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A Short History of Global Historic Crash Events

Stress Testing is a well established risk management tool used by financial institutions. Stress Testing is highly prioritized with respect to banks' capital due to the regulatory requirements that banks have to comply with.

Stress Testing has applications across different asset classes and business objectives. Macro Stress testing for instance aids in exploring how past and hypothetical future shocks transmit to loss and the income conditions for portfolios. A major objective in this regards is to examine solvency and profitability in the advent of crisis. The following article provides a brief outline of historic crash events affecting Financial Markets pre 2008.

System Dynamics Primer

System Dynamics was developed as a method for analyzing complex dynamical systems in the context of economy and social sciences with the aim to understand how and when systems and policies produce an unexpected outcome. The approach is focusing on certain aspects of reality by identifying constraints on any targeted outcome, time and reliability. The System Dynamics method zooms into relevant aspects of systems' behaviour and is thereby providing a useful method to support decision making processes. Applications of System Dynamics exist in multiple fields, namely project, risk and policy management as well as product development.

AI-ML in Banking and Finance

Enabled through advances in Information Technology (IT), Artifical Intelligence (AI) and Machine Learning (ML) find applications across multiple areas of Finance. Today, the availability of machines and algorithms specialised in learning and decision making from data make applications possible that draw insights from complex and high dimensional data. In addition, novel data sources lead to an increase in data complexity whilst providing a lever to usefulness of AI-ML techniques.

Customer Segmentation and Churn Prediction

In Marketing Analytics, segmentation of customer data has many use cases: targeting groups of potential customers in marketing for acquisition, improving servicing standards to customers, enhancing customer relationships and identifying customer segments for retention strategies.

In addition, novel data sources lead to an increase in data complexity whilst providing a lever to usefulness of AI-ML techniques.

Bayesian Networks

Decision making under uncertainty often has to consider a large number of factors. Bayesian Networks (BN) belong to the type of Probabilistic Graphical Models and provide a suitable framework to aid complex decision making across a wide range of applications. The structure of a Bayesian Network is represented by a Directed Acyclic Graph (DAG) consisting of nodes and directed arcs. In particular, each node`represents a random variable with nodes connected by directed arcs representing dependencies. With the following blog a brief primer on Baysian Networks is presented.