Scale’s wingman is data modernization

Accenture wrote that, “The beating heart of the investment banking business has seen profound change over the past decade,” in its 2021 report on capital markets, referring to the period between 2009-2019. I would argue that 2020-2025 has produced much more profound change, and in a shorter time frame — at a time when banks were striving to catch up with that first wave of change, the migration to cloud environments and SaaS solutions. Most definitely, it has proven a massive undertaking to integrate these SaaS point solutions with traditional banking systems, built on a lot of disparate systems that were either amalgamated through acquisitions, individual business units, or built internally. Yet, the most frustrating problem that institutions have faced in the last decade of digital transformation is the inability to scale efficiently.

As institutions onboard new clients, product lines, or tradeable assets like crypto, structured products, or private markets vehicles, they often lack unified systems that can handle booking, risk, compliance, and reporting seamlessly. Or their systems’ processing power simply cannot handle big increases in volume. In a 2024 survey report, 94% of sell-side respondents said that data fragmentation made integration of new applications challenging, and 90% reported that poor data quality caused issues in clearing and settlement.^v

Holistic banking data and operational modernization reduces the need for expensive, time-consuming custom builds, duplicative manual processes, and tech-related bottlenecks. Instead of new products triggering manual workarounds in valuation, booking, and risk monitoring, a data platform built expressly for capital markets participants, like the Aquata platform, automates the ingestion, normalization, and centralization of new datasets, as discussed in part 1.

This level of data management opens a lot of doors, including self-service tooling for business users. Automation and no-code tooling enable sell-side institutions to avoid the necessity to go on a pricey talent acquisition spree when scaling.

AI-powered self-service opens the door to innovation

Self-service tools with intuitive design help sell-side internal teams to build, iterate, and solve business challenges faster and more effectively. Recent advancements in AI have created fertile conditions for “citizen coders” or “non-technical users” in multiple functions who can now execute tasks that would otherwise have demanded emails to IT and other departments and considerable time expenditure. People across banking functions can create their own data quality and governance rules and customize their own models. They can ask our Aquata AI Copilot to make tailored dashboards for decision making in compliance, risk modeling, analytics, and more.

Users in risk, accounting, treasury, sales, or trading can ask AI-powered assistants to locate relevant data or uncover trends. My colleague referred to this as data curation that turns data noise into clarity. Self-service allows teams to innovate and collaborate more effectively and gives them a new sense of ownership. Often, information is not shared effectively between the front, middle, and back offices, creating grit in the gears of the bank. Self-service tools prevent banks from heavy reliance on external vendors and their own tech teams. Additionally, these intuitive tools are a shot in the arm for innovating distinct offerings in new financial products, structures, and vehicles.