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Trends in Performance, Risk, and Attribution Systems
Introduction
While a few years have passed since Meradia’s last Performance Vendor Landscape article, many of its stated trends still hold true. Data aggregation, quality control, and governance remain top priorities for clients evaluating performance platforms. Intuitive workflows and strong exception management continue to shape how efficiently operations teams work within these systems. The number of “BORs” keeps expanding beyond ABOR, IBOR, and PBOR, with new variations emerging regularly. Additionally, performance vendors continue to evolve from niche capabilities toward more complete, end-to-end solutions.
Our aim in this paper is not to revisit those trends but to focus on recent vendor activity we find most interesting and exciting. The main themes we intend to address include:
- How are vendors enabling a holistic view of investments, performance, and risk?
- How are vendors applying newer technologies, such as AI, in practical ways?
- How are vendors helping their clients improve speed, scalability, and resilience?
Cross-Functional Integrations Creates a Unified View
In an era of platform consolidation, investment performance, risk, and attribution systems are undergoing a parallel shift that places growing emphasis on integration capabilities rather than standalone solutions. This evolution is driven by sustained cost pressures, market demand for end-to-end platforms, the need for operational efficiency, and increasingly complex requirements from both asset owners and asset managers.
More than ever, investment performance and risk are inseparable in the market landscape. Asset owners now expect systems capable of supporting complex instruments and private market investments while simultaneously delivering a Total Portfolio View to enable the increasingly prevalent Total Portfolio Approach.
Integrated platforms that span investment performance, risk, and portfolio management are gaining broad adoption among both asset managers and asset owners. In this operating environment, standardized and consistent key inputs are shared across functions, enabling more effective cross-functional analysis and decision support. Solutions such as MSCI’s Total Plan Manager reflect this consolidation trend by offering both performance and risk attribution capabilities, while BlackRock’s Aladdin Risk platform is a clear example of this integrated model. Confluence, on the other hand, uses a unified data layer to allow clients to leverage features from both their Revolution and PARis products for greater functionality. Within these platforms, the primary value lies not simply in improved or clarified reporting, but in enabling better and more timely investment decision-making.
Total Portfolio View is now a core requirement for asset owners. As a result, liquidity and cash management have become critical capabilities that are deeply embedded within investment solutions. Ortec has historically been a strong player in this area through its performance solution, PEARL, and its asset liability management system, GLASS. Together, these platforms support robust performance attribution and analysis alongside macroeconomic factor models that enhance liquidity management.
The GLASS solution has been particularly well received by asset owners allocating across asset classes through strategic asset allocation from a total portfolio management perspective. Solovis offers a differentiated approach through its liquidity-focused platform, enabling analysis of future capital calls and liquidity requirements. Its bubble-and-lines data model provides a distinctive look, with visibility into underlying entities and investments.
While many vendors are pursuing solutions aligned to the increasingly popular Total Portfolio Approach, delivery remains uneven and relatively immature. This is likely to be a significant area of growth in the coming years as vendors continue to invest in meeting this evolving client requirement.
Private market investments and complex instruments have created a distinct opportunity for vendors to serve both asset managers and asset owners. In response, vendors have expanded their performance, risk, and attribution capabilities to better support these specialized investment profiles. Firms such as FactSet and MSCI have leveraged their market data franchises to provide private market data using public market proxies and comparable assets.
At the same time, native private market platforms have moved into the performance, risk, and attribution domain, offering differentiated insights alongside private market portfolio monitoring capabilities. Solutions such as Chronograph calculate and analyze private market performance and risk to support portfolio-level decision-making. While these platforms do not yet directly compete with traditional enterprise performance and risk systems, they are steadily moving closer to that space as they pursue more integrated, end-to-end platform offerings.
Artificial Intelligence is Technology. Operational Context is Intelligence.
Recent advancements in AI/ML* are daunting. The length of coding tasks frontier systems can complete is growing exponentially – approximately doubling every 7 months. The speed at which technological inputs are converted into first-order meaningful outputs is high. Machine learning succeeds where latent structure exists, labels are weak, and probabilistic approximation is acceptable.
For decades, platforms that support investment operations relied on an 80/20 rule i.e. solve for 80% of the use cases while the edge cases can be handled non-systemically. Machine learning has the potential to fill the 20% gap and open new horizons. Differentiating between signal and noise has become easier.
In the investment process value chain, AI solutions can be categorized in many ways. Segmenting into LLM models or algorithm-based may be beneficial for technological insight. However, to decipher the impact on business, the nature of operational context already existing in these platforms is key. This does not mean that if the operational context is low, the platform cannot produce benefits. It means it must be integrated or expanded into a broader ecosystem to get more out of it. With this lens, we look at some of the existing solutions in the market.
Labelling Patterns
Unstructured data processing has been a significant pain point, particularly in private market operations. Capital calls and GP statements do not have standardized formats. Varying design templates exacerbate complexity. AI systems force firms to formalize, label, structure, and operationalize these patterns. Alkymi, Carta and Canoe use AI models to automate extraction, normalize, uncover patterns, and operationalize unstructured financial data – especially from PDFs, statements, investor reports, and alternative investment documents. Further downstream integration with vendor platforms (such as Alkymi with Simcorp) amplifies benefits. These platforms enable knowledge-codification masquerading as automation. Investment operation tasks are being modularized into machine-verifiable micro-judgments.
Nerves to Digits
Traditional investment operations depended heavily on institutional memory, experienced operations staff, and undocumented heuristics. Ascertaining data quality is not a choice. As data travels through the firm, investment operations are responsible for cleansing and verifying quality. Expertise and spontaneous discovery of data patterns underlie decision-making. The heuristics present inside the minds of experienced users can be uncovered by machine learning algorithms through the digital footprints they leave. Oncorps AI is designed to reduce exception volumes through anomaly detection. Meradia’s proprietary algorithm adapts to regime switches and uncovers rules dynamically from data.
Simplify Through Fragmentation
A key insight from Anil Ananthaswamy’s book ‘Why Machines Learn’ is that systems learn from compressed representations of complexity. In an enterprise context, transaction reviews, compliance checks and fund administrator workflows traverse multiple systems. Bespoke orchestrations, Excel templates and system handoffs are prevalent, increasing operational risks. Automation may not be the right solution in such instances. Daizy’sNext matter presents a no-code solution to handle such problems. Abstractions across existing complex workflows with inbuilt governance aids simplification. In a way, this could enable fragmentation of existing workflows. Fragmentation uncovers the hidden commonality necessary for simplification. Historically, scale favored standardization. AI enables profitable personalization of workflows.
Fill in the Blanks
AI is compressing the distance between data and explanation. A typical investment firm’s architecture contains several layers. Data resides in the warehouse. Analytics is generated through business intelligence products. Analysts use document generators to provide insight and develop narratives.
AI collapses the layers. The investment firm evolves from a pipeline architecture to a cognitive-loop architecture. Instead of data → analyst → interpretation → presentation, AI-based solutions provide data ↔ model ↔ explanation ↔ human correction continuously. Operations SME (subject matter expert) is positioned at the end of the loop for verification. FactSet’s Portfolio commentary generates trend analysis that highlights significant contributors and detractors, with references to underlying analytics. With operational context already embedded within the FactSet PA3 platform, the solution framework provides a cohesive experience.
Strengthening Defense
Performance jobs have changed from measurement to decision defensibility. A few years back, the task-based job would be ‘Calculate and report portfolio month-end performance accurately’. Consequently, performance systems were designed to produce returns, attribute performance, and reconcile to books of record. The modern assurance-based job demands ‘Demonstrate that reported performance is the logical outcome of documented investment decisions, constraints, and risks’. This requires lineage from portfolio construction models to executed trades and consistency between performance, risk, and client reports. A downstream integrated-reporting architecture cannot satisfy this job.
Platforms such as Simcorp are integrating AI in at least two aspects – portfolio construction and lineage. Multi-dimensional mandates involving global risk factors and flexible constraint definitions challenge the portfolio construction process. AI features help decrease strategy-building and refining time. Machine learning algorithms uncover the hidden relationships between different modules, data storage structures and changing market data increasing lineage capabilities.
Curated Playground
Investment management firms face the inevitable question of buy vs build on many fronts. In our opinion, front, middle and back-office functions lean towards buy. Configurable features, self-service reporting and API enablement accelerated vendor platform adoption in recent decades. But what about generative capabilities?
Vendors serve the market in at least three ways :
- Resident (BlackRock Aladdin): Tightly integrated, embedded capabilities are high context and exponentially increase the benefits of the core platform
- Partner (Simcorp + Orbit): Platforms offer an extended ecosystem that allows vendors with deep tech expertise and domain awareness to participate. Clients benefit from increased services and not having to perform additional integrations.
- Augment (Arcesium): Platforms provide the building blocks to create agentic frameworks. Investment firms buy the infrastructure, domain-specific context, and build tailored agents to handle institution-specific workflows.
* The terms Artificial Intelligence (AI) and Machine Learning (ML) are sometimes used interchangeably and distinct at other times. For the purposes of this article, it is the former. There is no Artificial Intelligence without Machine Learning.
Operational Scale and Service Models
Streaming, event-driven processingis starting to replace traditional overnight batch processing models. For decades, firms relied on scheduled jobs, file transfers, and end-of-day reconciliations to move data between accounting, performance, reporting, risk, and operational platforms. While this approach has historically worked well, it is increasingly challenged by growing data volumes, compressed reporting timelines, and rising expectations for intraday transparency. As firms modernize their operating models, many are adopting streaming technologies and real-time data architectures that allow operational events to flow continuously across systems rather than waiting for the next batch cycle.
We have seen vendors like SimCorp and FactSet specifically introduce this technology for accounting and performance jobs. It is especially beneficial for teams who uncover issues in underlying data and cannot wait until the following day to present corrected results to consumers. As soon as a problematic transaction or position is corrected (either directly in the system or through a data feed), event-driven jobs kick-off for the impacted portfolios and results are transmitted through the entire workflow.
Managed services are increasingly paired with technology offerings and can span investment operations, data management, and platform support. This move towards managed services is driven by market pressures that many investment firms know well: growing data complexity, ongoing cost constraints, difficulty sourcing experienced operational talent, and rising expectations for speed, resiliency, and transparency across the investment lifecycle. As a result, firms are striving to ‘insource’ value-added processes and services that differentiate the firm, while outsourcing activities that may be more effectively delivered through strategic vendor partnerships.
Many vendors, including BlackRock, Solovis, SimCorp, and FactSet, have expanded their offerings to include managed services. We see particular value in models that support client-specific configuration and ongoing operational alignment. In our direct experience, SimCorp and FactSet offer a range of outsourced support options that can scale with client needs. Firms can sign up for basic data management checks (e.g., perform cursory checks on my accounting data) or full production support (e.g., if performance data doesn’t pass a series of pre-defined checks, work towards identifying the cause and remediation). Firms that will see the most success with managed services initiatives are those that work closely with the vendor to carefully align people and processes with the overarching platform strategy.
Auditable AI sidebar: Auditability of AI is becoming more of an expectation for firms that are utilizing this emerging technology in their operational processes. “Auditable AI” systems have built-in traceability, explainability, and governance, enabling outcomes to be reconstructed for internal review, audit, and regulatory scrutiny. The capability to audit outputs from LLMs is a key enabler for scaling AI safely across a financial institution.
Cloud Sidebar: A key distinction in cloud-based software is whether a product is ‘cloud-accessible’ or ‘cloud-native’. Legacy applications that were migrated to the cloud without meaningful redesign can be classified as ‘cloud-accessible’, whereas applications that were designed specifically for the cloud are ‘cloud-native’. Cloud-accessible platforms carry constraints from legacy infrastructure and impose some constraints on how fully firms can realize cloud benefits. Cloud-native platforms take full advantage of elastic infrastructure, enabling greater scalability and speed of change.
Conclusion
Vendors are evolving toward more integrated, context-rich platforms that unify performance, risk, and attribution while supporting broader investment decision-making. The practical application of AI is accelerating this shift by embedding operational knowledge directly into workflows and enhancing scalability, transparency, and control. As these capabilities mature, firms that align platform strategy, operating models, and vendor partnerships will be best positioned to realize the full benefits of this transformation.
Download Thought Leadership Article Strategy and Roadmap, System Rationalization Performance, Risk & Analytics Asset Managers, Asset Owner, Wealth Managers Jose Michaelraj, Josh Gerwick, Piers Hansen
