Authored By
Nate Xie
Nate partners with prospects and clients on difficult software and operational challenges.
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Operational alpha is key to asset management success, enhancing efficiency, risk management, and growth. By eliminating data silos, automating reporting, and optimizing treasury workflows, firms improve decision-making and scalability. Modern fintech solutions enable seamless operations, helping firms optimize costs, meet regulatory demands, and unlock sustainable alpha in a competitive landscape.
Investment banks and broker-dealers are on a digital transformation and AI adoption luge track, where the gravity of staying competitive is speeding them forward, and the inertia of managing legacy systems and regulatory shifts is slowing them down. Gartner forecasts that enterprise IT spending for the banking and investment services market will see a five-year CAGR of 8.7% that will exceed a whopping $1 trillion by 2028.
The need to optimize data and operational infrastructure will drive higher quality decision-making across levels and business units. Direct reports and cross-team collaboration streamline data discovery and bootstrap revenue-impacting questions, like 'how can I make meaningful inroads into the swaps business while balancing risk analysis appropriately’.
The question is no longer if digital transformation will occur; it is when it will occur. We would go a step farther and say it’s a question of how an institution goes about the transformation that will tell the tale of its success.
Thankfully, we are working in a time when technology is enabling people — even those not so technically inclined — to answer questions faster than they used to. For instance, to figure out how they can increase market share in a business vertical, an analyst doesn’t need to reach out to three teams, aggregate the data, and analyze the data to be able to answer the question. Instead of seven steps and five people across various teams, it's one person with no-code and low-code tools at their disposal and an increasingly agentic support system from their technology tools.
The technology knowledge curve is flattening, so empowering people with technology is both easier and more important.
For sell-side institutions, this era of self-service is generating positive impacts, from greater collaboration and communication between teams, to superior client service and enhanced visibility and risk management.
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Amid industry-wide digital transformation, many of banks' pain points concerning collaboration with data across business units begins with reference data and involves many disparate systems married together. Without modern infrastructure, governance, symmetry, and standardization, data cannot flow freely between departments, clients, their investors, or to counterparties and other third parties. Without quality data, banks cannot launch or improve effective generative AI initiatives.
Moreover, regulators are demanding a heightened data infrastructure from sell-side institutions. In 2024, “The industry's four top watchdogs — the Federal Reserve, the Office of the Comptroller of the Currency, the Federal Deposit Insurance Corp. and the Consumer Financial Protection Bureau — dished out more than 120 enforcement actions, fines, lawsuits and complaints against banks of all sizes”, according to American Banker.
Regulatory bodies are increasing their focus on data-driven oversight and risk management. Basel III, MiFID, and T+1 require more detailed reporting, making robust data management and automation crucial for staying compliant. In December, the Financial Stability Board called for disclosure practices between leveraged entities and prime brokerages with its proposal for enhancing risk identification and monitoring, “supported by a suite of risk metrics, and work to assess and address data challenges.” The lack of modern data infrastructure means compliance can falter leading to costly enforcements. Reporting — to regulators and clients — can be inaccurate and late.
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Banks are struggling to move internal data into upstream risk modeling, analytics, compliance, and trade execution systems. They encounter bottlenecks or additional downtimes beyond the expected level of service. For instance, if an important brokerage, execution, or risk system is down more frequently than anticipated, technology and business users alike may struggle to build momentum on strategic initiatives outside of day-to-day tasks in their respective verticals. Sell-side firms often have data spread across many disparate systems, making it difficult to access and consolidate. This leads to time-draining inefficiencies as employees spend significant time gathering data from different sources.
Data silos hinder collaboration between different departments, as information sharing is not seamless. Teams often need to engage in constant back-and-forth communication to get the data they need, sending or sharing spreadsheets between, for example, treasury and risk-compliance departments. This leads to miscommunications up and down middle- and back-offices about important items, causing versioning and provenance problems. When people don’t trust data, they may be may be reticent to invest in more strategic areas. . Sell-side firms can empower their professionals with better quality data access, automation, and self-service tech tools. Once empowered with flexible tools, they can deploy standardized operational processes which will lead to more efficient onboarding of clients and increased market share, among other advantages.
Advanced data infrastructure enables sell-side institutions to centralize and optimize datasets running the gamut from securities terms and conditions to liquidity. This ensures accuracy for trade execution, risk modeling, and regulatory compliance. Subsequently, a risk analyst and compliance analyst working on reporting FR 2052a, for example, will not become bogged down chasing liquidity risk information from different teams and systems. Those questions or analyses are answered more efficiently and accurately when data is consolidated.
Various business units have various revenue streams. As a result of those differing priorities across verticals, technology systems have traditionally been more akin to tapestry patchwork than aesthetic symmetry. By extension, the operational processes and cross-team collaboration are inherently limited by the scope of disjointed requirements. Some institutions have built systems through retrofitting or acquisitions that have accumulated in certain business lines, the patchwork leading to downstream impacts. These systems simply cannot handle the volume of financial data flowing through their pipes and are prone to crashes and downtime. In a bank mired in data silos and detached systems, some departments can become bottlenecks to the entire operation, especially those with the most horizontal influence like treasury, risk, or trading, which touch many other functions.
With a modern data platform and cross-functional data integration, there is better collaboration across departments, with a lower-touch approach. Furthermore, real-time data flows between prime brokerage, clients, and internal teams optimize all workflows, enabling firms to derive insights for real-time analyses.
For instance, the treasury department can become a bottleneck for other departments if it’s unable to quickly provide data and answers to questions from across the institution. Treasury teams are tasked with managing collateral requirements, balance sheet risk, and concentration risk across clients. But a lack of consolidated data can impact their ability to monitor risk, particularly concerning concentration risk across clients and the firm’s balance sheet and can lead to severe consequences.
Firms must get control and visibility into complicated risk data spread across multiple, disconnected platforms. Once the centralized data foundation and low-code or no-code technology is in place, then individual departments such as treasury can customize dashboards for a deeper comprehension of their data. This is the essence of modern self-service solutions.
The technology that empowers people the most in a financial organization is going to be high in self-service functionality. For example, self-service tools enable citizen developers to leverage an existing library of data quality rules — or create their own rules. Therefore, it is important to alleviate the complexity and give them the tools they need to do their work effectively. Self-service platforms are highly customizable, allowing different departments within a bank to tailor the tools to their specific use cases.
Self-service solutions eliminate bottlenecks by providing direct access to consolidated data, reducing the need to involve other teams for data access. They also enable the automation of reporting and analytics by providing faster access to data.
In 2025, we rely less on technical counterparts, empowering everyday business users. The right data and operational technology will drive collaboration across the bank, which opens the door for a near-real-time, 360-degree view of risk, integrated data management, reduced costs, and a workforce empowered to innovate and create efficiencies in their own role.
1. Data silos hinder efficiency – Investment banks struggle with fragmented systems, impacting compliance, risk modeling, and decision-making.
2. Self-service tools empower teams – No-code/low-code solutions enable non-technical users to manage and analyze financial data independently.
3. AI and automation drive transformation – Clean, centralized data is essential for leveraging AI and real-time analytics in capital markets.
4. Regulatory pressure is increasing – Banks must modernize data infrastructure to meet Basel III, T+1, and other compliance mandates.
5. Integrated data unlocks collaboration – Cross-functional data sharing enhances risk monitoring, trade execution, and client service.
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