Auto Frauds.pptx
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Insurance fraud is done by providing false details to gain the extra benefit. Insurance fraud is the most commonly practiced fraud in the Nation, according to the Society of Professional Fraud Examiners.
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Insurance today is considered both as a form of security and investment. It gives a sense of assurance to its client- the courage to mitigate unforeseen mayhem in life. But with the influx of fraudulent activities and felony across various industries, the insurance sector stands to be no exception. One of the ways that miscreants try to get money from insurance companies is through Insurance Claims Fraud
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Billions of dollars of loss are caused every year by fraudulent credit card transactions. The design of efficient fraud detection algorithms is key for reducing these losses, and more and more algorithms rely on advanced machine learning techniques to assist fraud investigators. The design of fraud detection algorithms is however particularly challenging due to the non-stationary distribution of the data, the highly unbalanced classes distributions and the availability of few transactions labeled by fraud investigators. At the same time public data are scarcely available for confidentiality issues, leaving unanswered many questions about what is the best strategy. In this thesis we aim to provide some answers by focusing on crucial issues such as: i) why and how under sampling is useful in the presence of class imbalance (i.e. frauds are a small percentage of the transactions), ii) how to deal with unbalanced and evolving data streams (non-stationarity due to fraud evolution and change of spending behavior), iii) how to assess performances in a way which is relevant for detection and iv) how to use feedbacks provided by investigators on the fraud alerts generated. Finally, we design and assess a prototype of a Fraud Detection System able to meet real-world working conditions and that is able to integrate investigators’ feedback to generate accurate alerts.
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Cybercrime is a global and growing phenomenon tied to a combination of factors that includes the new era of global economic interdependency along with rapidly changing technology, according to a new research report from Mercator Advisory Group. These factors create opportunities for criminals to find new and often faster ways to defraud businesses of all sizes. Data breaches will inevitably lead to follow-on activities that take advantage of people, holes in processes, and cracks in systems to transfer wealth from legitimate sources to fraudsters across the globe. A range of activities can be undertaken by companies to create better protective cover around organizational data and prevent or limit the damage from payments fraud.
A rule-based machine learning model for financial fraud detection
Financial fraud is a growing problem that poses a significant threat to the banking industry, the government sector, and the public. In response, financial institutions must continuously improve their fraud detection systems. Although preventative and security precautions are implemented to reduce financial fraud, criminals are constantly adapting and devising new ways to evade fraud prevention systems. The classification of transactions as legitimate or fraudulent poses a significant challenge for existing classification models due to highly imbalanced datasets. This research aims to develop rules to detect fraud transactions that do not involve any resampling technique. The effectiveness of the rule-based model (RBM) is assessed using a variety of metrics such as accuracy, specificity, precision, recall, confusion matrix, Matthew’s correlation coefficient (MCC), and receiver operating characteristic (ROC) values. The proposed rule-based model is compared to several existing machine learning models such as random forest (RF), decision tree (DT), multi-layer perceptron (MLP), k-nearest neighbor (KNN), naive Bayes (NB), and logistic regression (LR) using two benchmark datasets. The results of the experiment show that the proposed rule-based model beat the other methods, reaching accuracy and precision of 0.99 and 0.99, respectively.
RSB72-PPT.pptx
1) The document discusses the application of artificial intelligence in finance fraud detection. It outlines key points such as different AI applications in finance, the impact of AI, and methodology.
2) AI systems use machine learning algorithms to analyze financial data and identify patterns that indicate fraudulent activity in real-time. This helps reduce fraud and financial losses.
3) The future of AI in finance fraud detection is promising, with potential applications including advanced machine learning, natural language processing, biometric authentication, and more automated risk management and investment processes.
Machine Leaning Insurance
Data has always played a central role in the insurance industry, and today, insurance carriers have access to more of it than ever before. We have created more data in the past two years than the human race has ever created. Insurers—like organisations in most industries—are overwhelmed by the explosion in data from a host of sources, including telematics, online and social media activity, voice analytics, connected sensors and wearable devices. They need machines to process this information and unearth analytical insights. But most insurers are struggling to maximize the benefits of machine learning.
Widening Technology Gap
This document discusses the challenges insurance companies face in keeping up with technological advances. It notes that only 15% of insurance businesses consider themselves technologically progressive, and that outdated systems and a generational gap are hindering modernization efforts. However, improving efficiency, customer experience, fraud detection, and mobile technologies could help companies better serve customers and gain competitive advantages if they are willing to invest in new technologies like smart machines and the Internet of Things.
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