"... we develop a framework based on a combination of a neural network together with a dimensionality reduction technique t-SNE (t-distributed stochastic neighbour embedding)... The obtained results, which are based on real insurance data, reveal a clear contrast between the high and low risk policy holders, and indeed improve upon the actual risk estimation performed by the insurer."
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"... we have applied text analysis methodologies to extract information from descriptions in the OpRisk database. After delicate tasks like data cleaning, text vectorization, and semantic adjustment, we apply methods of dimensionality reduction and several clustering models and algorithms to develop a comparison of their performances and weaknesses. Our results improve retrospective knowledge of loss events and enable to mitigate future risks."
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"An employee may be attacked by a potentially sophisticated adversary whose goal is to steal all their data. Therefore, the firm trades off the efficiency benefit of the more permissive data access architecture with the adversarial risk it incurs. We characterize the firm's optimal data access architecture and investigate how it depends both on the adversarial environment and the firm's technology."