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Applying an internal models approach to real-time counterparty risk exposures
Technology Category
- Analytics & Modeling - Real Time Analytics
Applicable Industries
- Finance & Insurance
Applicable Functions
- Business Operation
Use Cases
- Regulatory Compliance Monitoring
Services
- System Integration
The Challenge
Intesa Sanpaolo, an Italian banking group, was facing a challenge in its risk management infrastructure. The bank's trading decisions on Over-the-counter (OTC) derivatives were based on multiple modeling techniques. Traders either had to run overly conservative add-ons or call risk teams for internal model results. This process was time-consuming and resource-intensive. The bank wanted to help front- and middle-office teams work together more effectively by enabling its trading desks to access the same approved internal models for counterparty risk that its risk management team was already using. This would empower traders to gain deeper insight into the total exposure to a given counterparty much more quickly, helping them understand the limits before making a trade, and avoid the risk of breaching those limits.
About The Customer
Intesa Sanpaolo is an Italian banking group with a market capitalization of EUR 48.7 billion (July 2017) and a leading position in retail banking, corporate banking, and wealth management. Its investment banking division, Banca IMI, employs more than 800 people at its headquarters in Milan, offices in Rome and Italy, and a subsidiary in New York, and trades in equities, bonds, and derivatives with 350 clients in more than 70 markets. Like many banks, Intesa Sanpaolo is transforming its risk management infrastructure to deliver on-demand risk analytics to support its investment banking division and make risk management processes more efficient.
The Solution
Intesa Sanpaolo and IBM built a solution that lets traders run what-if analysis on counterparty risk in real time, providing more accurate exposure measures that are consistent with the risk team’s models. The bank decided that the best strategy was to give its trading desks access to the risk management department’s existing IBM Algo One platform. The Algo solution had already achieved internal model approval from the local regulator for market risk and counterparty credit risk. As a result, the risk metrics from Algo were regarded as the “official numbers” within the bank for optimizing regulatory capital requirements. In addition, the bank had already implemented the Algo real-time credit engine to deliver counterparty risk metrics in real time. To help front-office users adopt the tool, the project team designed an intuitive interface that not only enables what-if analysis of potential deals, but also provides useful metrics such as current exposure, expected positive exposure (EPE) and potential future exposure (PFE) for each counterparty and for the bank’s top ten counterparties.
Operational Impact
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