Solution introduction
Strict regulatory requirements, record low interest rates and increasing competition from fintechs are some of the challenges that are placing pressure on large financial institutions to be more agile and adapt
But current machine learning approaches lack adaptability and fail to meet expectations of transparency
With Causal AI, banks can build explainable models that unlock the potential in their data, empowering them to be more efficient and flexible.
The regulatory landscape
We understand the time, effort and scrutiny required for models to be deployed within large financial institutions. With causaLens, model developers can spend less time writing reports and more time deriving insights from the analysis. A lot of our technology goes into ensuring that all models, no matter how complex, are fully explainable, with automatic model stability and performance analysis. Furthermore, causaLens models can be automatically reported in templates compatible with SR 11-7 requirements.
Our scientists have extensive experience in finance and will assist with any internal or external audit queries, as well as in developing models and reports tailored to CCAR, ICAAP, DFAST, EBA Stress Test and any other regulatory stress-testing exercise.
