Latest Insights

Quantification of model risk

Risk management and treasury specialists are using diverse models on a daily basis to manage various risks. It is easy to forget about the risk that is implied in using the model itself. What we refer to as ‘model risk’ can arise due to misuse of the model, incorrect model choices or inappropriate model use.

Risk management in the wake of Basel 3:

The future of banking

Much has been done to define new regulations for the banking sector since the financial crisis. The prudential rules of Basel 3 with the so-called ‘final reform’ of December 2017 (commonly referred to as Basel 3.5 or Basel 4), for example, are as good as ready. So what can banks expect during the coming years?

Functional Data Management: What makes data projects very costly and never-ending?

Missing the functional piece in a data project puzzle – The financial industry is going through a disruptive phase, in which buzzwords such as blockchain, big data and deep learning are enticing financial institutions to ride the technological wave. Solid data management is the foundation of these developments. Financial institutions not only have an internal drive to create this foundation, as this improves analysis and decision-making, but also face challenging regulations imposed by national and international supervisors.

Challenges with the replacement of IBOR benchmark interest rates

Benchmark interest rates are an essential part of financial markets. These interest rates are used for numerous financial products, such as bonds, loans and derivatives, and in the construction of discount curves. This has applications in fair value calculations, hedge strategies, sensitivity analysis, treasury and risk management systems, and much more.

The impact of liquidity spread risk on banks’ earnings

What exactly are liquidity spreads and liquidity spread risk? And how can banks incorporate liquidity spread risk into their risk management framework?