Causal AI: Introduction and applications in risk management and finance

The increasing availability of large amounts of data and powerful machine learning methods has fundamentally changed the financial industry in recent years. In risk management in particular, data-driven models are now routinely used to forecast credit risks, market movements or fraud probabilities. However, despite high predictive accuracy, traditional machine learning approaches have conceptual limitations: they provide correlations but no cause-and-effect relationships. This poses a significant problem, particularly in regulated areas such as banking and insurance, where decisions need to be explainable, stable and controllable. Causal AI – the combination of modern machine learning methods with causal inference – promises a paradigm shift here [Feuerriegel and Spindler 2025]. Instead of merely updating past patterns, Causal AI aims to estimate causal effects and thus create robust decision-making bases for risk, capital and management issues. This article provides an introduction to the basic principles of Causal AI and uses selected fields of application to show the potential this approach offers for risk management and financial practice.

1. From predictive to causal modeling

1.1 Limitations of classic machine learning models in the financial context

Classic machine learning models are primarily designed for forecast optimization. They detect patterns in historical data and use these to make predictions based on correlations. Typical applications in risk management include credit scoring models, default forecasts or value-at-risk estimates.
However, the problem is that such models implicitly assume that the underlying patterns remain stable. However, changes in the economic environment, regulatory interventions or strategic measures regularly cause the predictive quality of the models to collapse. In these cases, purely correlative models quickly lose their informative value [see Pearl 2009].

1.2 Basic idea and delimitation of Causal AI

Causal AI extends data-driven methods to include explicit assumptions about cause-and-effect relationships. The central goal is not the prediction of a target variable per se, but the estimation of causal effects: What happens to the risk portfolio when interest rates rise? How does the probability of default change when lending guidelines are adjusted?
Methodologically, Causal AI is based on formal causal models, such as directed acyclic graphs, as well as on econometric concepts such as counterfactuals and treatment effects [see Imbens/Rubin 2015 and Chernozhukov et al. 2025, Schölkopf et al. 2021]. Modern approaches combine these concepts with machine learning, for example in the form of double machine learning or causal forests [see Chernozhukov et al. 2018, Bach et al. 2022].

2. Fields of application in risk management and finance

2.1 Credit and counterparty risks

An important area of application for Causal AI is the analysis of credit risks. While traditional scoring models only predict the probability of default, causal models allow statements to be made about which factors actually cause the default.
For example, it is possible to investigate whether an increase in interest rates causally leads to higher default rates or whether observed correlations are due to selection effects. This is particularly relevant for the design of credit guidelines and the evaluation of regulatory measures.

2.2 Market and liquidity risks

Causal AI also offers added value in the area of market and liquidity risks. Causal models allow the simulation of stress scenarios in which targeted interventions – such as monetary policy measures or market distortions – are analyzed.
In contrast to purely historical stress tests, causal approaches can explicitly take into account how market participants react to new information and how these reactions affect prices and liquidity.

2.3 Regulatory requirements and model governance

Another advantage of Causal AI lies in the improved explainability of models. Regulatory frameworks such as Basel III or Solvency II increasingly demand comprehensible and robust risk models.
Causal structures make assumptions explicit and allow a transparent discussion of model risks. Causal AI can thus contribute to better model governance and the reduction of model uncertainty.

3. Differentiation from interpretable ML

In recent years, interpretable machine learning, also known as explainable AI (xAI), has become very popular in the industry. Here it is very important to understand the difference to Causal AI. Interpretable machine learning makes the correlation patterns, which can be very complex, transparent and explains why a prediction was made. However, it cannot reveal causal relationships, so it cannot answer questions such as: “What is the causal effect of variable X on the outcome?”

Conclusion and outlook

Causal AI represents an important development step for risk management and the financial sector. While traditional machine learning models focus primarily on forecasting accuracy, the causal approach enables a deeper understanding of the underlying mechanisms of action. This perspective is becoming increasingly important in an environment of growing uncertainty, regulatory requirements and structural changes.

It should also be noted that combining causal AI with predictive models is a new development. Predictive models often break down when they are supposed to predict situations that are atypical for the training data. Here, the combination with Causal AI provides better, more robust predictions in “new” situations, as the underlying mechanisms are learned, which can then also be generalized.
The application examples presented show that Causal AI should not be seen as a replacement, but as a supplement to existing models. Its greatest added value lies in areas where decisions are to be actively shaped – for example in the adjustment of credit guidelines, the evaluation of regulatory interventions or the simulation of stress scenarios.
With the increasing maturity of causal methods and their integration into modern software tools such as DoubleML [Bach et al. 2022], it is to be expected that Causal AI will increasingly find its way into operational financial practice in the coming years. However, this will require a close integration of economic theory, data expertise and regulatory understanding. Causal AI is therefore less of a purely technical instrument and more of a methodological framework for well-founded, responsible decisions in the financial system.