade capture and settlement operations to enable 24/5 trading, while nothing to panic about, is really a spoke in the unstoppable data transformation wheel. It is imperative that sell-siders stop changing only the spokes and start changing the wheels.

AI forces the data modernization issue

Now, the AI imperative has stoked flames a previously slow burning, industry-wide sell-side data transformation. Banks were dabbling in AI digital assistants and large language models (LLM) tools in 2024; in 2025, they are awash in them – or at least in the development of them, urgently needing to “shed technical debt so they can realize the promise of becoming an AI-powered bank.”^iv

CDOs are flocking to AI tools to automate reporting and disclosures, improve data ingestion, normalization, and analysis from vast amounts of structured, semi-structured, and unstructured data. Before AI agents can replicate operational tasks like processing trade captures and resolving data quality problems that prevent trades from being correctly entered into the system, they must be fed precise, high-quality data. It’s a chicken-egg scenario in which the number of eggs is seemingly endless.

KPMG’s 2025 Banking Survey corroborates what we have been hearing from clients and prospects. The top blockers to their data modernization efforts are data quality (89%) and the complexity of integration with legacy systems (81%). Their results also indicate only a small majority of banks have installed foundational data capabilities, at 64%.v Just over a year ago, Deloitte reported that most banking leaders said (88%) that their data is available but from different sources, duplicated, and not integrated, aka not centralized or harmonized.vi Since then, banks report that more centralized data is accessible, but the quality remains lacking.

Their world is exploding with data and middle- and back-office processes are creaking on top of quaking operational models. See our previous article to find out more about how sell-side institutions can prepare their data systems for AI.

Why risk porous risk management?

A sell-side organization’s inability to deal with the increasing volume, velocity, and variety of financial data hits the top line, the reputation, and the bottom line. The management of credit risk, operational, liquidity, and non-compliance risks is perilous with bad data governance and management.

For example, if a brokerage has a hedge fund client that has a blip at a bank, it needs to be able to monitor their collateral requirements in real time. The staff will be examining implied and realized volatility metrics, leverage ratio data to look for balance sheet stress, margin call trigger data, and asset class haircut data, among others. With imprecise, missing, or untimely data, collateral calls go unmet, the brokerage becomes the bagholder, and the brokers scramble to unwind positions. Fire sales and unwanted regulatory attention follow.

And we are only talking about one subset of risk management. Hundreds of regulatory reports still have to be filed on a daily, weekly, and monthly basis. Simply put, banking professionals need access to accurate, reliable data – and they need it now.