Database

Tech Plus Data mining and machine learning look set to revolutionise knowledge management in routine business processes[1]. Can they be used to assist with the identification and evaluation of new liability risks? The Robotic Toxicologist report presents an expert-based analysis of a liability-related work recently published by Allianz[2] et al. The Allianz et al. report demonstrated some very appealing underlying capabilities. However, it is clear there are areas where targeted expert assistance could lead to improvements in toxicological insight, relevance and liability meaning-making. Insurers wishing to develop or acquire robotic tools could include such targeted expertise in their development and evaluation projects and if they are adopted, in the management of machine outputs. The Robotic Toxicologist Report. Could an automated search machine usefully identify injury outcomes associated with three of the chemicals in nail varnish? There were 15 overt “predictions” in the Allianz

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 “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 any advisory tool is t

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

Processed meat and colorectal cancer claims – Yes, No, How Big? Summary It has been proposed that processed meat consumption causes colorectal cancer. Since there have been no UK insurance claims for this, and the proposal has not been put to proof at common law, it is described as a potential liability ENID[1], or, emerging liability risk. The background is briefly described below. As part of the necessary[2] evaluation response, a software model of the potential UK personal injury liability exposure has been developed. The model allows the user to test various technical and legal “what ifs” and to perform sensitivity analyses. A generic causation assessment tool is included in the software and is based on UK legal precedents. The normal medical response to colorectal cancer includes surgery. Such surgery entails a risk of serious infection and the consequential need for antibiotics. Given the increased rates of antimicrobial resistance (AMR) some bowel surgery cases will die as a res

De minimis – a practical step In a nut shell I propose that the explicit recognition of a two stage test of de minimis may lead to clarity of fact finding in difficult cases such as marginal noise induced hearing loss, and minor neck sprain. Further, by focusing on the first stage test, the significance of marginal exposures to risk can be resolved objectively. The conventional two-stage test The concept of de minimis is fairly straightforward to assess in cases of broken limbs, burns and other ‘impact’ events. Indeed it is so straightforward that the assessor and opposing parties may not make explicit that they are using a two stage test. In general when assessing injury: The first stage is to assess whether the state of the claimant after the event was/is ‘probably different’ to their state immediately prior to the event. Has anything changed? ‘Probably different’, is properly assessed on the balance of probabilities. If yes, the second stage is to assess whether the difference is su

Artificial intelligence and liability insurance. After a brief introduction to AI, some of the liability issues are introduced in this brief report. AI simulations Essentially AI systems are structured in the following way. The computer is supplied with digital information, coded with a set of statistical tools and a set of purposes that the customer would like to work with. Once data, methods and purposes are properly coded, the AI generates useful simulations of the data and can be used to identify the degree to which new situations meet the set of purposes. Missing data is effectively imputed from the overall model. As a very much simplified example. An example helps set the scene for the discussion of legal liability. Intended purpose: A liability claims manager wants to identify claims which are suited to making an immediate offer, and those which are best examined more closely. Process: Having coded ten thousand claims of the relevant sort, AI generates a statistically weighted s