business units, said Snyman, then the project would have merely replicated the current poor processes once more. At BNP Paribas, it was recognised that there would be a


need to shift from end-of-day to real-time risk management but it was still relatively early in the journey by late 2014. At least, unlike most banks, it had a risk platform that covered all lines of business and both counterparty and market risk. It was also the only part of the bank that had an IT development team within the group function, reflecting the fact that risk management was deemed to be a special case. It uses Monte Carlo models for calculating regulatory capital, with this coming to be seen as important for the management of the bank, not just a regulatory task. It had automatic monitoring of around 900,000 limits. It had multi- dimensional drill-down capabilities


for risk analysis and


reporting and stress testing for both market and counterparty risk. On the input side, it had a lot of source systems and siloes, market data, calibration, static data (‘absolutely crucial, you have to be a lot more attentive and there are more and more descriptive regulations about how you classify counterparties’, said the bank’s head of risk systems, Mikael Sorboen) and risk indicators (typically more than 100,000 feed files per day, 800 million rows per day, and more than ten source systems). The bank had a three-layer database (Sybase IQ) in its Paris


data centre, with three days of data in the first layer, 50 days in the second and five years of history in the third, amounting to around 40 terabytes of data. Input data was handled by a Java/Python-based in-house developed scripting engine,


then moved in file format into a reconciliation engine (around 90,000 jobs per day), and was then loaded into the three-day layer (it was only loaded into the second layer if there were any adjustments to be made). The bank had an event engine to trigger calculations as soon as possible, with data extracted, calculated and input back into the database. The platform supported 2000 users in 40 sites, generating around 50,000 requests per day. There was an internal business intelligence layer, dubbed MRflex, which was described by Sorboen as a bespoke Java-based wrapper.


In terms of what within this set-up needed to be real-time,


BNP Paribas had identified a number of areas, for compliance and control. These were pre-trade checks against counterparty limits; compliance with trading mandates; detection and prevention of abnormal trading patterns; incremental initial margin requirements from central counterparties (CCPs); and control of market risk exposures per counterparty, net of collateral. Moving towards that was a multi-year work in progress.


It should be noted there are often forces pulling in opposite


directions within institutions. The theory around enterprise risk and finance is now fairly well understood but ahead of the financial crisis, few institutions had made much headway. The need to turn theory into practice is now even clearer but many banks are in a mindset where they are not willing to consider big projects, however strong the rationale.


Risk Management Systems & Suppliers Report | www.ibsintelligence.com


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