Improved adaptive genetic algorithm for the vehicle insurance fraud identification model based on a BP neural network

With the development of the insurance industry, insurance fraud is increasing rapidly. The existence of insurance fraud considerably hinders the development of the insurance industry. Fraud identification has become the most important part of insurance fraud research. In this paper, an improved adaptive genetic algorithm (NAGA) combined with a BP neural network (BP neural network) is proposed to optimize the initial weight of BP neural networks to overcome their shortcomings, such as ease of falling into local minima, slow convergence rates and sample dependence. Finally, the historical automobile insurance claim data of an insurance company are taken as a sample. The NAGA-BP neural network model was used for simulation and prediction. The empirical results show that the improved genetic algorithm is more advanced than the traditional genetic algorithm in terms of convergence speed and prediction accuracy

Predicting automobile insurance fraud using classical and machine learning models

Insurance fraud claims have become a major problem in the insurance industry. Several investigations have been carried out to eliminate negative impacts on the insurance industry as this immoral act has caused the loss of billions of dollars. In this paper, a comparative study was carried out to assess the performance of various classification models, namely logistic regression, neural network (NN), support vector machine (SVM), tree augmented naïve Bayes (NB), decision tree (DT), random forest (RF) and AdaBoost with different model settings for predicting automobile insurance fraud claims. Results reveal that the tree augmented NB outperformed other models based on several performance metrics with accuracy (79.35%), sensitivity (44.70%), misclassification rate (20.65%), area under curve (0.81) and Gini (0.62). In addition, the result shows that the AdaBoost algorithm can improve the classification performance of the decision tree. These findings are useful for insurance professionals to identify potential insurance fraud claim cases

Flexible operation of large-scale coal-fired power plant integrated with solvent-based post-combustion CO2 capture based on neural network inverse control

Post-combustion carbon capture (PCC) with chemical absorption has strong interactions with coal-fired power plant (CFPP). It is necessary to investigate dynamic characteristics of the integrated CFPP-PCC system to gain knowledge for flexible operation. It has been demonstrated that the integrated system exhibits large time inertial and this will incur additional challenge for controller design. Conventional PID controller cannot effectively control CFPP-PCC process. To overcome these barriers, this paper presents an improved neural network inverse control (NNIC) which can quickly operate the integrated system and handle with large time constant. Neural network (NN) is used to approximate inverse dynamic relationships of integrated CFPP-PCC system. The NN inverse model uses setpoints as model inputs and gets predictions of manipulated variables. The predicted manipulated variables are then introduced as feed-forward signals. In order to eliminate steady-state bias and to operate the integrated CFPP-PCC under different working conditions, improvements have been achieved with the addition of PID compensator. The improved NNIC is evaluated in a large-scale supercritical CFPP-PCC plant which is implemented in gCCS toolkit. Case studies are carried out considering variations in power setpoint and capture level setpoint. Simulation results reveal that proposed NNIC can track setpoints quickly and exhibit satisfactory control performances

Artificial Intelligence in Accounting and Finance: Meta-Analysis

Abstract   The use of the traditional system is declined greatly and with a modernization of the accounting and finance process there have been a great deal of change, and these improvements are beneficial to the accounting and finance industry. Adopting Artificial Intelligence applications such as Expert systems for audit and tax, Intelligent Agents for customer service, Machine Learning for decision making, etc. can lead a great benefit by reducing errors and increasing the efficiency of the accounting and finance processes. To keep ensuring a transparent and replicable process, we have conducted a meta-analysis. The database search was between the years 1989-2020 and reviewed 150 research papers. As meta-analysis results show, the majority of researches illustrate a positive effect of the impact of AI systems in the accounting and finance process

Artificial intelligence for fraud detection in motor insurance sector

Project Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceOne of the major problems in the insurance sector is related to fraud, aside from tax fraud, insurance fraud is the most practiced fraud in the world since insurance, by its nature is very susceptible to it. Fraud could be minimized by investigating each claim that occurs but that also means an increase of the costs for the insurance companies. The fraudulent clients or agents that will be caught with the investigation and the amount of money spent by looking into every new claim is not worth it. Insurance fraud is usually caught only when the fraudsters get greedy and it becomes obvious that they are involved in a scheme. To minimize the investigation costs by only looking at suspicious claims, this project tries to identify the ones that are worth to scrutinize, through machine learning techniques. Five different predictive models will be used: Logistic Regression, Decision Tree, Random Forest, Neural Network and Gradient Boosting. The goal is to build an optimal model that will determine which automobile claims have higher probability of being fraudulent. An efficient fraud management can reduce costs, minimize claims and increase profits. This goal was accomplished with a Gradient Boosting classifier with 400 estimators, that is able to predict correctly 49% of the fraudulent claims, with 75% less investigated claims. There is still room for improvement by introducing the expected claim and investigation costs in the model. Since only the ones with significant costs would be worth to open an investigation, an even greater decrease in the number of investigated claims would be possible and, consequently, a decrease in the company’s costs with claims. Also, it would be expected that the claims with higher costs are more likely fraudulent than the ones with small indemnities; hence, this variable could lead to a higher precision of the model. These two features will be available in the future

Aplicación de redes neuronales en operaciones industriales de manufactura para productos prismáticos circulares en materiales metálicos no ferrosos

Actualmente en el Ecuador, el mercado ha dado acogida al mecanizado de varios materiales metálicos, en especial los no ferrosos como las aleaciones de aluminio con tratamiento térmico de la familia 6xxx y 7xxx, para hacer productos primaticos circulares. La presente investigación tiene como objetivo el estudio de la integridad superficial y la tabulación del caudal de material removido aplicando aprendizaje no supervisado (reglas de asociación y agrupación) y supervisado (red neuronal artificial) en el proceso de manufactura en un torno CNC para mecanizar los ejes de aluminio. Se utilizo dos parámetros de corte constantes, como lo son la velocidad de corte de 420 m/min y volumen de material removido de 22.2 cm3. Previo al proceso de mecanizado se hicieron simulaciones utilizando software de manufactura y diseño asistido por computador para hacer un análisis comparativo con el tiempo real en el procesamiento de cada ensayo, determinando la correlación de los datos censados. En el desarrollo del aprendizaje no supervisado se estudió la correlación de los parámetros de corte y se diseñó el algoritmo de agrupamiento en función del valor máximo del análisis de Elbow, concluyendo que para estudiar los resultados de la aleación de aluminio AA 7075 T6, se necesita un arreglo ortogonal de veinte y siete niveles. Para el análisis neuronal se clasifico los resultados de la rugosidad superficial utilizando la escala Likert de cinco niveles (baja, regular, buena, muy buena y excelente) y en la estructura de la neurona se formuló en función de la profundidad, avance y velocidad de corte; presentando eficiencia del 75% en la arquitectura del avance de corte y la profundidad en la tabla de materiales AA 6061 T6 y AA 7075 T6 y eficiencia del 100% en la arquitectura del avance de corte y la profundidad en la tabla del material AA 6061 T6.Currently in Ecuador, the market has welcomed the machining of various metallic materials, especially non-ferrous ones such as aluminum alloys with heat treatment of the 6xxx and 7xxx families, to make circular primary products. The present research aims to study the surface integrity and the tabulation of the flow of removed material applying unsupervised learning (association and grouping rules) and supervised (artificial neural network) in the manufacturing process on a CNC lathe to machine the aluminum shafts. Two constant cutting parameters were used, such as the cutting speed of 420 m / min and the volume of material removed of 22.2 cm3. Before the machining process, simulations were made using computer-aided design and manufacturing software to make a comparative analysis with real time in the processing of each test, determining the correlation of the census data. In the development of unsupervised learning, the correlation of the cutting parameters was studied and the clustering algorithm was designed based on the maximum value of the Elbow analysis, concluding that to study the results of the aluminum alloy AA 7075 T6, it is necessary an orthogonal arrangement of twenty-seven levels. For the neuronal analysis, the results of the surface roughness were classified using the Likert scale of five levels (low, regular, good, very good and excellent) and in the structure of the neuron it was formulated according to the depth, advance and speed of court; presenting 75% efficiency in the architecture of the cutting feed and depth in the AA 6061 T6 and AA 7075 T6 material table and 100% efficiency in the architecture of the cutting feed and depth in the AA 6061 T6 material table