In these days of machine learning solutions for business optimisation, one key question is whether machines can usefully pick out emerging liability risks. The Robot Toxicologist The Robot Toxicologist The “Toxic Trio” as a case study Wouldn’t it be wonderful if a machine could read all of the world’s science literature, decide which substance would trigger new liability exposures, say how much this would cost and who should pay? After > 10 years of development work, the recent marketing document[1] from Allianz illustrates how far along this path one particular robot has travelled. UK liability insurers read the Allianz report and asked –‘is it better than tossing a coin’? 51% is seen as the minimum requirement for authorising reserves for example. The task was to compare the fifteen substantial findings in the report (in the context of nail varnish) with the written views of expert toxicology committees produced over several decades. Is this a fair test? One of the key features of

Vibration White Finger A modelling case study An epidemiology-based approach to liability ENID modelling has been developed and applied[1]. While based on the same concepts, in practice each scenario-specific ENID model is mathematically unique. This note describes the approach using the example of vibration white finger (VWF[2]). The results agree, within tolerance, with official data.   Brief background Long term exposure to high intensity vibrations leads to a predisposition to episodes of finger blanching. In severe cases there is loss of dexterity. Cause and severity of VWF are both cumulative in nature. A typical presentation is illustrated below: The cause of these symptoms is an autonomic[3] constriction of the blood vessels supplying parts of the hands[4]. Episodes of finger blanching may be provoked by vibration, cold weather and wetting with water. A similar effect is seen in Raynaud’s’ phenomenon (RP) which is of constitutional origin. This initially gave rise to uncer