When Financial Data Becomes Explainable: Opensee’s Approach to Real-Time Analytics and Risk Intelligence
Published: 2026-09-21 | Author: VisionariesNetwork Team
Financial institutions generate enormous amounts of data every day. Market activity, transactions, positions, risk indicators, liquidity information, and counterparty exposures can produce billions of individual data points. The challenge is no longer simply collecting this information. For banks, asset managers, and hedge funds, the greater challenge is being able to explore it quickly, understand what it means, and use it to support decisions.
This is the problem at the heart of financial data analytics and real-time risk management. Opensee was created by former traders, risk officers, and capital markets practitioners who had experienced this challenge firsthand. In 2018, the Paris-founded company began gaining recognition among major financial institutions for its ability to help business users access and analyze data stored across large data environments.
Today, Opensee provides an AI-powered data platform designed for financial institutions, combining data quality, aggregation, analytics, and reporting. Its platform enables users to interact with massive multisource datasets while supporting real-time analysis and increasingly conversational approaches to working with complex financial information.
Built from a Problem Inside Financial Markets
Opensee's origins are closely connected to the practical difficulties of working with large financial datasets. Its founding team came from the finance industry and had encountered situations where existing tools made it difficult to efficiently explore data and perform what-if analysis across hundreds of billions of data points.
Rather than accepting those limitations, the team developed its own solution.
That industry experience continues to shape the company's technology. Opensee combines financial expertise with big data engineering, creating a platform intended specifically for the complex requirements of financial institutions.
Its early development demonstrated the relevance of this approach. In 2019, Opensee joined Societe Generale's Global Markets Incubator program, gaining access to the bank's infrastructure and large-scale datasets. The collaboration provided an opportunity to test the platform in a demanding financial environment. Societe Generale subsequently implemented Opensee for market risk management and liquidity processes.
The experience helped establish a foundation for the company's expansion within the financial sector.
Making Massive Datasets Easier to Explore
Large datasets are valuable only when organizations can actually work with them. Financial institutions may have extensive information stored in data lakes, but extracting meaningful insight can be difficult when users depend heavily on technical teams or fragmented analytical processes.
Opensee addresses this challenge through a low-code and no-code approach designed to give business users greater access to financial data. Its platform integrates data management at scale with interactive analytics, enabling users to investigate information without necessarily requiring extensive programming expertise.
This can be particularly useful for financial risk teams, where questions often change quickly. Analysts may need to examine a particular exposure, compare scenarios, investigate an anomaly, or understand how a portfolio behaves under different conditions.
Interactive access to underlying data can help reduce the distance between asking a question and obtaining an analytical answer.
Connecting Data Management with Risk Intelligence
Risk management requires more than isolated calculations. Market risk, counterparty risk, and liquidity considerations can involve information from multiple sources and require institutions to produce consistent analysis and reporting.
Opensee's platform brings data management, analytics, and reporting together within a single environment. Its deployments with major global systemically important banks have included first- and second-line-of-defense use cases for producing, reporting, and managing market and counterparty risks.
The broader value of this approach lies in connecting the underlying data with the processes through which institutions monitor risk. Instead of viewing data management as a separate technical function, organizations can integrate data exploration and analytics into risk workflows.
This becomes increasingly important as financial institutions face growing data volumes and increasingly complex reporting requirements. Faster access to reliable information can support more responsive analysis while improving the ability of teams to investigate the details behind risk indicators.
Bringing AI into the Financial Data Conversation
Opensee's evolution is now extending into artificial intelligence through Agensee, its agentic AI capability. Rather than requiring users to navigate complex datasets manually for every analytical question, Agensee allows teams to interact with data conversationally.
The system is designed to support users ranging from senior decision-makers to risk analysts. Its agent-driven reasoning capabilities can help automate parts of daily analytical workflows, including the production of explainability reports and the detection and correction of data anomalies.
Explainability is particularly significant in financial services. As AI becomes more involved in analytical and decision-making processes, organizations need ways to understand and communicate how conclusions are reached. Automatically generated explainability reports can help bring greater transparency to complex analytical processes.
Similarly, identifying data anomalies is important because analytical results depend on the quality of the information being analyzed. By incorporating anomaly detection and correction into its AI capabilities, Opensee is extending its focus from simply analyzing data toward helping institutions improve how they work with it.
Designed for the Scale of Modern Finance
Opensee's customer base spans banks, asset managers, and hedge funds, organizations that often operate with exceptionally large and complex datasets. The company's platform is designed around the needs of these environments, where speed, reliability, data quality, and analytical flexibility can directly influence how teams work.
The company's growth has also been supported by investment and institutional partnerships. In 2022, Opensee raised €11 million in Series A funding led by Omnes Capital, with participation from Laurion Capital and Societe Generale Ventures.
From its Paris origins, the company has expanded into an international organization with more than 70 employees across the USA, UK, France, Germany, and Singapore. Its team brings together former finance professionals and technology specialists, combining knowledge of financial markets with expertise in data and software.
This combination remains central to Opensee's identity. The company is not approaching financial analytics solely as a technology problem or solely as a finance problem. Its platform reflects the intersection of both.
Making Data More Useful, Transparent, and Actionable
The next stage of financial analytics is likely to involve more than increasingly sophisticated dashboards. Financial institutions need systems that allow their teams to investigate data, test scenarios, identify problems, and explain conclusions with greater speed and clarity.
Opensee's development reflects this shift. From its origins as a solution created by financial-market practitioners to its current AI-powered platform, the company has focused on making complex financial data more accessible and useful.
Its combination of large-scale data management, interactive analytics, reporting, and agentic AI illustrates how financial institutions can move toward more connected analytical environments. For organizations managing enormous datasets, the ability to ask questions of their information in real time—and understand the reasoning behind the resulting insights—can become an important part of modern risk and financial management.
Stéphane Rio, CEO & Founder
“The value of financial data does not come from its volume alone. It comes from making complex information accessible, explainable, and useful when decisions need to be made.”
FAQs
1. What does Opensee specialize in?
Opensee provides an AI-powered data platform for financial institutions, combining data quality, aggregation, analytics, and reporting for large and complex financial datasets.
2. Who uses Opensee's platform?
Opensee works with financial institutions including banks, asset managers, and hedge funds, supporting analytical and risk-management use cases.
3. How does Opensee help financial institutions analyze data?
Its platform enables users to interactively explore and analyze massive multisource datasets through low-code and no-code capabilities, supporting faster access to financial insights.
4. What is Agensee?
Agensee is Opensee's agentic AI capability that enables users to interact with complex datasets conversationally while supporting automated reasoning, explainability reporting, and data-anomaly detection and correction.
5. Why is explainability important in financial analytics?
Explainability helps financial teams understand and communicate how analytical results are produced, which can be particularly valuable when complex datasets and AI-driven processes are involv
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