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Dealing With A Data Tsunami: Big Data Analytics For Market Risk Management


The ability to calculate market risk accurately is a key function for financial institutions and, as a universal bank, Societe Generale collects a huge amount of data on a daily basis. Societe Generale needed a solution that could help solve its data management and analytics capabilities, particularly around the new capital requirement calculations mandated by the Fundamental Review of the Trading Book (FRTB) regulatory framework. Banks now need to calculate the amount of capital they must hold to absorb losses from market risk at a much more granular level, which necessitates the management of huge volumes of data.

By implementing Opensee’s innovative data analytics solution, Societe Generale now has no limits on the size of the datasets. In addition, the platform empowers the bank’s business users to autonomously perform any aggregation, calculate ever-growing datasets and manage exponential data growth more efficiently, at a minimal cost without compromising on performance or volume. Having the right tools to access and leverage the most granular level of information and most relevant historical data, they can make advanced business decisions while optimising the bank’s resources, such as capital, credit lines, market risk, exposures, liquidity and so on.


“By implementing Opensee’s transformative technology, Societe Generale has been able to better analyse its risks, delivering solutions faster to end users.” Kevin Bruyère, Deputy Head of Risk on Capital Markets Activities, Societe Generale


Societe Generale Market Risk Department (RISQ/RMA)

Societe Generale is one of the leading European financial services groups. Founded in 1864, the bank has been playing a vital role in the economy for over 150 years. With more than 148,000 employees based in 76 countries worldwide, Societe Generale accompanies 32 million clients throughout the world on a daily basis. Based on a diversified universal banking model, the Group combines financial strength with a strategy of sustainable growth.

Societe Generale Corporate & Investment Banking (SGCIB) is the fourth largest investment bank in the European Economic Area and is present in more than 75 countries around the world. The mission of the Global Market Risk team (RISQ/RMA), headquartered in Paris, is to contribute to the development of the SG Group’s activity by facilitating the objectives of the Business Lines while maintaining independent oversight through risk evaluation and monitoring.


Societe Generale’s Requirements


FRTB and the Data Volume Challenge

During the last financial crisis it was apparent that the regulatory capital for market risk was not adequate enough to cover these risks, therefore, the Basel Committee on Banking Supervision (BCBS) created a new framework called the Fundamental Review of the Trading Book (FRTB). As a result, large financial institutions have had to face a number of adjustments in their methodologies, systems and technologies for calculating the capital charge for market risk. In particular, a new Expected Shortfall (ES) risk metric is to be employed for internal market risk models (replacing the Value at Risk (VaR) method), in addition to other revisions covering the model approval process, taking account of market liquidity of positions, backtesting requirements and many more. Because substantially larger data sets are needed to calculate risk to satisfy FRTB requirements, banks have been forced to significantly rethink their data management

Banks can choose between two methods to calculate capital under FRTB: a standardised approach (SA) or an internal models approach (IMA). There are, however, many new complexities in calculating IMA: beyond the requirement of aligning trading desks and risk management pricing, the increase in data volume (transactions and historical data) is substantial. There are also data management challenges, such as the use of proxy data and managing rules across multiple jurisdictions, with full auditability and data versioning throughout. One strategic question for banks is to choose between the SA or IMA. As the methodology needs to be applied at the trading desk level, results on both approaches need to be analyzed at the most granular level to avoid shortcuts that may call into question the viability of the decision. Banks, therefore, need to be able to simulate, as well as quickly adapt to new situations. This requires more flexibility from data management setups: ability to quickly scale while operating either on-cloud or on-premise, with easy-to-use APIs and adapters for many data types.


“We knew a tsunami was coming”

Similar to other major international banks confronted with having to find adequate IT systems to meet the FRTB rules, Societe Generale had the challenge of dealing with a massive increase in the volume of data, both in terms of storage and analysis. The underlying hardware infrastructure of the incumbent in-memory technology was becoming inordinately expensive to maintain when faced with “tsunami levels” of increasing volumes of data. Due to the unsustainable nature of having to constantly purchase costly new servers providing terabytes of RAM, Societe Generale was actively on the lookout for an innovative technical solution to provide an alternative to manage the challenge of producing and analysing FRTB indicators.


We knew we were facing a tsunami of data and we needed to have a solution to analyse the shortfall and manage the huge volume of data. The ability to accurately calculate market risk is a key function for financial institutions and, as a universal bank, Societe Generale collects a huge amount of data on a daily basis. By using Opensee, we have gained between one to two years in developing a solution because they took care of managing all the low-level details.Philippe Vibien, Global CIO Finance, Risk & Market Data (GBIS), SGCIB


Faster and less costly database management system

Societe Generale had been contemplating various options in order to lower the infrastructure costs linked to their technology and solve its limitations. As Societe Generale has historically preferred to build its own solutions, internal developments were considered and a prototype solution had been built using ClickHouse (which is one of the components of the Opensee solution). ClickHouse is a fast, open-source database management system that allows generating analytical data reports in real time using SQL queries, whose column-oriented storage allows it to run on faster, top-performing (and less expensive) commodity hardware. In the end, Societe Generale decided to choose a technology provider that had the experience of dealing directly with ClickHouse and which had created an entire solution around it, as well as the knowledge and financial expertise of building relevant calculations around specific use cases. Another key requirement for the bank was that the solution would need to have flexibility for data storage (to be either on-premise or cloud-based) in order to cater for differing data confidentiality obligations.


Opensee Solution Strategy

The Opensee’s innovative data analytics solution was chosen by Societe Generale’s RISQ/RMA department because it helped tackle the bank’s data challenges at scale. The Opensee platform empowers the bank’s business users to autonomously perform any aggregation, calculate ever-growing datasets and manage exponential data growth more efficiently, at a minimal cost without compromising on performance or volume.

Specifically, Opensee was chosen for the following three main reasons:

1. Speed: The Opensee platform is based on the high-performance, open-source database ClickHouse;

2. Financial services expertise: In addition to the technological benefits, Opensee provides financial services expertise that provides pre-configured procedures (or calculators) for custom aggregations, which help build internal or regulatory metrics (such as expected shortfall calculations up to the full calculation of FRTB SA or FRTB IMA). This allows business users to
easily plug into dedicated functionalities, easing up constraints on developers;

3. Ability to scale: Due to Opensee’s proven capability in coping with various technical challenges around extremely large and/or inhomogenous datasets, and the inevitable need for increasingly
larger data warehouses, Societe Generale intends to roll out the solution across other risk platforms.


About Opensee

The Opensee platform democratises self-service analytics of all stored data. Users can explore all underlying data in real time and build user-defined functions to improve their response to regulations, such as FRTB, using targeted analytics. The platform can also be used to access all trade and order data to improve market intelligence and execution, or to analyse customer behaviour based on all related data.

In technical terms, the platform uses open source components, is written in the functional programming language Scala, and based on ClickHouse, an open-source big data, analytical column-oriented database to which the company has added a multitude of functionalities that allows non-data scientists to extract, aggregate, analyse and visualise data interactively. It also includes generic APIs to integrate with bank information systems and ingest large volumes of data at high speed, as well as a low-code API that allows users to write code in Python and interrogate data autonomously. The platform can be deployed on premise, in private or public clouds, or in a hybrid environment, with scaling supported by commodity hardware. The technology also offers advanced data management, such as a unique git-like versioning system, in which users can perform a full audit of any change (user-based changes, what-if, collaborative versioning, etc.).


Key Quantitative and Qualitative Results

With Opensee, Societe Generale now has no limits on the size of the datasets. Opensee has allowed Societe Generale to not only improve how it leverages data to make advanced risk decisions and optimise resources, but to also significantly decrease infrastructure costs.


● By giving financial institutions’ business users the autonomy to dive deeper into their data, Opensee solves the complex problem of providing more data to users, saving up to 90% on infrastructure costs

● Societe Generale can now instantly and interactively analyse an unlimited number of years’ worth of data, rather than 3 days’ worth which was previously the case, thereby allowing them to manage risk more efficiently

● The Market Risk team is now able to produce specific reports requested by top management or the regulator in a very short time frame to make quick decisions. Thanks to user-defined functions, model risk analysts can – on demand – simulate the impact of a new model in a time frame of 3-36 hours, as opposed to an average of 2-3 months for previous cycles

● In addition, Opensee simplifies dealing with the outperforming, but complex, distributed database technology of ClickHouse by managing any technical issues that may arise or subsequent releases of the technology. Due to Opensee’s expertise with the underlying technology, time to market has been accelerated by 24-36 months.


Working with Opensee

Opensee was one of the first cohorts of Societe Generale’s flourishing Global Markets Incubator (GMI) programme and had successfully completed two proof of concepts on running calculations for both the liquidity risk profile and the risk-weighted assets (RWA’s) on market activities of the bank. Opensee’s progression to other departments within Societe Generale such as Market Risk is testament to its success in pushing the boundaries of self-service data analytics for financial institutions.


“Opensee brought a cutting-edge solution that combines the transformative use of technology with financial markets business experience. Its tools allow us to access and leverage the most granular level of information and most relevant historical data.” Claire Calmejane, Group Chief Innovation Officer, Societe Generale

“It was really key to have a partner like Opensee to help our development team. Opensee’s financial expertise allows them to have a view over all the necessary use cases, which helps us to capitalise on many areas and really saves us time.” Gary Elezovic, Head of Technical Architecture for Finance Risk & Market Data, GBIS


“Opensee are always available when you need them and have provided us with excellent advice. Our department is one of the first to work with Opensee, and we are pleased to report that the market risk project is sticking to our requirements. I look forward to continuing to work at this level of cooperation in the future.” Yann Le Bars, IT Architect for Finance Risk & Market Data


Future roadmap

Looking forward, Societe Generale plans to leverage the competitive edge that Opensee provides by managing big data via multi-dimensional cubes with low-cost supporting facilities, and expanding it across other areas within the bank.

There are a number of further use cases expected to be launched with Opensee, such as credit risk.


Collaborative Success

Opensee is thrilled to be in the position of providing an innovative technology solution to Societe Generale that delivers new ways to manage huge volumes of data across several parts of the business – all in an economical way. As roll-out of Opensee’s data analytics solution continues across other parts of their business, Opensee looks forward to continuing to build on the successful implementation in what has grown into a highly collaborative relationship with Societe Generale.


“Our mission is to help users realise their data potential by providing the ability to leverage more data than they originally thought possible, by unlocking technological and cost barriers.” Stephane Rio, Founder & CEO of Opensee

Opensee’s Big Data Analytics Platform Goes Live At Societe Generale

● Opensee accelerates how data and solutions are delivered to end-users in Societe Generale’s finance and risk divisions while slashing infrastructure and running costs

● First implemented on ALM and FRTB, several more use cases for Opensee platform at Societe Generale are imminent

● Opensee was part of Societe Generale’s incubator programme


Opensee, the Paris-based fintech offering financial institutions a real-time self-service analytics platform, has gone live with its big data solution at Societe Generale, one of Europe’s leading financial services groups.

Societe Generale started using the Opensee platform for its day-to-day regulatory reporting and business needs in two critical areas: management of the interest rate risk of the Banking Book (IRRBB) by the Liquidity & Asset Liability Management Department and management of market risk-related capital requirements linked to FRTB (Fundamental Review of The Trading Book) by the Risk department (RISQ RMA). The bank will also implement the platform imminently in other areas, notably for its liquidity and risk-weighted assets (RWA) calculations.

Mathieu Giovachini, Global Head of Asset Liabilities Management at Societe Generale, added: “Opensee has helped us to alleviate significant limitations from our existing solution and, while keeping all important functionalities, offers new horizons to our activity.”

Building on a partnership started under Societe Generale Global Markets Incubator (GMI) programme, under which the banking group shared access to its data and infrastructure, the go-live underscores how Opensee’s innovative data analytics solution is helping Societe Generale tackle big data challenges at scale by pushing the boundaries of self-service data analytics.

Claire Calmejane, Group Chief Innovation Officer at Societe Generale, added: “In the Incubator we saw how Opensee had the potential to create value and synergies for the business and our risk management processes, so the partnership has blossomed. Opensee brought a cutting-edge solution that combines the transformative use of technology with financial markets business experience. Its tools allow us to access and leverage the most granular level of information and most relevant historical data.”

Opensee’s platform enables users at the Liquidity & Asset Management Department and other departments to perform any aggregation autonomously, calculate ever-growing datasets and manage exponential data growth more efficiently, at a minimal cost without compromising on performance or volume.

Philippe Vibien, Global CIO Finance, Risk & Market Data (GBIS) at Societe Generale Corporate and Investment Banking (SGCIB), said, “Facing a tsunami of data, we needed a system with the flexibility to retrieve data and manage the risks, so we’re delighted to have the Opensee solution to analyse and manage data sets of unlimited size. We’re now able to produce specific reports for the most senior management or the regulator in a very short time frame to allow quick decision-making. We’ve gained up to 36 months in achieving this objective using the Opensee solution.”

Opensee’s platform saves up to 90% of financial institutions’ infrastructure costs and gives users the freedom to dive deeper into the data and broadens access to it. Societe Generale users can manage risk more efficiently by analysing instantly and interactively a decade of data instead of a few months’ worth beforehand. Thanks to user-defined functions, business users can access any aggregation or analytics on demand in 3 hours instead of waiting for more than two weeks using previous systems.

Stephane Rio, Founder and CEO of Opensee, said: “This is a special moment in Opensee’s highly collaborative relationship with Societe Generale. It’s thrilling to be providing an innovative and economical solution that delivers new ways to manage huge volumes of data across several parts of the business. Our mission is to help financial institutions realise their data potential and gain a competitive edge through self-service data analytics. We look forward to continuing to build on the successful implementation, as roll-out of our data analytics solution continues across other parts of the business.”

Opensee on air!

Big Banks Battling Imminent Tsunami of Big Data

How financial institutions can unlock the unrealised value of big data and address a sector issue

Financial organisations dealing with hundreds of terabytes of data are currently sitting on unrealised opportunities to serve their customers, gain competitive insight and satisfy regulators. How financial institutions can unlock the unrealised value of big data and address a sector issue?

Firms need to work more intelligently with today’s data volumes to become more resilient and better withstand future shocks to the global financial system. Yet many institutions are finding that the complexity and cost of accessing this data are proving obstacles to using this information in a timely and meaningful way, while at the same time being under enormous pressure to economise by delivering much-needed divisional and enterprise-wise operating efficiencies.


What data analysis means today

The challenge of unlocking the business value of data has become an acute issue, with banks, hedge funds, asset managers and insurance companies looking to fully exploit the potential of all data. Financial institutions are sitting on vast amounts of data as they store up to terabytes of newly generated data every day. With many having invested significantly in storing data, there is real business opportunity in being able to make these large quantities of data widely available internally and allow business users to actually “play” with it. The world of finance is on a journey to optimise the use of all the data it holds, and data analytics “at scale” is helping to accelerate and release the value of all the information stored.

While recent storage solutions have allowed institutions to save and retain more information than ever, it is rarely well “cleaned”, homogenised or structured. Further, these storage solutions are not fit for purpose for business users of financial institutions to interactively perform analytics on such large amounts of data. Many business users currently have to make conscious compromises, limiting their ability to fully leverage all the data they have stored. For example, choices around seeing less history or less depth (i.e. accessing only pre-aggregations) in their data; or choosing to expand the data volume, but at an unsustainable and often spiralling complexity and cost. The constant compromise between speed, volume and cost control creates limitations in interactivity and fails to optimise existing financial institutions’ investments in data.


Big data challenges 

Those compromises create countless frustrations on the business side. Here are a few illustrations:

  • Risk managers want to interactively analyse market and credit risk in a single view. They need the capability to go from a high level visualisation of data to granularity of all available data in any direction, with any depth and any history,  and be able to interrogate the data in a way that’s meaningful and that they can easily understand.
  • Asset managers want to precisely analyse all the data at hand to deliver “true” best execution and minimise transaction costs.
  • Treasurers want the ability to aggregate all available data without limitations to precisely forecast their liquidity needs or surplus at any time.


Helping financial firms realise true data potential through analytics

Opensee, formerly ICA, was born out of our personal frustration as former capital markets senior executives, at not being able to find an appropriate Big Data analytics solution that would enable us to easily and efficiently manipulate, explore and perform “what-if-analysis” on the hundreds of billions of data we were handling across the business. So we created a team of financial industry and data analytics experts and formed Opensee, to build our own solution and help others still struggling to unlock vital business-user-led opportunities, “dive deeper” into their data.

We are focused on pushing the boundaries of self-service data analytics for financial institutions, building on our capital markets heritage to help banks, hedge funds and asset managers tackle their data challenges – at scale. Opensee gives financial institutions’ business users the autonomy to perform any aggregation and, more broadly, any analytics on demand.


A background of growth

We have seen an acceleration in the way our initial Tier 1 banking clients are using our Big Data analytics solution, from the Risk department to Treasury and then Commercial too. At the same time we are helping business users of smaller banks or on buy-side (asset managers, hedge funds)  harness 100% of their vast quantities of data and tackle their own data challenges. Our rename reflects these changes, marking a new chapter of growth.

We wanted a name that more closely captures our vision on big data and embodies our long-held belief that there is a better way to help business users across financial institutions analyse today’s very large amount of available data, deeper and faster.

Discover Stephane Rio’s vision in Finextra, the independent newswire and information source for the worldwide financial technology community.