False Insurance Claims Research Articles

Insurance fraud detection: Pre-emptive analysis and prevention

Insurance companies have been facing the problem of false insurance claims, costing resources and money in detecting fraudulent claims among the many true ones. Many times, fraudulent claims are mistaken for genuine claims, and genuine ones mistaken for fraudulent claims. This paper aims to develop a Machine Learning algorithm to detect fraudulent claims and distinguish them with genuine claims. This project also aims to compare other algorithms and see which one is the most effective to deal with this growing problem that the insurance industry is facing.

Evaluation of Deep Learning for Automatic Multi-View Face Detection in Cattle

Individual identification plays an important part in disease prevention and control, traceability of meat products, and improvement of agricultural false insurance claims. Automatic and accurate detection of cattle face is prior to individual identification and facial expression recognition based on image analysis technology. This paper evaluated the possibility of the cutting-edge object detection algorithm, RetinaNet, performing multi-view cattle face detection in housing farms with fluctuating illumination, overlapping, and occlusion. Seven different pretrained CNN models (ResNet 50, ResNet 101, ResNet 152, VGG 16, VGG 19, Densenet 121 and Densenet 169) were fine-tuned by transfer learning and re-trained on the dataset in the paper. Experimental results showed that RetinaNet incorporating the ResNet 50 was superior in accuracy and speed through performance evaluation, which yielded an average precision score of 99.8% and an average processing time of 0.0438 s per image. Compared with the typical competing algorithms, the proposed method was preferable for cattle face detection, especially in particularly challenging scenarios. This research work demonstrated the potential of artificial intelligence towards the incorporation of computer vision systems for individual identification and other animal welfare improvements.

The certainty–offending relationship as a function of a nation's free market cultural ethos

The current study sought to integrate aspects of classic strain and institutional anomie theories with concepts from deterrence and rational choice theories for application in a large sample of European respondents. Participants were 52,458 individuals (55% female, average age = 48 years) from the fifth round of the 27-country European Social Survey. Each participant rated their involvement in three relatively minor offenses (false insurance claims, buying stolen property, and traffic offenses) over the past five years along with their perceived certainty of getting caught and punished should they commit one or more of these prohibited acts. Each country's total 2010 Index of Economic Freedom score was also included in the study as a level 2 variable in a two-level multilevel modeling analysis. Consistent with predictions, participants from countries with higher Index of Economic Freedom scores displayed a significantly stronger connection between certainty of punishment and involvement in minor offending than participants from lower Index of Economic Freedom countries. An additional individual-level variable, the marketized mentality, was also included in the analysis and while it correlated with minor offending, it failed to interact with the Index of Economic Freedom or alter free market cultural ethos moderation of the certainty-offending relationship. These findings suggest that individuals living in a country with a strong free market cultural ethos are more apt to incorporate rational choice principles like certainty into their crime-related decisions than individuals residing in a country with a weaker free market cultural ethos.

Dog Nose-Print Identification Using Deep Neural Networks

Recently, there has been rapid growth in the number of people who own companion pets (cats and dogs) due to low birth rates, an increasingly aging population, and an increasing number of single-person households. This trend has resulted in a growing interest in problems requiring solutions, such as missing pets and false insurance claims. Traditional non-biometric-based methods cannot address these problems. This paper proposes a novel deep-learning model that can extract discriminative features through dog nose-print patterns for individual identification. We present a robust baseline for how individual dogs can be identified. The proposed dog nose network (DNNet) is a convolutional neural network (CNN)-based Siamese network structure comprising feature extraction and self-attention modules. Moreover, there is no need for a separate scanning device because it uses popular mobile devices to acquire the dataset. Besides high recognition performance, the proposed method also ensures simplicity and efficiency. The proposed method achieves better recognition performance than state-of-the-art methods for the collected dog nose-print dataset. It achieves recognition performance superior to state-of-the-art methods for the collected dog nose-print dataset. Using multiple datasets through cross-validation, we acquired an average identification accuracy of 98.972% with the Rank-1 approach. Additional performance benefits were demonstrated through the receiver operating characteristic (ROC) curve, t-distributed stochastic neighbor embedding (t-SNE), and confusion matrix.

Cattle identification: the history of nose prints approach in brief

Petersen was the first published paper to address cattle biometrics and identification problem by suggesting a permanent cattle identification method based on nose print principles widely accepted today. His major concern was on proper identification of cattle for registration and of cattle on an official test so that the possibility of swapping, false insurance claims, and ownership disputes can be guarded against. It was with this identification problem in the mind of every breeder that the practicable suggestion of using nose print as means of identification was made by O. H. Baker of the American Jersey Cattle Club in Petersen’s paper entitled “The identification of the bovine by means of nose-prints”. Before the advent of the nose print method, cattle identification has been by conventional constructs such as tattoo, tags, photographs, descriptions, branding (hot and freeze), ear notching, and sketching (drawings) the color markings on them on paper for registration and identification purposes. These classical methods of identification cause trouble among the breeders especially when their cattle are sold or are on an official test due to lack of artistic ability on the part of the breeders which makes the matching of the sketches and the markings on the cattle disagree. Presented in this paper are the various cattle biometrics and identification methods, most especially from the classical methods to the modern methods.

Neophodnost bliže međunarodne saradnje različitih ustanova u borbi protiv prevara u osiguranju

In this paper, the author analyses a few examples of fraud in life and accident insurance. The cases that are the subject of this Article can be classified as "heavy frauds" according to the seriousness of the crime committed and as "external" according to the source type. The course and epilogue of the trials show how important it is to have the closest possible coordination between the judicial, police and insurance institutions and that the character of insurance frauds is becoming increasingly international, requiring stronger cross-border inter-institutional cooperation. Also, experts from a growing number of disciplines (from medicine, through mechanical engineering to technological sciences) do have a particularly notable role in discovering false insurance claims. Their expertise findings can make a decisive contribution to clarifying the circumstances of each and every case. The examples presented in this Article illustrate that closer cooperation between all the named participants and the use of advanced methods and technologies are the indispensable tool of a good and a more efficient fight against the insurance fraud.

Subjective Probabilities of Sanction, Norms, Legitimacy and Everyday Life Crimes in Europe

Les crimes de la vie quotidienne sont généralement définis comme des pratiques injustes et contraires à l’éthique commises sur le marché par des personnes qui se considèrent elles-mêmes et qui sont perçues par les autres comme des citoyens respectables. Cet article étudie les principaux moteurs de ce comportement criminel spécifique sur la base d’une analyse économétrique transnationale des données de l’Enquête sociale européenne 2010. Il se concentre en particulier sur la probabilité subjective de sanction, l’internalisation des normes et la légitimité perçue du système judiciaire. Trois crimes du quotidien sont examinés : les fausses demandes de réclamations d’assurance, l’achat de biens potentiellement volés et les infractions routières. En contrôlant les variables socio-démographiques, le capital social et les effets fixes des pays, nous constatons que l’internalisation des normes est associée négativement à la criminalité quotidienne, alors que la légitimité perçue des institutions judiciaires a un effet non significatif. Les résultats sont ambigus pour la probabilité subjective des sanctions. Enfin, notre travail souligne la difficulté d’utiliser les données d’enquête dans l’analyse de la criminalité. Codes JEL: D8, K14, K42

Deep learning framework for recognition of cattle using muzzle point image pattern

Animal biometrics is a frontier area of computer vision, pattern recognition and cognitive science to plays the vital role for the registration, unique identification, and verification of livestock (cattle). The existing handcrafted texture feature extraction and appearance based feature representation techniques are unable to perform the animal recognition in the unconstrained environment. Recently deep learning approaches have achieved more attention for recognition of species or individual animal using visual features. In this research, we propose the deep learning based approach for identification of individual cattle based on their primary muzzle point (nose pattern) image pattern characteristics to addressing the problem of missed or swapped animals and false insurance claims. The major contributions of the work as follows: (1) preparation of muzzle point image database, which are not publically available, (2) extraction of the salient set of texture features and representation of muzzle point image of cattle using the deep learning based convolution neural network, deep belief neural network proposed approaches. The stacked denoising auto-encoder technique is applied to encode the extracted feature of muzzle point images and (3) experimental results and analysis of proposed approach. Extensive experimental results illustrate that the proposed deep learning approach outperforms state-of-the-art methods for recognition of cattle on muzzle point image database. The efficacy of the proposed deep learning approach is computed under different identification settings. With multiple test galleries, rank-1 identification accuracy of 98.99% is achieved.

Biometric Recognition for Pet Animal

Missing, swapping, false insurance claims and reallocation of pet animals (dog) are global problems throughout the world and research done to solve this problem is minimal. Traditional biometrics and non-biometrics methods have their own boundaries and they fail to provide competent level of security to pet animal (dog). The work on animal identification based on their phenotype appearance (coat patterns) has been an active research area in recent years and automatic face recognition for dog is not reported in the literature. Dog identification needs innovative research to protect the pet animal. Therefore it is imperative to initiate research, so that future face recognition algorithm will be able to solve this important problem for identification of pet animal (like dog, cat). In this paper an attempt has been made to minimize the above mentioned problems by biometrics face recognition of dog. The contributions of this research are: 1) implementation of an existing biometrics algorithm which mitigates the effects of covariates for dogs; 2) proposed fusion based method for recognition of pet animal with 94.86% accuracy. Thus in this paper, we have tried to demonstrate that face recognition of dog can be used to recognize the dog efficiently.

The fog of fraud – Mitigating fraud by strategic ambiguity

Most insurance companies publish few data on the occurrence and detection of insurance fraud. This stands in contrast to the previous literature on costly state verification, which has shown that it is optimal to commit to an auditing strategy. The credible announcement of thoroughly auditing claim reports is a powerful deterrent. Yet, we show that uncertainty about fraud detection can be an effective strategy to deter ambiguity-averse agents from reporting false insurance claims. If, in addition, the auditing costs of the insurers are heterogeneous, it can be optimal not to commit, because committing to a fraud-detection strategy eliminates the ambiguity about auditing. Thus, strategic ambiguity can be an equilibrium outcome in the market. Even competition does not force firms to provide the relevant information. This finding is also relevant in other auditing settings, like tax enforcement.

The Fog of Fraud – Mitigating Fraud by Strategic Ambiguity

Most insurance companies publish few data on the occurrence and detection of insurance fraud. This stands in contrast to the previous literature on costly state verification, which has shown that it is optimal to commit to an auditing strategy, as the credible announcement of thoroughly auditing claim reports might act as a powerful deterrent. We show that uncertainty about fraud detection can be an effective strategy to deter ambiguity-averse agents from reporting false insurance claims. If, in addition, the auditing costs of the insurers are heterogeneous, it can be optimal not to commit, because committing to a fraud-detection strategy eliminates the ambiguity. Thus, strategic ambiguity can be an equilibrium outcome in the market and competition does not force firms to provide the relevant information. This finding is also relevant in other auditing settings, like tax enforcement.

Consumer morality in times of economic hardship: evidence from the European Social Survey

AbstractCrimes of everyday life, often referred to as unfair or unethical practices committed in the marketplace by those who see themselves and are seen as respectable citizens, have burgeoned as a result of the transformations in the European economy in the late 20th century, namely the transition to neo‐liberal markets and the emergence of consumer society. A ‘cornucopia of new criminal opportunities’ has given rise to a new range of crimes such as ripping software, making false insurance claims or paying cash on hand to circumvent taxes. These shady behaviours (legal or not) are part of people's experience, albeit they are collectively regarded as morally dubious. Taken collectively, crimes of everyday life are indicators of the moral stage of a particular society and therefore a valuable instrument for social and political analysis. This paper addresses the question of whether and under which conditions feelings of economic hardship trigger crimes of everyday life. A multilevel theoretical and empirical perspective that integrates theories stemming from political science, sociology, and social psychology is adopted. I start by exploring the embeddedness of economic morality in social institutions, followed by an elaboration of the concept of market anomie to account for deviant behaviour in the marketplace, to finally step down to the examination of the correspondence between social attitudes and consumer behaviour, as postulated by the Theory of Planned Behaviour. The empirical study relies on micro data from the European Social Survey (ESS) (Round 2) and attempts to model, for each country, a formative measure of crimes of everyday life based on socio‐demographic variables and the current economic situation, as it is perceived by the individual (taken as a measure of relative deprivation). The resultant country‐specific regression coefficients are mapped onto the broader economic and normative context of 23 European countries. The results reveal that crimes of everyday life are driven by feelings of economic hardship only in countries where normative factors dictate their deviance. In countries where fraudulent behaviour is more generalized, inner motivations to offend play a secondary role as the more privileged consumers are more likely to commit fraud as they interact more often with the market. In turn, normative aspects result from a dynamic interplay of cultural and economic factors. As the economy grows faster, the tendency to offend in the market becomes more visible, but only in countries whose gross domestic product (GDP) stands above the European average. In countries with low GDP, the normative landscape is shaped by cultural factors that seem to obfuscate the power of economic factors favourable to consumer fraud.

Treating incompetent defendants: the Supreme Court's decision is a tough Sell.

Treating incompetent defendants: the Supreme Court's decision is a tough Sell.