A comparative analysis of decision trees vis-a-vis other computational data mining techniques in automotive insurance fraud detection

The development and application of computational data mining techniques in financial fraud detection and business failure prediction has become a popular cross-disciplinary research area in recent times involving financial economists, forensic accountants and computational modellers. Some of the computational techniques popularly used in the context of - financial fraud detection and business failure prediction can also be effectively applied in the detection of fraudulent insurance claims and therefore, can be of immense practical value to the insurance industry. We provide a comparative analysis of prediction performance of a battery of data mining techniques using real-life automotive insurance fraud data. While the data we have used in our paper is US-based, the computational techniques we have tested can be adapted and generally applied to detect similar insurance frauds in other countries as well where an organized automotive insurance industry exists

Causal-Aware Generative Imputation for Automated Underwriting

Underwriting is an important process in insurance and is concerned with accepting individuals into insurance policy with tolerable claim risk. Underwriting is a tedious and labor intensive process relying on underwriters' domain knowledge and experience, thus is labor intensive and prone to error. Machine learning models are recently applied to automate the underwriting process and thus to ease the burden on the underwriters as well as improve underwriting accuracy. However, observational data used for underwriting modelling is high dimensional, sparse and incomplete, due to the dynamic evolving nature (e.g., upgrade) of business information systems. Simply applying traditional supervised learning methods e.g., logistic regression or Gradient boosting on such highly incomplete data usually leads to the unsatisfactory underwriting result, thus requiring practical data imputation for training quality improvement. In this paper, rather than choosing off-the-shelf solutions tackling the complex data missing problem, we propose an innovative Generative Adversarial Nets (GAN) framework that can capture the missing pattern from a causal perspective. Specifically, we design a structural causal model to learn the causal relations underlying the missing pattern of data. Then, we devise a Causality-aware Generative network (CaGen) using the learned causal relationship prior to generating missing values, and correct the imputed values via the adversarial learning. We also show that CaGen significantly improves the underwriting prediction in real-world insurance applications

Classification of capital expenditures and revenue expenditures: An analysis of correlation and neural networks

The classification between the capital expenditures and revenue expenditures is one of the common problems in the accounting literature since it has a significant impact on financial statements.This study aims to analyze the correlation of classification model such as Neural Networks (NN) in order to develop a model that can be trained to recognize hidden patterns of the borderline between the two expenditures types, viz: the capital and revenue expenditure.Twelve criterions were identified in order to classify between the two expenditures types and a Backpropagation Learning was utilized in this study.The highest classification accuracy obtained by NN is 94.20%. Correlation analysis reveals a significant correlation between some identified criterions with the model’s target.Strong correlation between target and criterion LASMFY (0.532) indicates that any expenditure lasts for more than a fiscal year will be more probable to be classified into a capital expenditure.Also, criterion RESALE proves its strong influence, with correlation of (-0.874) which implies more probability of classification into revenue expenditure if any expenditure was spent for intent for resale. Medium correlation shown by criterion REGULR (-0.251) indicates a moderate probability of classification into revenue expenditure if expenditure was spent in a regular basis

Artificial Intelligence and Bank Soundness: A Done Deal? - Part 1

Banks soundness plays a crucial role in determining economic prosperity. As such, banks are under intense scrutiny to make wise decisions that enhances bank stability. Artificial Intelligence (AI) plays a significant role in changing the way banks operate and service their customers. Banks are becoming more modern and relevant in people’s life as a result. The most significant contribution of AI is it provides a lifeline for bank’s survival. The chapter provides a taxonomy of bank soundness in the face of AI through the lens of CAMELS where C (Capital), A(Asset), M(Management), E(Earnings), L(Liquidity), S(Sensitivity). The taxonomy partitions opportunities from the main strand of CAMELS into distinct categories of 1 (C), 6(A), 17(M), 16 (E), 3(L), 6(S). It is highly evident that banks will soon extinct if they do not embed AI into their operations. As such, AI is a done deal for banks. Yet will AI contribute to bank soundness remains to be seen

Big Data and Artificial Intelligence in Digital Finance

This open access book presents how cutting-edge digital technologies like Big Data, Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTech, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also describes some more the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance

The impact of technology on data collection: Case studies in privacy and economics

Technological advancement can often act as a catalyst for scientific paradigm shifts. Today the ability to collect and process large amounts of data about individuals is arguably a paradigm-shift enabling technology in action. One manifestation of this technology within the sciences is the ability to study historically qualitative fields with a more granular quantitative lens than ever before. Despite the potential for this technology, wide-adoption is accompanied by some risks. In this thesis, I will present two case studies. The first, focuses on the impact of machine learning in a cheapest-wins motor insurance market by designing a competition-based data collection mechanism. Pricing models in the insurance industry are changing from statistical methods to machine learning. In this game, close to 2000 participants, acting as insurance companies, trained and submitted pricing models to compete for profit using real motor insurance policies --- with a roughly equal split between legacy and advanced models. With this trend towards machine learning in motion, preliminary analysis of the results suggest that future markets might realise cheaper prices for consumers. Additionally legacy models competing against modern algorithms, may experience a reduction in earning stability --- accelerating machine learning adoption. Overall, the results of this field experiment demonstrate the potential for digital competition-based studies of markets in the future. The second case studies the privacy risks of data collection technologies. Despite a large body of research in re-identification of anonymous data, the question remains: if a dataset was big enough, would records become anonymous by being "lost in the crowd"? Using 3 months of location data, we show that the risk of re-identification decreases slowly with dataset size. This risk is modelled and extrapolated to larger populations with 93% of people being uniquely identifiable using 4 points of auxiliary information among 60M people. These results show how the privacy of individuals is very unlikely to be preserved even in country-scale location datasets and that alternative paradigms of data sharing are still required.Open Acces

Potential Effects of Autonomous Vehicles on the Insurance Industry

The implementation of autonomous vehicles, or self-driving cars, promises to radically change much of the normal way of life. While it may seem inconsequential to start small with a vehicle of relatively low level of automation, there are many factors to consider. Some of these factors include security, moral dilemmas, and even the insurance field. One can look back at previous implementations of new technology, such as air bags, and see that it can be difficult to predict consequences and adapt. However, actuaries have been suggesting solutions to make autonomous vehicles a safe reality. While the solutions may vary, one thing is clear: communication between the vehicle and the insurance field is imperative