\n<\/em>\u2014 Ralph H. Groce III, Former CIO at Wells Fargo<\/p>\n
Financial data often relies on complex relationships and interdependencies; shifts in one data point can unpredictably impact others. For example, an interest rate change can simultaneously impact bonds, currencies, stocks, and commodities, requiring sophisticated models to capture holistic effects.<\/p>\n
Data volume<\/h3>\n
The amount of financial data generated worldwide is staggering, with capital markets producing vast volumes of transactional, market, and client data every second. The NASDAQ sees 35+ million trades per day<\/a>, and Visa averaged 700M transactions per day in 2023<\/a>.<\/p>\n Regulatory regimes like MIFID II require banks, asset managers, hedge funds, etc., to report 65 data fields for nearly every equity, fixed income, and derivative trade (137B derivatives contracts alone were traded in 2023<\/a>).\u00a0These added processes necessitate efficient retrieval, formatting, and storage of massive amounts of data.<\/p>\n Many FIs have been operating for decades and rely on legacy systems. These technologies are often deeply integrated into operations and can be costly to replace. Many firms made incremental updates to infrastructure instead of full replacements, creating inefficient data flows and architecture. As explained to Insight by the Former Chief Data Architect at J.P. Morgan: \u201c…that’s hundreds of [data transformation] steps and hundreds of systems that have been building up for 70 years…the likelihood of something going wrong is very high.\u201d<\/p>\n Organizational silos emerge as distinct business units have unique data needs. Based on our conversations with the Former CTO Architect for Global Risk and Control at Citi, “Each line of business is building their own systems which has led to this hodgepodge mess, and that\u2019s the only way to put it\u2026 every large bank has this problem.”<\/span><\/p>\n As a result, data becomes challenging to locate across an organization. \u201cAI\/ML data scientists were spending an inordinate amount of time, something like 60% to 70%, just trying to find the right data,” explained the Former CTO for Data and Analytics at J.P. Morgan.<\/p>\n This can delay analysis, increase errors, duplicate work, and limit an organization\u2019s ability to address emerging opportunities and risks.<\/p>\n Reconciling internal data can be challenging and adding additional counterparties, stakeholders, and data vendors makes the task nearly unmanageable.<\/p>\n \u201cReconciliation faces critical challenges, with fragmented systems and data inconsistencies threatening operational efficiency and compliance in an era of soaring transaction volumes and tightening regulatory demands.\u201d Recent events like the 2023 banking failures, rapid interest rate hikes, and heightened geopolitical tensions have amplified economic risk, pushing regulators to apply greater oversight. Regulatory bodies are increasingly data-driven, elevating expectations for real-time compliance and detailed data disclosures.<\/p>\n The stakes for non-compliance are high, demonstrated by the CFTC’s $200 million fine<\/a> on J.P. Morgan for misreporting critical data fields in May 2024. Aggregate regulatory fines are also at historic levels. The SEC issued nearly $5B in enforcement actions<\/a> in 2023, second only to 2022\u2019s\u00a0<\/a>record-setting $6B<\/a>.<\/span><\/p>\n <\/p>\n Additionally, the\u00a0scope of regulatory requirements is expanding. MIFID III (planned for 2025\/2026) is expected to significantly expand reporting requirements,<\/span>\u00a0and operators are directly feeling these compliance pressures.<\/p>\n AI-powered models \u2014 from credit scoring algorithms to fraud detection systems \u2014 are only as effective as the data feeding them. As AI use expands, many firms are realizing their existing data infrastructure is not prepared.<\/p>\n \u201cWe are starting to see firms prioritize large data transformations to harness the power of AI. They are putting plans in place and increasingly looking to outsource data services.\u201d Poor data hygiene \u2014 such as incomplete or inconsistent data \u2014 can compromise AI model performance. This gives way to severe consequences in financial contexts, where accuracy is critical.<\/p>\n \u201cYour data must be AI-ready. Raw numbers lack meaning without context\u2014tagging and maintaining high data quality are crucial to enabling AI.\u201d<\/em> AI is increasingly used for real-time decision-making. However, legacy systems weren\u2019t designed to support such continuous, high-speed data flow. As a result, the gap between the demands of AI models and the capabilities of existing infrastructure is widening.<\/p>\n Financial clients, investors, and limited partners (LPs) increasingly expect portfolios and products tailored to their specific goals, risk profiles, and values. This is driven, in part, by increasing awareness of risk factors\u00a0like ESG exposure, interest rates, and geopolitical events.<\/p>\n “Clients now want specific factors and sectors in their portfolio construction, which impacts everything from research to risk models and data. There are hundreds of different parameters to consider; platforms must be both highly adaptive to client needs and dynamic enough to consolidate complex information into a single interface.\u201d <\/span><\/em><\/p>\n \u2014 Kfir Godrich, Chief Innovation Officer at BlackRock<\/span><\/p>\n ESG risk factors have become a central focus, with 89% of investors now considering ESG criteria<\/a> in their investment decisions. Isolating specific risks is daunting given the multi-layered nature of financial instruments. Mutual funds and ETFs often comprise hundreds of underlying holdings, making accurate calculations dependent on evaluating each security’s ESG rating and aggregating the results based on portfolio weight.<\/p>\n The front office includes \u201cclient-facing\u201d activities such as trading, investment diligence, and wealth management, where revenue generation and client relationship management are key.<\/p>\n In recent years, several startups have emerged to streamline search and knowledge management, leveraging AI for front-office research, analysis, and decision-making. Hebbia<\/a>, Rogo<\/a>, and Sibli<\/a> help enable banks, asset managers, and PE firms to analyze unstructured data quickly.<\/p>\n Auquan<\/a> automates tasks like due diligence and portfolio monitoring by aggregating data from management accounts and public filings, giving FIs real-time views of business risks. \u201cWe use finance-specific RAG agents to automate complex diligence and monitoring workflows,” explains Founder and CEO Chandini Jain.<\/p>\n Reflexivity<\/a> allows asset managers to analyze companies and test investment theses using natural language. \u201cMapping structured and unstructured data using our proprietary knowledge graph enables delivery of instant answers to deep computational questions,” says Cofounder and CEO Jan Szilagyi.<\/p>\n With 40% of FIs viewing front-office research as the top opportunity for AI<\/a>, these tools have the potential to significantly reduce research hours and streamline analytical workflows.<\/p>\n Startups are launching to streamline front-office data analysis for PE\/VC as well. Dealbase<\/a> automates customer file analysis via \u201cone-click outputs\u201d in Excel. Quikirr<\/a> helps simplify revenue analysis, data cuts, and KPI evaluation using AI for data reconciliation. \u201cWith a focus on deep Excel integration, we\u2019re starting with common data room analyses and expanding to complete quantitative due diligence workflows,” says Quickirr Cofounder and CPO Mateo Varela.<\/p>\n Foresight<\/a> combines deal sourcing and due diligence into a single platform, empowering private market investors to optimize investment decisions.<\/p>\n As AI tools improve, they will reshape how FIs handle research, analysis, and decision-making across the deal lifecycle.<\/p>\n The middle office<\/strong> bridges the front and back offices, ensuring necessary data, documents, and policies are in place to support workflows like portfolio monitoring. EdgeConnect<\/a> uses AI to reconcile data from company systems of record (e.g., ERP\/CRM) into a single, standardized view. \u201cWe combine PE expertise with software innovation to transform portfolio management and company reporting,” explains Founder and CEO Polina Kyriushko.<\/p>\n Shelton<\/a> uses AI to provide investors with real-time portfolio analytics, which is critical for capital pacing, performance attribution, and allocation strategies.<\/p>\n Data management platforms offer versatile solutions, supporting everything from cleaning data to regulatory reporting. Arch<\/a> provides “LPs, advisors, and treasury teams with a centralized hub to collaborate and access portfolio data at both the LP and asset levels. From small family offices to large private banks, Arch helps unite stakeholders on a single, integrated platform,” says Cofounder and CEO Ryan Eisenman.<\/p>\n Automated-Data<\/a> leverages AI to interpret the semantic meaning of data, simplifying data integration and reconciliation. OutcomeCatalyst<\/a> streamlines the preparation of complex alternative datasets, ensuring seamless integration with other data across the organization. KOR<\/a>\u2019s platform helps firms maintain compliance by increasing data transparency within transaction reporting. “Our mission is to bring an excellent user experience to achieving compliance through accuracy, completeness, and timeliness,” says KOR Founder and CEO Jonathan Thursby.<\/p>\n The back office handles essential operational and administrative tasks, including transaction processing, trade settlement, record-keeping, and accounting. While incumbents typically focus on pure service delivery, 4Pines<\/a> and TrustServe<\/a> are tech-enabled fund administrators focused on alternative investors. 4Pines enhances accounting workflows for PE firms. TrustServe\u2019s solutions cater to a broader range of alternative investors, spanning hedge funds, private equity, real estate, and family offices \u2014 with a focus on reporting and compliance.\u201cWe are a team of experts delivering high-quality professional services via cutting-edge technology to afford GPs greater focus on fundraising and deal-making,” explains TrustServe Founder and CEO Jorge de Cardenas.<\/p>\n These approaches combine technology with personalized service, modernizing and improving the offering while preserving a hands-on touch.<\/p>\n Software vendors are also emerging to centralize historically ad-hoc finance workflows. By centralizing capital calls, distributions, and investor reporting, Maybern<\/a> helps finance teams reduce manual work and improve operations. Bunch<\/a> helps fund managers and private investors set up, manage, and monitor investments. LemonEdge<\/a> offers a robust, end-to-end accounting system for private markets, automating multi-ledger management and event-driven transactions.<\/p>\n Together, these tech-enabled services and software solutions are bringing greater efficiency and centralization to an increasingly complex office of the CFO.<\/p>\n![\"\"](\"https:\/\/www.insightpartners.com\/wp-content\/uploads\/2024\/12\/3.png\")
<\/p>\nLegacy infrastructure<\/h3>\n
Data silos<\/h3>\n
\n<\/em>\u2014 Aman Thind, Chief Architect at State Street<\/p>\n![\"Critical](\"https:\/\/www.insightpartners.com\/wp-content\/uploads\/2024\/12\/chart-01-scaled.jpg\")
<\/p>\nThe industry is reaching a breaking point catalyzed by regulation, AI, and personalization<\/h2>\n
Increased level of regulatory scrutiny<\/h3>\n
![\"\"](\"https:\/\/www.insightpartners.com\/wp-content\/uploads\/2024\/12\/4.png\")
<\/p>\nReal demand for AI<\/h3>\n
\n<\/em>\u2014 John Costigan, Chief Data Officer at FactSet<\/p>\n
\n\u2014 Srinivas Gunturu,\u00a0<\/em>Director of Engineering Enterprise Data Delivery at Bloomberg*<\/p>\n![\"\"](\"https:\/\/www.insightpartners.com\/wp-content\/uploads\/2024\/12\/chart-02-scaled.jpg\")
<\/p>\nHeightened expectations for personalization and risk awareness<\/h3>\n
Companies reshaping the landscape<\/h2>\n
![\"Functional](\"https:\/\/www.insightpartners.com\/wp-content\/uploads\/2024\/12\/Chart07-1-2-scaled.jpg\")
<\/p>\nFront Office<\/h3>\n
Middle Office<\/h3>\n
Back Office<\/h3>\n
![\"Finserv](\"https:\/\/www.insightpartners.com\/wp-content\/uploads\/2024\/12\/market-map-3-scaled.jpg\")
<\/a><\/p>\n