ArVision Quarterly Newsletter:
Why Efficiency Is the New Alpha
June 27, 2025
Newsletter
ArVision Quarterly Newsletter
Arcesium's quarterly newsletter delivers our perspectives on data, innovation, and industry trends in the investments space.
Whether through AI-powered automation, seamless system integration, or collaborative innovation, investment managers are redefining what it means to be operationally agile. This four-part series explores how firms can connect ideas to infrastructure and insight to impact.
How a Large, Global Asset Manager Accelerated Reporting and Simplified Data Integration Post-Merger
Context:
After completing a strategic merger and expanding into private credit and digital assets, a global asset manager was juggling legacy systems, siloed data, and manual workflows.
Challenge:
Lengthy ETL processes, data mapping headaches, and manual maintenance were delaying innovation. Teams were struggling to generate timely investor reporting and analyze exposures across asset classes.
Impact:
To reduce complexity, the firm implemented Arcesium’s Aquata® platform—leveraging out-of-the-box integrations with Bloomberg, FactSet, and internal systems to unify investment, risk, and market data.
A clean, centralized data foundation enabled the asset manager to:
Reduce integration time by approximately 30 minutes per task
Onboard new asset classes in weeks, not months
Unify data across public and private market investments
Free up data engineers to focus on higher-value analytics and AI initiatives
This data-first approach streamlined operations and improved responsiveness to investor and regulatory demands, laying the groundwork for faster product innovation.
Modernization doesn’t have to be a multi-year overhaul. In fact, the most successful transformations are often incremental, starting with low-effort, high-return projects. Begin by centralizing a few key data sources. Swap out a reporting process that pulls directly from siloed systems and redirect it to a clean, unified dataset.
For example, a private credit firm might begin by replacing its Excel-based interest accrual tracking with an automated engine that pulls data directly from loan servicing systems and reconciles it with fund accounting records.
Once you validate success, you can begin to expand. With each successful implementation, your firm builds momentum, retires legacy processes, and can explore new capabilities.
How AI Is Creating Operational Efficiency
Conversations about artificial intelligence are moving from buzz to implementation. No longer a novelty or a futuristic concept, AI has become a practical lever to manage growing complexity, cost pressures, and the limitations of on-prem infrastructure.
For leaders such as COOs or data heads, the question isn’t whether to adopt AI, it’s how to do it in a way that delivers real, measurable results. Competitive firms are using AI to unify fragmented data, reduce manual workflows, and equip their teams with faster, more accurate insights.
Let’s take a clear snapshot of where AI stands today, how it's reshaping the hedge fund operating model, and what’s next.
Fragmentation, friction, and the fight for efficiency
Despite managing sophisticated investment strategies, many hedge funds are still saddled with operational inefficiencies rooted in legacy systems.
Take a global macro hedge fund running high-volume FX and fixed-income strategies across multiple geographies. Each strategy uses a separate data architecture, with pricing feeds, trade records, and risk metrics flowing through disconnected systems.
Common pain points include:
Manual workflows: Reconciliation, compliance reporting, and data aggregation are often spreadsheet-driven or reliant on point systems, resulting in delayed P&L snapshots.
Fragmented data: Multiple systems keep information in silos, making it harder to get a unified view of portfolios or operations.
Compliance pressure: Ever-increasing demands from regulators and investors create additional overhead and make it difficult to maintain consistent reporting across strategies and regions.
As operational challenges inflate costs, they also introduce risk and delay strategic decisions. Operational bandwidth is often stretched just trying to maintain accuracy, let alone enabling real-time analysis or preparing for investor inquiries.
Use cases redefining efficiency
For COOs, the most valuable AI use cases are those that reduce cost and risk in critical operational workflows. AI is already making an impact in areas where speed, accuracy, and volume are essential.
Data leaders may be more focused on translating AI hype into practical value with a focus on solving real-world problems rather than experimenting for experimentation’s sake.
Here is where firms are seeing real impact:
Trade reconciliation: Intelligent systems match trades, identify breaks, and flag exceptions automatically, saving hours of daily manual work.
Risk analysis: Machine learning models ingest real-time data to assess exposure and suggest hedging strategies.
Data normalization: AI-powered platforms can ingest and standardize unstructured data—from PDFs to emails and conference notes—creating clean, usable datasets for downstream analytics or to support capital raising and investor relations.
Investor portals: Some firms are deploying AI-powered assistants that handle routine investor queries, freeing up operations teams for more strategic interactions. AI agents pull from multiple data layers, such as fund, share class, call notes, and capital flows, to generate investor-specific snapshots on demand.
Advisory support: Some funds are using AI in an advisory-like capacity to power analytics, apply machine learning, and deliver insights to portfolio companies. Tools help gather and surface information in ways that are most useful to GPs to bolster decision-making and operational support.
As our colleague Matt Katz recently shared with Hedgeweek, AI is enabling a new type of worker: the “data centaur”. People supported by powerful AI tools are combining the best of both machine speed and innate talents of human judgment, reasoning, and consciousness.
The foundation of every AI strategy
There’s a consistent theme, though, when it comes to the powerful technology: AI is only as good as the data behind it.
Establishing a single source of truth is especially key for data managers as it ensures both people and the tools they’re working with are using the same accurate and up-to-date-information.
Clean, structured, and accessible data is the foundation for effective AI. Modern data platforms serve as a golden source for investment, risk, and operational data. These platforms not only power AI applications but improve the entire data supply chain.
Smaller funds, in particular, are finding success with AI due to more manageable data volumes and fewer organizational silos. Their size allows for faster experimentation and implementation—a “virtuous cycle” of innovation that often eludes larger, more complex organizations. More established funds that grew through acquisition or with years of historical data in legacy formats can leverage AI platforms to ingest, normalize, and unify data for use cases such as current-day or historical pricing.
From hype to reality: the shift to production-grade AI
Not every AI announcement signals meaningful change. Firms must distinguish between innovation theater and operationalized AI.
True transformation occurs when AI is embedded within daily workflows and supported by:
A robust technology stack
Governance structures
Executive sponsorship
Cross-functional collaboration between humans and AI systems
According to the World Economic Forum, future agentic AI systems will increasingly integrate collaboration with human experts. This hybrid model—humans and AI working together—is where the real value lies.
Build for the future, starting now
The rapid advancement of AI in financial services is not slowing down. Firms that wait for “perfect” conditions will fall behind. Chasing the hype won’t work either.
Instead, hedge funds must begin by:
1.Investing in data infrastructure and clean data governance
2.Identifying high-impact, repeatable use cases for AI
3.Testing and refining with small, agile projects
4.Scaling what works and being ready to pivot
AI isn’t magic; it’s a tool. In the hands of firms with the right foundation, it’s a tool that can open up unprecedented operational efficiency, insight, and adaptability.
By many measures, the first half of 2025 has been tumultuous. Geopolitical tensions, trade disputes, economic uncertainty, and fluctuating interest rates dominated the headlines. Despite turbulence, the markets have remained resilient and largely shrugged off volatility.
Against this backdrop, asset managers are working to carve out their niche in a complex and interconnected investment ecosystem. Regulatory pressures, persistent market volatility, rising operational costs, the need to attract and retain skilled professionals, and shifting customer preferences continue to shape their priorities.
As managers expand into new asset classes, markets, and strategies, their infrastructure must evolve in tandem.
Growth, however, often introduces added complexity. Whether through geographic expansion, diversification into areas like private credit or digital assets, or M&A activity, firms frequently find themselves operating a patchwork of modern systems and legacy ways of working that don’t speak the same language.
In this context, data becomes a critical enabler. With a steady flow of accurate, consistent, and timely information, buy-side firms can strengthen decision-making, improve compliance, enhance client experiences, and drive operational efficiency.
A shift toward smarter integration
Most firms have been extremely adept at generating data, not so much at extracting value from it. Traditional, manual approaches to data integration are unsustainable in today’s fast-moving, data-driven environment. The modern model demands seamless connectivity, automated data quality controls, and intuitive, self-service access.
As investor expectations shift toward more frictionless experiences, firms must take dismantle internal silos, unify data architectures, and align around shared goals. This requires reimagining operating models; not around legacy structures, but around the needs and preferences of your investors.
1. Connect data at scale
Pre-built integrations with hundreds of market data vendors and financial systems can enable your teams to accelerate onboarding and reduce custom engineering work. Plug-and-play can help you deploy new data pipelines in weeks—not months—and adapt more quickly to changing markets.
2. Cleanse and enrich
According to Gartner, bad data costs organizations an average of $15 million per year.
Extensible data models and built-in data quality tools are designed to mitigate that risk, enabling teams to automate exception flagging and build trust in the data they're using.
3. Empower with self-service
Modern data integration isn’t just about pipelines; it’s about people. By democratizing access through intuitive tools, business users can analyze and visualize data from multiple sources without needing to write code or face bottlenecks with IT requests.
Strategic outcomes
As generative AI and advanced analytics continue to mature, their effectiveness will depend on the availability of clean, well-structured, and timely data.
Cloud-native platforms deliver scalable infrastructure, rapid deployment capabilities, and integration across public and private investment data. This unified view supports everything from regulatory reporting to investment analysis—without the noise of disconnected systems.
With better data integration, asset managers can:
Accelerate time to insight by reducing ETL burdens and improving data accessibility.
Support cross-asset strategies through unified data views across public and private market investments.
Enhance operational agility to respond quickly to investor and regulatory demands.
Improve ROI on technology spend by maximizing the utility of existing and new data sources.
When milliseconds and margins matter, data connectivity is a competitive differentiator. Asset managers who prioritize modern data integration strategies are better positioned to navigate complexity, improve operational efficiencies, and lead in a data-driven future.
Private markets have seen explosive growth in recent years, becoming an increasingly attractive asset class for investors seeking uncorrelated returns and long-term value.
As the market matures, so do the operational demands. Investor expectations are changing, regulatory scrutiny is intensifying, and legacy systems, often built around spreadsheets and point solutions, are straining under the weight of it all.
The growing pains of success
In many ways, the problems are good ones to have. Capital is flowing in, new strategies are emerging, and firms are scaling fast.
But, growth also brings complexity. Fund managers are struggling to respond quickly to investor inquiries, adapt to more modern performance metrics, or provide timely reporting. What once worked in Excel or through manual reconciliation doesn't scale in an environment where agility and precision are non-negotiable.
Private credit deals often come with bespoke terms, frequent cash flows, and complex capital structures. The deals require more dynamic ongoing data updates, especially when servicing floating-rate loans, calculating accrued interest, or managing amortization schedules.
For fund managers, manual reconciliation and fragmented processes are making key operational processes more challenging—just as the stakes have never been higher.
Data is the common denominator
As private finance works to keep pace with an influx of new investors, diversified products, and a range of asset classes, data is taking center stage. In a recent survey of 100 private market fund managers, conducted by Private Equity Wire and sponsored by Arcesium, diversifying the investor base and launching new products and strategies topped the list of challenges.
The root cause of many operational hurdles? Fragmented data environments. Over time, firms have solved specific business problems with tactical tools and isolated systems, inadvertently creating disconnected workflows and duplicate data.
Many private credit shops have added funds, regions, and investor types without a unified data strategy. Over time, portfolio information sits in one database; the Ops team gets their cash flow models from spreadsheets; investor records are in a CRM; and documents are stored in shared drives.
When it comes to striking a NAV, this “data chain problem” leads to issues like:
Siloed teams working from conflicting numbers and NAV inputs
Reporting delays
IR bottlenecks
Difficulty diagnosing errors without full data lineage
In the PEW research, firms classified themselves through a range of responses when asked to assess the use of data in daily operations. Almost half (47%) considered their data prowess to be a work in progress.
Solving the data challenges requires more than just better tooling—it requires a new mindset that prioritizes centralized oversight, data lineage, quality checks, and cross-functional visibility.
The pivot to nimble, scalable operations
Firms that are successfully modernizing start by building data foundations that serve the business across use cases—investor relations, compliance, portfolio monitoring, and beyond.
The process starts with getting out of the silos. By consolidating core data sources—such as financial statements, deal flow data, and market intelligence—and applying automation and validation rules early in the process, firms reduce downstream complexity and improve accuracy.
Consider this: you can’t run advanced AI or automation workflows on bad data. The principle of “garbage in, garbage out” remains relevant. Before AI tools can deliver real value, firms need confidence in the data that feeds them.
While AI, automation, and data platforms are powerful, the human element remains irreplaceable. The best systems empower teams—not replace them. Judgment, context, and adaptability still come from people.
Take data extraction. Rather than manually combing through loan agreements, capital call notices, or servicing reports, the Ops team can deploy AI to automatically identify and extract key terms like interest rates and payment schedules and feed them directly into core systems to improve data quality.
Similarly, when investors request information, such as the factors that influenced a NAV calculation or the timing of their next distribution, AI-powered assistants pull from multiple datasets, eliminating the need to chase down files or reconcile numbers across spreadsheets.
Firms best positioned for the future are those that equip their teams with clean data, easy-to-use tools, and the freedom to run small experiments—whether it's launching an investor portal, exploring AI-driven insights, or refining operational processes.
The future is data-first
We are at an inflection point. Data is no longer a back-office concern—it’s the backbone of investor trust, operational agility, and strategic innovation. Whether it’s navigating NAV precision, accelerating investor responses, or enabling AI adoption, it all starts with data.
Firms that modernize incrementally, invest in data quality, and empower their teams will be the ones that stay ahead of the curve and make the most of what private markets have to offer.
The pressure is building on the sell side. As client demands for real-time data, seamless API access, and transparency across the trade lifecycle grow more sophisticated, many sell-side institutions are confronting a sobering reality: their infrastructure wasn’t built for this.
Too often, banks are still relying on fragmented legacy systems that weren’t designed to handle the speed, scale, and customization buy-side firms now expect. Maintaining outdated tech is eating up resources that could be used to reduce latency and deliver value-added services in a more automated, self-service model.
Recent signals from regulatory leaders, including Acting Comptroller of the Currency Rodney Hood, point to a shifting sell-side environment that’s more supportive of innovation, responsible Fintech adoption, and digitally powered banking. In his June 3, 2025 address to industry executives, Hood underscored four strategic priorities for 2025: accelerating FinTech partnerships, reducing regulatory burden, enabling digital asset activities, and advancing financial inclusion.
This policy pivot is opening the door for sell-side firms to reimagine their role as both market makers and data partners.
Today’s massive volumes of data and client demand for new asset classes and strategies, however, expose the limitations of outdated technology and processes that are prevalent inside sell-side institutions. Too often, budgets are consumed by maintaining aging systems rather than on centralizing data or empowering internal or external business users with modern systems.
As trading volumes climb and regulatory tone shifts in favor of responsible innovation, the role of the sell side is evolving.
From risk mitigation to return optimization
In the post-2008 era, banks were defined by caution. Dodd-Frank reforms reshaped the risk landscape and prioritized resilience.
But as trading volumes surge and the regulatory tone becomes more innovation-friendly, banks are once again looking outward—to their clients, to their data, and to their future.
For hedge funds, asset managers, and allocators, that future is being shaped by the sell side’s ability to offer more than execution.
The intelligence that sell-side firms have on liquidity signals, pre-trade analytics, benchmark comparisons, and customized data feeds is helping their buy-side clients manage complex markets and shape strategies. It’s also positioning sell-side firms as indispensable partners in a world where alpha increasingly comes from information.
Buy-side firms are turning to their banking partners for support in areas such as:
Simulations to optimize margin and collateral before they commit capital
Pre- and post-trade analytics
Transparent dashboards that allow portfolio teams to self-serve
APIs that integrate execution data into their own internal tooling
Buy-side firms are no longer just consuming services, they want digital capabilities. Transparency. Faster cycles. Tools that let them self-serve and experiment.
Consider a global bank supporting a hedge fund client with both high-frequency and discretionary strategies. Real-time insights into funding costs across regions and asset classes enable fast decision-making. But siloed systems delay visibility, and critical decisions around liquidity allocation are still made using static reports or outdated messaging systems.
This operational burden is also a competitive liability.
The sell side’s data-driven playbook
March 2025 research from Crisil Greenwich Coalition explored what buy-side investors in the corporate bond market wanted. Their report highlighted that, “An overwhelming theme arose: increased automation supported by more data and more APIs.”
Whether designed to power a trading algorithm or inform a client strategy, data quality and interoperability are prerequisites to innovation.
Sell-side institutions leading this shift are re-architecting their systems around a few core principles:
Common data layers that span businesses and products
Cloud-native architectures for scale and flexibility
AI-enabled tools that provide predictive insights and streamline operations
This is no longer just about internal efficiency. Banks are building client-facing platforms that help their hedge funds and asset manager clients monitor funding costs, simulate what-if scenarios, and make smarter, faster investment decisions.
For example, instead of simply offering margin optimization, some desks are leveraging tools to unify data across risk, compliance, operations, and trading to enable their buy-side clients to seamlessly manage multi-asset financing, optimize liquidity, and access robust securities lending.
This kind of transparency builds trust and drives stickier relationships.
Partnerships, not products
Fintech partnerships can allow for a practical strategy. Banks are collaborating with specialist providers that equip their external clients with purpose-built tools to unify data and create better alignment between trading desks, treasury, and client teams.
Sell-side institutions also have an opportunity to improve cross-team coordination within the bank itself. Tighter linkages between front-office trading, middle-office risk and treasury, and back-office operations help to deliver a unified view to clients that breaks down silos and reduces time-to-insight for client-facing teams
It’s no longer just about selling more tools. It’s about solving client problems faster and in a more holistic way that’s backed by clean, high-quality data.
Operational harmony, strategic differentiation
Ultimately, the buy side isn’t looking to be different in how they operate—they’re looking to be different in the returns they deliver. And that means more are embracing a shared foundation of operational best practices powered by sell-side innovation.
Sell-side institutions that can connect insight to infrastructure—delivering the right data, at the right time, in the right context—position themselves as indispensable partners.
Operational agility begins with data, but it’s delivered through collaboration. Banks that modernize their tech stacks aren’t just optimizing for themselves—they’re helping their clients get ready for what’s next.
The buy side is asking for more—and the sell side is finally ready to deliver.
