The most intuitive way to model a transaction in the financial world is through a Graph. Every transaction can be considered as an edge between two vertices, one of which is the paying party and another is the receiving party. Properties of these nodes and edges directly map to business problems in the financial world. The problem of detecting a fraudulent transaction can be considered as a property of the edge. The problem of money laundering can be considered as a path-detection in the Graph. The problem of a merchant going delinquent can be considered as the property of a node. While there are many such examples, the above help in realising the direct mapping of Graph properties with the financial problems in the real-world. This tutorial is based on the potential of using Graph Neural Network based Learning for solving business problems in the financial world.

Of many things, Covid-19 has provided a stark proof that uncertainty is real, and it is here to stay. Perhaps nothing is more sensitive to uncertainty than the Financial World. To couple with it, while Artificial Intelligence techniques are used to predict the future state of events, their performance is significantly impacted by disruptions not captured in the past. Unforeseen scenarios such as economy changes, variations in the customer behaviour, pandemics, recessions, and fraudulent transactions often result in unexpected behaviour of financial models, thus associating a level of uncertainty with them. It is thus imperative for the research community to explore, identify, analyze, and address such uncertainties in order to develop robust models applicable in real-world scenarios. To this effect, the International Workshop on Modelling Uncertainty in the Financial World 2021 (MUFin21) aims to bring academics and industry experts together to discuss on this important, timely and yet- unsolved area of modelling uncertainties in the financial world.

Traditional reliability analysis of complex machinery involves statistical modeling of historical data on part failures from warranty claims, using distributions from exponential family such as the Weibull or log-normal distribution. When observed failures (in one or more parts) across a population of machines exceed the number expected based on such a model, this may serve as an early warning of a potential systemic problem with the population. Of course, such early warnings rely on some exceptionally high failures having actually occurred. However, modern connected vehicles, engines and machines of all kinds are equipped with on-board electronics that transmit alerts, referred to as `diagnostic trouble codes' or DTCs over the network, whenever abnormal conditions are detected. Such DTC signals should also be able to serve as early-warning indicators, typically before actual failures are observed in large numbers. In this paper, we develop a graphical Bayesian model that augments standard reliability analysis with early-warning indicators such as DTC signals observed over the industrial Internet. We demonstrate that our augmented model can detect of potential problems earlier than that using traditional reliability analysis. Going further, we note that significant deviations from expected failure counts might often occur only in some unknown subset of the population, e.g., a particular batch, or machines manufactured at a particular plant. In such cases, deviations from expected numbers are insignificant across the full population. We present a rule mining technique that discovers such subsets efficiently even when the number of dimensions across which a subset may be defined is large. We term our approach as reliability mining since it combines the use of a Bayesian reliability model with subgroup discovery using data mining techniques. We present experimental results using synthetically simulated scenarios as well as real-life data from a major global automobile manufacturer.