Risk managers of asset management companies monitor portfolio risk metrics such as the Value at Risk in order to analyze and to communicate the risks timely to portfolio managers, and to ensure regulatory compliance. They must investigate the possible causes if a portfolio risk significantly increases or breaches a regulatory limit. However, monitoring can quickly become overwhelming, time and labor-intensive as each risk manager has to deal with over a hundred portfolios, numerous daily market data, and hundreds of risk factors of the supervised portfolios and of their securities. Particularly, understanding the interrelations between incidents in different portfolios beyond high level indicators is important. However, analyzing these interrelations manually is one of the most difficult tasks. In this paper, we describe and demonstrate how automatically generating causal graphs can address the capacity problem of practitioners in risk management, who are facing more and more capital markets based risk data daily on the portfolio level alone. Based on a proof of concept implementation, we compare a pairwise causal-inference-based approach with a clustering-based construction approach. We discuss the advantages and disadvantages of both approaches, both computationally and based on the resulting structure. Based on our initial findings, we outline further challenges and research topics
