In this paper we develop an efficient approach based on a Machine Learning technique which allows one to quickly evaluate insurance products considering stochastic volatility and interest rate. Specifically, following De Spiegeleer et al., we apply Gaussian Process Regression to compute the price and the Greeks of a GMWB Variable Annuity. Starting from observed prices previously computed by means of a Hybrid Tree PDE approach for some known combinations of model parameters, it is possible to approximate the whole target function on a bounded domain. The regression algorithm consists of two main steps: algorithm training and evaluation. In particular, the first step is the most time demanding, but it needs to be performed only once, while the prediction step is very fast and requires to be performed only when evaluating the function. The developed method, as well as for the calculation of prices and Greeks, can also be employed to compute the no-arbitrage fee, which is a common practice in the Variable Annuities sector. We consider three increasing complexity models, namely the Black-Scholes, the Heston and the Heston Hull-White models, which extend the sources of randomness up to consider stochastic volatility and stochastic interest rate together. Numerical experiments show that the accuracy of the estimated values is high, while the computational cost is much lower than the one required by a direct calculation with standard approaches. Finally, we stress out that the analysis is carried out for a GMWB annuity but it could be generalized to other insurance products. Machine Learning seems to be a very promising and interesting tool for insurance risk management.

In this paper we show similarities between turbulence and financial systems. Motivated by similarities between the two systems, we construct a multiscale model for hierarchical financial structures that exhibits a constant cascade of wealth from large financial entities to small financial entities. According to our model, large and intermediate scale financial institutions have a power law distribution. However, the wealth distribution is Maxwellian at individual scales.

We propose a novel machine-learning-based approach to detect bid leakage in first-price sealed-bid auctions. We extract and analyze the data on more than 1.4 million Russian procurement auctions between 2014 and 2018. As bid leakage in each particular auction is tacit, the direct classification is impossible. Instead, we reduce the problem of bid leakage detection to Positive-Unlabeled Classification. The key idea is to regard the losing participants as fair and the winners as possibly corrupted. This allows us to estimate the prior probability of bid leakage in the sample, as well as the posterior probability of bid leakage for each specific auction. We find that at least 16\% of auctions are exposed to bid leakage. Bid leakage is more likely in auctions with a higher reserve price, lower number of bidders and lower price fall, and where the winning bid is received in the last hour before the deadline.

In this note we describe the application of existing smart contract technologies with the aim to construct a new digital representation of a financial derivative contract. We compare several existing DLT based technologies. We provide a detailed description of two separate prototypes which are able to be executed on a centralized and on a DLT platform respectively. Beyond that we highlight some insights on legal aspects as well as on common integration challenges regarding existing process and system landscapes. For a further introductory note and motivation on the theoretical concept we refer to [1]. A very detailed methodological overview of the concept of a smart derivative contract can be found in [3].