This special issue on computational and algorithmic finance showcases contemporary developments ranging from advanced numerical methods to machine learning techniques and efficient parallel implementations in finance and insurance. This, in particular, includes: calibration of various asset pricing models (local volatility, stochastic volatility, jumps) to market data; development of new approaches in constructing efficient finite difference and radial basis function methods; study of models and machine learning techniques, like Bayesian and neural networks, for asset liability management and limit order books; analysis of bond quote inconsistencies; and also implementation issues on GPU of a Monte Carlo insurance balance sheet projection. (C) 2017 Published by Elsevier B.V.