How Sell-Side Institutions Can Monetize The Market Turn: From Data Deficits to Digital Dominance

Investment banks and prime brokers can take a variety of approaches during volatile, uncertain economic times. In the past 100 years, banks often retrenched when there was increased volatility and uncertainty in markets. And yes, sell-siders now do business during a transitional reshuffling of global trade and capital flows. While data-driven risk mitigation is more urgent than finding new revenue avenues, it is essential for banks to be ready for the earliest stages of an up economic cycle, which are some of the most profitable time windows.  

With reduced regulatory burdens, banks must now address longstanding data challenges — fragmented systems, inconsistent reporting, and operational inefficiencies — to fully capitalize on new revenue-generating opportunities. 

Don't let a crisis go to waste  

High performing sell-side institutions will certainly do what’s necessary to mitigate risk and manage through the storm. But they shouldn’t let a crisis go to waste. Some can use this era to muscle into a position ahead of the competition when it’s time to capitalize on new opportunity sets.  

A 2015 study of the 2008 financial crisis revealed two factors were imperative in the recovery of firms (without bailout funds) from the crisis: 

1) Identifying the root cause of the problem before embarking on a course of action  

2) Taking “long-term investment positioning versus a short-term fire-fighting posture”  

The study cited Goldman Sachs as a good example of identifying root causes, “First, sources at Goldman noted that risk processes across trading desks were too human-centric and lacked the technological capacity required to capture total risk in the system.”  

It’s a good time for banks to invest in people, processes, and technology, especially for institutions trailing their competitor’s digital transformation. 

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Which banks are digitally transforming in 2025? 

Some institutions are in the midst of real, significant capital markets digital transformation. However, post-trade processes remain heavily intermediated due to poor data quality. Meanwhile, front-office staff is spending too much time on non-revenue generating tasks due to regulatory complexity and legacy technology. A handful of Tier-1 banks have a track record of leading the pack by investing in advanced technologies.  

Some banks realize they have fallen behind competitors in terms of efficiency, balance sheet optimization, and, of course, profitability. Other Tier-2 banks recognize a golden opportunity to use technology to close the gap with the bigger players that charge significantly more for their services without providing value equal to the spend. Meanwhile, the 40 or so Tier-3 banks with balance sheets between $100-500 billion realize the need to scale to remain competitive and not get scooped up as part of an ongoing consolidation trend. 

Going (cloud-) native for speed and customization 

To move the needle on modernization, banks first need to understand where they can create efficiencies, appraising all operating systems, risk systems, and funding systems. Then they can drill down to exactly where they can build or buy technologies that are really going to drive revenue.  

Modernization is by no means a simple initiative. National or multinational institutions face a monumental task: execute a strategic reorientation and transform from deep-rooted (sometimes mainframe) technology that has been in place for decades. The north star is cloud-native, scalable architecture with data standardization and easy integration — technology that will enable growth for 20-30 years in the future.  

Imagine you are a global head of treasury running funding for a trillion-dollar balance sheet. Say you receive 20 different middle-office P&L reports a day, 20 different risk reports a day, and you operate in 65 markets around the world. It’s stressful just thinking about it.  

If you’re still running a vintage mainframe, you may have a team of 30-40 people trying to fund a trillion-dollar balance sheet. Manual, legacy processes are not conducive to speed, much less real-time speed, which is exactly what buy-side institutions and investors are expecting in 2025.  

Buy-side wants speed and personalized client experiences 

Speed by way of automation eliminates bottlenecks, reduces errors, and streamlines workflows. Regulators will continue to ask for speed. SEC Rule amendments to the Broker-Dealer Customer Protection Rule require broker-dealers with $500 million+ in customer credit balance to increase the frequency they perform computations of the net cash they owe to customers and other broker-dealers (known as PAB account holders). The amended rules will shift cash computations from weekly to daily, beginning no later than December 31, 2025. 

Most clients, especially those that run elaborate, sophisticated strategies like multi-strategy hedge funds, PEs, private credit funds, and family offices, increasingly demand fast, personalized service, tailored reporting, and data insights. They also want sell-siders to demonstrate a deeper understanding of their evolving businesses. Banks, prime brokers, and fund administrators have an opportunity to leverage the vast amounts of data they process to generate intelligent observations that can be valuable to their clients and potentially monetized and personalized.  

Building revenue-ready architecture: cloud-native transformation 

Data transformation can provide a competitive advantage in terms of optimized treasury operations that drive capital efficiency. But data transformation is also indispensable for driving revenue. For example, prime brokerages can set themselves up to seamlessly manage securities lending, synthetic financing, OTC derivatives, and multi-asset financing. Advanced data capabilities can help PBs deploy advanced margining and collateral tools to optimize liquidity and scale operations with surgical precision. Further, centralized margin management and automated call calculations give PBs the agility to rehypothecate collateral. 

Analysis from the European Central Bank reports that banks are increasingly relying on outsourcing, increasing from 6.8% in 2023 to 7.2% in 2024. Experts at Deloitte predict that the future of investment banking is cloud-based architecture, centralized data management, consolidated operations processes and activities across asset classes. 

The Devil is still in the data, especially if the data is incomplete, hard to access, and widely dispersed.  

Solving the sell-side data problem 

In late 2024, the World Cloud Report for Financial Services 2025 revealed that banks and insurers named two of their three top concerns in handling financial data: legacy systems impeding siloed data integration (71%) and lackluster data quality, including incorrect and missing information (69%). 

To get value from the oceans of data flowing through their conduits, sell-siders will need to upgrade to more cohesive and scalable data infrastructure and operating models with greater commonality across asset classes and business functions.  

Some sell-side business units may be using the low-cost data lake approach to leverage raw, unstructured data. There are data transformation layers that exist inside of banks on top of older infrastructure. If an institution ships their data out to a data lake and gets it back the next day fully formatted, it’s already too late for real-time analytics. And they have no ability to amend their reports. Teams are struggling to deal with dynamic markets. They're dealing with increased requests from clients. They're seeing more volume and more asset classes. Business units have to become nimbler in how they ingress data, transform data, and create data analytics that are going to deliver better client experiences and drive profitability.  

A data platform on a data lake house is a superior solution that offers exceptional flexibility in reporting as well as supporting various BI tools, due to their structured data format. As business units look for ways to nimbly ingress data and create analytics, a transformation layer built on battle-tested financial models will help normalize data and get information upstream leading to better client experiences, a more optimized balance sheet, and greater profitability. 

Keeping the data flowing during digital transformation 

For large sell-siders, digital transformation is a multi-year journey of sunsetting legacy systems and integrating new technologies into operations. Those that have embarked in these initiatives at various stages have encountered challenges in keeping the data flowing.  

And while renovations are happening on their digital construction site, banks still have to work around existing structures. Data from sources like reference data masters, cash ledgers, collateral systems, and deposits platforms get kinked up in rusty, patchwork plumbing. Subsequently, answers come slowly. 

While under construction, data science teams still have to extract and normalize data. They must send accurate data upstream for compliance and regulatory reporting, management and market oversight, client metrics, and AI initiatives. Implementing a data mesh as a transition layer is a crucial strategy to manage interoperability, governance, and gradual migration by acting as a bridge between the centralized data of legacy systems and the decentralized, data-sharing principles of collaborative businesses. This involves extracting and normalizing data from existing systems and feeding it to critical upstream use cases, risk systems, regulatory reporting, funding systems, central treasury, and compliance. Then, the institution can automate and customize its data operations, mastering securities and issuers, transactions, corporate actions, positions, and price data. 

In a time of extraordinary volatility, resilience against geopolitical and trade shocks is vital to navigating this ambiguous, risk-laden environment. It’s also an important time to get ready to seize the moment of the next up-cycle by installing the infrastructure pipes for greater speed, customization, and data-driven insights. 

Key takeaways 

1: How can banks turn market crises into future revenue opportunities? 

By identifying the root causes of inefficiency and investing in long-term digital strategies rather than reactive fixes, banks can position themselves for profitability in early up-cycles, often the most lucrative periods. 

2: Why is legacy tech a barrier to sell-side competitiveness? 

Fragmented systems, slow reporting, and human-centric workflows slow decision-making and hurt client service. Cloud-native architecture with standardized data enables real-time analytics, efficiency, and scalability. 

3: What are digitally advanced banks doing differently? 

Leading institutions invest in cloud platforms, consolidate post-trade systems, automate client reporting, and scale across asset classes and geographies — closing the gap between data capture and action. 

4: What does the buy-side expect from sell-side partners in 2025? 

Speed, customization, and insight. Clients demand real-time reporting, tailored services, and data-backed advisory — especially in complex strategies like private credit and multi-asset investing. 

5: How can banks keep operations resilient during transformation? 

By implementing a data mesh as a transitional layer, banks can normalize and route critical data across systems, ensuring ongoing compliance, risk management, and service delivery while modernizing backend infrastructure.