•This study quantifies the impact of ADAS on road safety across driving conditions.•The most severe accidents happen in dark conditions on rural roads or motorways.•ADAS could reduce accident ...frequency in the United Kingdom by 23.8%•The most frequent accident types in the UK can be reduced by 29% with a full deployment of ADAS.•Automatic Emergency Braking is the most impactful technology on road safety.
Advanced Driver Assistance Systems (ADAS) have introduced several benefits in the vehicular industry, and their proliferation presents potential opportunities to decrease road accidents. The reasons are mainly attributed to the enhanced perception of the driving environment and reduced human errors. However, as environmental and infrastructural conditions influence the performance of ADAS, the estimation of accident reductions varies across geographical regions. This study presents an interdisciplinary methodology that integrates the literature on advanced driving technologies and road safety to quantify the expected impact of ADAS on accident reduction across combinations of road types, lighting, and weather conditions. The paper investigates the safety effectiveness of ADAS and the distribution of frequency and severity of road accidents across 18 driving contexts and eight accident types. Using road safety reports from the United Kingdom (UK), it is found that a high concentration of accidents (77%) occurs within a small subset of contextual conditions (4 out of 18) and that the most severe accidents happen in dark conditions on rural roads or motorways. The results of the safety effectiveness analysis show that a full deployment of the six most common ADAS would reduce the road accident frequency in the UK by 23.8%, representing an annual decrease of 18,925 accidents. The results also show that the most frequent accident contexts, urban-clear-daylight and rural-clear-daylight, can be reduced by 29%, avoiding 7,020 and 3,472 accidents, respectively. Automatic Emergency Braking (AEB) is the most impactful technology, reducing three out of the four most frequent accident categories – intersection (by 28%), rear-end (by 27.7%), and pedestrian accidents (by 28.4%). This study helps prioritise resources in ADAS research and development focusing on the most relevant contexts to reduce the frequency and severity of road accidents. Furthermore, the identified contextual accident hotspots can assist road safety stakeholders in risk mitigation programs.
The enduring impact of the COVID-19 crisis on the financial sector is undeniable, persisting far beyond the eventual waning of the pandemic. This research examines central bank interventions during ...the pandemic, using a quantitative event study approach over a five-day window to analyse the impact of 188 monetary policy announcements on banking stocks in China, the U.S., and Europe. Our results demonstrate how monetary policy announcements targeting different economic mechanisms have produced a diverse market reaction throughout the COVID-19 pandemic. Namely, cuts in interest rates and the maintenance of a low interest rate environment by the Federal Reserve resulted in negative abnormal returns in the U.S.A., while short-term announcements surrounding intra-day credit and liquidity provisions boosted banking sector stock prices. In Europe, a muted reaction by the banking sector was observed, with negative abnormal returns observed in response to the ECB’s 2% inflation objectives. Finally, banking stocks in China responded strongly and positively to foreign currency and exchange-related announcements by the People’s Bank of China. The results and insights from this analysis can thus inform preparations made by policymakers, governments, and financial market stakeholders in the event of future waves of COVID-19, or further extreme societal disruptions.
A telematics device is a vehicle instrument that comes preinstalled by the vehicle manufacturer or can be added later. The device records information about driving behavior, including speed, ...acceleration, and turning force. When connected to vehicle computers, the device can also provide additional information regarding the mechanical usage and condition of the vehicle. All of this information can be transmitted to a central database via mobile networks. The information provided has led to new services such as Usage Based Insurance (UBI). A range of consultants, industry commentators and academics have produced an abundance of projections on how telematics information will allow the introduction of services from personalized insurance, bespoke entertainment and advertise and vehicle energy optimization, particularly for Electric Vehicles (EVs). In this paper we examine these potential services against a backdrop of nascent regulatory limitations and against the technical capacity of the devices. Using a case study approach, we examine three applications that can use telematics information. We find that the expectations of service providers will be significantly tempered by regulatory and technical hurdles. In our discussion we detail these limitations and suggest a more realistic rollout of ancillary services.
Explainable Artificial Intelligence (XAI) models allow for a more transparent and understandable relationship between humans and machines. The insurance industry represents a fundamental opportunity ...to demonstrate the potential of XAI, with the industry’s vast stores of sensitive data on policyholders and centrality in societal progress and innovation. This paper analyses current Artificial Intelligence (AI) applications in insurance industry practices and insurance research to assess their degree of explainability. Using search terms representative of (X)AI applications in insurance, 419 original research articles were screened from IEEE Xplore, ACM Digital Library, Scopus, Web of Science and Business Source Complete and EconLit. The resulting 103 articles (between the years 2000–2021) representing the current state-of-the-art of XAI in insurance literature are analysed and classified, highlighting the prevalence of XAI methods at the various stages of the insurance value chain. The study finds that XAI methods are particularly prevalent in claims management, underwriting and actuarial pricing practices. Simplification methods, called knowledge distillation and rule extraction, are identified as the primary XAI technique used within the insurance value chain. This is important as the combination of large models to create a smaller, more manageable model with distinct association rules aids in building XAI models which are regularly understandable. XAI is an important evolution of AI to ensure trust, transparency and moral values are embedded within the system’s ecosystem. The assessment of these XAI foci in the context of the insurance industry proves a worthwhile exploration into the unique advantages of XAI, highlighting to industry professionals, regulators and XAI developers where particular focus should be directed in the further development of XAI. This is the first study to analyse XAI’s current applications within the insurance industry, while simultaneously contributing to the interdisciplinary understanding of applied XAI. Advancing the literature on adequate XAI definitions, the authors propose an adapted definition of XAI informed by the systematic review of XAI literature in insurance.
Hazard identification is the key step in risk assessment and management of manufactured nanomaterials (NM). However, the rapid commercialisation of nano-enabled products continues to out-pace the ...development of a prudent risk management mechanism that is widely accepted by the scientific community and enforced by regulators. However, a growing body of academic literature is developing promising quantitative methods. Two approaches have gained significant currency. Bayesian networks (BN) are a probabilistic, machine learning approach while the weight of evidence (WoE) statistical framework is based on expert elicitation. This comparative study investigates the efficacy of quantitative WoE and Bayesian methodologies in ranking the potential hazard of metal and metal-oxide NMs-TiO₂, Ag, and ZnO. This research finds that hazard ranking is consistent for both risk assessment approaches. The BN and WoE models both utilize physico-chemical, toxicological, and study type data to infer the hazard potential. The BN exhibits more stability when the models are perturbed with new data. The BN has the significant advantage of self-learning with new data; however, this assumes all input data is equally valid. This research finds that a combination of WoE that would rank input data along with the BN is the optimal hazard assessment framework.
From February 2020, both urban and rural Ireland witnessed the rapid proliferation of the COVID-19 disease throughout its counties. During this period, the national COVID-19 responses included ...stay-at-home directives issued by the state, subject to varying levels of enforcement.
In this paper, we present a new method to assess and rank the causes of Ireland COVID-19 deaths as it relates to mobility activities within each county provided by Google while taking into consideration the epidemiological confirmed positive cases reported per county. We used a network structure and rank propagation modelling approach using Personalised PageRank to reveal the importance of each mobility category linked to cases and deaths. Then a novel feature-selection method using relative prominent factors finds important features related to each county's death. Finally, we clustered the counties based on features selected with the network results using a customised network clustering algorithm for the research problem.
Our analysis reveals that the most important mobility trend categories that exhibit the strongest association to COVID-19 cases and deaths include retail and recreation and workplaces. This is the first time a network structure and rank propagation modelling approach has been used to link COVID-19 data to mobility patterns. The infection determinants landscape illustrated by the network results aligns soundly with county socio-economic and demographic features. The novel feature selection and clustering method presented clusters useful to policymakers, managers of the health sector, politicians and even sociologists. Finally, each county has a different impact on the national total.
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•A novel network is presented to model the relationships between causes, cases, and deaths of COVID-19.•Personalised PageRank Propagation is used to rank COVID-19 national deaths determinant.•A novel clustering method extends the support for identifying the causes and their impacts in different counties.•Personalised PageRank network coupled with clustering shapes a complete picture of the importance of causes.•Top mobility-based determinants of Ireland COVID-19 death count are workplace and retail and recreation trend change.
The proliferation of technologies embedded in connected and autonomous vehicles (CAVs) increases the potential of cyber-attacks. The communication systems between vehicles and infrastructure present ...remote attack access for malicious hackers to exploit system vulnerabilities. Increased connectivity combined with autonomous driving functions pose a considerable threat to the vast socioeconomic benefits promised by CAVs. However, the absence of historical information on cyber-attacks mean that traditional risk assessment methods are rendered ineffective. This paper proposes a proactive CAV cyber-risk classification model which overcomes this issue by incorporating known software vulnerabilities contained within the US National Vulnerability Database into model building and testing phases. This method uses a Bayesian Network (BN) model, premised on the variables and causal relationships derived from the Common Vulnerability Scoring Scheme (CVSS), to represent the probabilistic structure and parameterisation of CAV cyber-risk. The resulting BN model is validated with an out-of-sample test demonstrating nearly 100% prediction accuracy of the quantitative risk score and qualitative risk level. The model is then applied to the use-case of GPS systems of a CAV with and without cryptographic authentication. In the use case, we demonstrate how the model can be used to predict the effect of risk reduction measures.
•TabNet provides high model performance for insurance risk pricing.•When compared to XGBoost & GLM, TabNet provides better or comparable performance.•Unlike other Deep Learning models, TabNet is ...highly interpretable.
Generalized Linear Models (GLMs) and XGBoost are widely used in insurance risk pricing and claims prediction, with GLMs dominant in the insurance industry. The increasing prevalence of connected car data usage in insurance requires highly accurate and interpretable models. Deep learning (DL) models have outperformed traditional Machine Learning (ML) models in multiple domains; despite this, they are underutilized in insurance risk pricing. This study introduces an alternative DL architecture, TabNet, suitable for insurance telematics datasets and claim prediction. This approach compares the TabNet DL model against XGBoost and Logistic Regression on the task of claim prediction on a synthetic telematics dataset. TabNet outperformed these models, providing highly interpretable results and capturing the sparsity of the claims data with high accuracy. However, TabNet requires considerable running time and effort in hyperparameter tuning to achieve these results. Despite these limitations, TabNet provides better pricing models for interpretable models in insurance when compared to XGBoost and Logistic Regression models.
•Electric and hybrid drivers exhibit different behaviours than traditional vehicles.•Electric vehicles record higher at-fault claims than traditional vehicles.•Hybrids do not display a statistically ...significant increase in claim likelihood.•Electric vehicles are 6.7% more expensive to repair than traditional vehicles.
Electric vehicles (EVs) differ significantly from their internal combustion engine (ICE) counterparts, with reduced mechanical parts, Lithium-ion batteries and differences in pedal and transmission control. These differences in vehicle operation, coupled with the proliferation of EVs on our roads, warrant an in-depth investigation into the divergent risk profiles and driving behaviour of EVs, Hybrids (HYB) and ICEs. In this unique study, we analyze a novel telematics dataset of 14,642 vehicles in the Netherlands accompanied by accident claims data. We train a Logistic Regression model to predict the occurrence of driver at-fault claims, where an at-fault claim refers to First and Third Party damages where the driver was at fault. Our results reveal that EV drivers are more exposed to incurring at-fault claims than ICE drivers despite their lower average mileage. Additionally, we investigate the financial implications of these increased at-fault claims likelihoods and have found that EVs experience a 6.7% increase in significant first-party damage costs compared to ICE. When analyzing driver behaviour, we found that EVs and HYBs record fewer harsh acceleration, braking, cornering and speeding events than ICE. However, these reduced harsh events do not translate to reducing claims frequency for EVs. This research finds evidence of a higher frequency of accidents caused by Electric Vehicles. This burden should be considered explicitly by regulators, manufacturers, businesses and the general public when evaluating the cost of transitioning to alternative fuel vehicles.
•This study analyses the link between the driving context (where people drive) and risk.•The impacts of the contextual factors on the predictions are ranked and interpreted using SHAP.•It is found ...that the driving context has significant power in predicting driving risk.•High-speed roads in warm temperatures increase the likelihood of harsh acceleration and braking.•Speed limit, temperature, traffic conditions, and road slope are the leading contexts for most risk events.
Usage-based insurance has allowed insurers to dynamically tailor insurance premiums by understanding when and how safe policyholders drive. However, telematics information can also be used to understand the driving contexts experienced by the driver within each trip (e.g., road types, weather, traffic). Since different combinations of these conditions affect exposure to accidents, this understanding introduces predictive opportunities in driving risk assessment. This paper investigates the relationships between driving context combinations and risk using a naturalistic driving dataset of 77,859 km. In particular, XGBoost and Random Forests are used to determine the predictive significance of driving contexts for near-misses, speeding and distraction events. Moreover, the most important contextual factors in predicting these risky events are identified and ranked through Shapley Additive Explanations. The results show that the driving context has significant power in predicting driving risk. Speed limit, weather temperature, wind speed, traffic conditions and road slope appear in the top ten most relevant features for most risky events. Analysing contextual feature variations and their influence on risky events showed that low-speed limits increase the predicted frequency of speeding and phone unlocking events, whereas high-speed limits decrease harsh accelerations. Low temperatures decrease the expected frequency of harsh manoeuvres, and precipitations increase harsh acceleration, harsh braking, and distraction events. Furthermore, road slope, intersections and pavement quality are the most critical factors among road layout attributes. The methodology presented in this study aims to support road safety stakeholders and insurers by providing insights to study the contextual risk factors that influence road accident frequency and driving risk.
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