In today’s risk management scene, some major European banks are seriously thinking about ditching their in-house models for regulatory capital requirements, according to the latest scoop from Risk.net magazine.
Using internal models instead of standardized ones typically means you require less capital. But here’s the catch: regulators want the valuation models used by both the front office and risk management to align. If they don’t, you get discrepancies in Profit and Loss (PnL) that can lead to some serious headaches.
The root of the problem is that front-office models and risk management models have different priorities. Front-office folks want accuracy for making spot-on bids in the market, while risk management needs speed to crunch a wide range of risk factors and run stress tests over longer periods.
The smart solution? Use the same pricing model for both front office and risk management. But, that model has to be very fast and very accurate to meet both departments’ demands.
Here’s where Riskfuel comes in. We build lightning-fast machine-learned copies of a bank’s internal models. These machine learning (ML) models have the accuracy needed by the front office and the speed required by risk management. So, with Riskfuel, one risk model can be used across the entire organization. In an industry where speed and accuracy is everything, Riskfuel has the answer to a tricky problem.