Machine learning and artificial intelligence are regularly in the news: from self-driving cars to clever algorithms that detect fraudulent transactions. However, within risk modelling machine learning has yet to make an impact. This is partly due to the strict regulations these models need to comply with and possibly also due to unfamiliarity with these types of models. Nonetheless, the strong performance of machine learning models cannot be ignored. At RiskQuest we believe these new techniques will be part and parcel of risk modelling within a few years.
Machine learning models are incredibly powerful, primarily because of their flexibility compared to standard statistical techniques. Machine learning is better at handling:
- High number of input variables,
- Non-monotonic data patterns and
- Varying data types
These properties make machine learning models highly suitable for dealing with new data sources, such as PSD2 (sharing bank transaction data). For such complex, unstructured data we expect machine learning models to make the largest impact. Nevertheless, it can be a risky endeavor to develop innovative models because it is not always clear whether these techniques meet the broad range of regulations set and/or checked by the AFM, EBA and ECB.
A case in point is the Dutch allowance affair (‘toeslagenaffaire’) of the Dutch tax authority (‘De Belastingdienst’) in which many parents were erroneously forced to return their allowances due to biases in the machine learning models. Another reason why standard statistical techniques will never be entirely discarded within risk modelling is that they allow users full understanding of the relationship between each risk driver in the model and the impact on risk. Something that is difficult to achieve using machine learning.
The trade-off between model performance and transparency is a balancing act. Introducing machine learning can lead to a competitive advantage. However, fully switching to machine learning is not compatible with risk management’s needs given machine learning’s lack of transparency and the difficulty of understanding the relationship between the different risk drivers in these models. We envision hybrid models combining both existing statistical techniques and machine learning. For instance, machine learning can support model development by quickly identifying patterns over time and by pre-selecting relevant risk drivers that experts can accept/reject on economic grounds. During validation, machine learning can be very useful in benchmarking the explanatory power of the data.
Machine learning is here to stay. As the toeslagenaffaire committee stated: a system like De Belastingdienst cannot function without computers, algorithms, and automated processes. Another example is the discussion paper published by the Dutch central bank (DNB) last year on the “General principles for the use of Artificial Intelligence in the financial sector”, showing that the DNB is seriously preparing for machine learning and artificial intelligence applications. As an industry, we need to adapt to these changing circumstances. Investing time and resources in machine learning models will pay out when machine learning models become market practice in risk modelling.
How soon these changes will happen remains uncertain. At RiskQuest, we are well-prepared and ready to advise on current market practice models, as well as to open up the advantages machine learning can offer you.