IMD & IRD Awards 2023: Best risk data aggregation initiative—Opensee

Overview

Ensuring data accuracy is a critical starting point for risk management. Multiple data sources can cause fragmentation, potentially increasing errors and inconsistency. With the size of financial institutions’ datasets growing exponentially, it is essential to minimize the risks when running large aggregations and computations. The traditional approach to risk management has relied heavily on manual validation and exploration; however, this does not meet the demands for real-time insights and decision-making. Opensee provides financial institutions with controlled views of their risk profiles by giving them access to data at scale with embedded analytics, allowing them to achieve their risk management goals and regulatory reporting requirements.

The solution

Opensee specializes in any data-intensive use-case where scalability and flexibility need to be combined. It started with risk use-cases, recognizing that analyzing risk is as important as calculating it. It allows users to navigate and aggregate large datasets with self-service functionalities without compromising scalability, granularity, real-time access and cost efficiency. Users have full control over their business intelligence but can also make use of pre-configured Python calculators and dashboards for various use-cases, such as liquidity and Fundamental Review of the Trading Book (FRTB). They can also utilize the calculations embedded in the solution, as well as customize them to create their own metrics.

Opensee also includes risk management frameworks that incorporate scenario analysis, stress testing and sensitivity analysis, allowing firms to assess the potential impact of various risk factors.

Secret sauce

Opensee’s initiative to leverage artificial intelligence (AI)-embedded tools for data quality and data exploration addresses the growing data challenges facing financial institutions. AI offers significant potential in assisting users pre- and post-aggregation and can transform the way financial institutions approach risk management. Opensee recognized that automating data analytics and capitalizing on advanced algorithms could help financial institutions identify risk more efficiently and accurately, therefore enabling users to respond proactively to market changes. Opensee’s certification module with its configurable workflow adjustment features ensures the accuracy of the finalized reports and results.

Recent milestones

• The appointment of a number of key hires to support global expansion, including a new chief growth officer
• The establishment of a representative office in Singapore to support expansion into South East Asia
• Series A funding round, with €11 million ($11.9 million) raised from Omnes Capital together with Laurion Capital and Societe Generale Ventures

Future objectives

• Broadening the range of embedded-AI analytics available on the platform to more use-cases, such as market risk, credit risk, liquidity risk, and trade analytics to allow business users to generate business intelligence quickly
• Launching a drag-and-drop interface to provide users with greater control of the data model
• Developing a more interactive and dynamic user interface to simplify access to data and analytics through advanced data visualization tools

Why they won

Paris-based Opensee follows up last year’s win in this category by winning it again, thanks to its eponymous bid data and analytics platform designed to allow capital markets firms to aggregate large, complex datasets with self-service functionality without compromising on scalability and granularity. Key to the firm’s success is its use of AI technology and algorithms to largely automate data analytics functions, allowing users to produce more timely and accurate risk measures/computations. It aims to extend its AI-driven analytics strategy to include a variety of new use-cases, such as market risk, credit risk, liquidity risk, and trade analytics, which will no doubt strike a chord with its growing user base.