Credit Card Fraud Detection Using Asexual Reproduction Optimization

As the number of credit card users has increased, detecting fraud in this domain has become a vital issue. Previous literature has applied various supervised and unsupervised machine learning methods to find an effective fraud detection system. However, some of these methods require an enormous amount of time to achieve reasonable accuracy. In this paper, an Asexual Reproduction Optimization (ARO) approach was employed, which is a supervised method to detect credit card fraud. ARO refers to a kind of production in which one parent produces some offspring. By applying this method and sampling just from the majority class, the effectiveness of the classification is increased. A comparison to Artificial Immune Systems (AIS), which is one of the best methods implemented on current datasets, has shown that the proposed method is able to remarkably reduce the required training time and at the same time increase the recall that is important in fraud detection problems. The obtained results show that ARO achieves the best cost in a short time, and consequently, it can be considered a real-time fraud detection system

Credit Fraud Recognition Based on Performance Evaluation of Deep Learning Algorithm

Over time, the growth of credit cards and the financial data need credit models to support banks in making financial decisions. So, to avoid fraud in internet transactions which increased with the growth of technology it is crucial to develop an efficient fraud detection system. Deep Learning techniques are superior to other Machine Learning techniques in predicting the customer behavior of credit cards depending on the missed payments probability of customers. The BiLSTM model proposed to train on Taiwanese non-transactional dataset for bank credit cards to decrease the losses of banks. The Bidirectional LSTM reached 98% accuracy in fraud credit detection compared with other Machine Learning techniques

Interactive Learning in Decision Support

De acordo com o dicionário priberam da língua portuguesa, o conceito de Fraude pode ser definido como uma “ação ilícita, punível por lei, que procura enganar alguém ou alguma entidade ou escapar a obrigações legais”. Este tópico tem vindo a ganhar cada vez mais relevância em tempos recentes, com novos casos a se tornarem públicos de uma forma frequente. Desta forma, existe uma procura contínua por soluções que permitam, numa primeira fase, prevenir a ocorrência de fraude, ou, caso a mesma já tenha ocorrido, a detetar o mais rapidamente possível. Isto representa um grande desafio: em primeiro lugar, a evolução tecnológica permite que se elaborem esquemas fraudulentos cada vez mais complexos e eficazes e, portanto, mais difíceis de detetar e parar. Para além disto, os dados e a informação que deles se pode retirar são vistos como algo cada vez mais importante no contexto social. Consequentemente, indivíduos e empresas começaram a recolher e armazenar grandes quantidades de todo o tipo de dados. Isto representa o conceito de Big Data – grandes quantidades de dados de diferentes tipos, com diferentes graus de complexidade, produzidos a ritmos diferentes e provenientes de diferentes fontes. Isto veio, por sua vez, tornar inviável a utilização de tecnologias e algoritmos tradicionais de deteção de fraude, uma vez que estes não possuem capacidade para processar um tão grande conjunto de dados, tão diversos. É neste contexto que a área de Machine Learning tem vindo a ser cada vez mais explorada, na busca por soluções que permitam dar resposta a este problema. Normalmente, os sistemas de Machine Learning são vistos como algo completamente autónomo. Nos últimos anos, no entanto, sistemas interativos nos quais especialistas humanos contribuem ativamente no processo de aprendizagem têm vindo a apresentar um desempenho superior quando comparados com sistemas completamente automatizados. Isto pode verificar-se em cenários em que existe um grande conjunto de dados de diversos tipos e de diferentes origens (Big Data), cenários em que o input é um fluxo de dados ou quando existe uma alteração do contexto no qual os dados estão inseridos, num fenómeno conhecido por concept drift. Tendo isto em conta, neste documento é descrito um projeto cujo tema se insere no contexto da utilização de aprendizagem interativa no suporte à decisão, abordando a temática das auditorias digitais e, mais concretamente, o caso da deteção de fraude fiscal. Desta forma, a solução proposta passa pelo desenvolvimento de um sistema de Machine Learning interativo e dinâmico, na medida em que um dos principais objetivos passa por permitir a um humano especialista no domínio não só contribuir com o seu conhecimento no processo de aprendizagem do sistema, mas também que este possa contribuir com novo conhecimento, através da sugestão de uma nova variável ou um novo valor para uma variável já existente, em qualquer altura. O sistema deve então ser capaz de integrar o novo conhecimento de uma forma autónoma e continuar com o seu normal funcionamento. Esta é, na verdade, a principal característica inovadora da solução proposta, uma vez que em sistemas de Machine Learning tradicionais isto não é possível, visto que estes implicam uma estrutura do dataset rígida, e em que qualquer alteração neste sentido implicaria um reinício de todo o processo de treino de modelos, desta vez com o novo dataset.Machine Learning has been evolving rapidly over the past years, with new algorithms and approaches being devised to solve the challenges that the new properties of data pose. Specifically, algorithms must now learn continuously and in real time, from very large and possibly distributed datasets. Usually, Machine Learning systems are seen as something fully automatic. Recently, however, interactive systems in which the human experts actively contribute towards the learning process have shown improved performance when compared to fully automated ones. This may be so on scenarios of Big Data, scenarios in which the input is a data stream, or when there is concept drift. In this paper, we present a system that learns and adapts in real-time by continuously incorporating user feedback, in a fully autonomous way. Moreover, it allows for users to manage variables (e.g. add, edit, remove), reflecting these changes on-the-fly in the Machine Learning pipeline. This paper describes the main functionalities of the system, which despite being of general-purpose, is being developed in the context of a project in the domain of financial fraud detection

Credit card fraud detection using asexual reproduction optimization

Purpose – The best algorithm that was implemented on this Brazilian dataset was artificial immune system (AIS) algorithm. But the time and cost of this algorithm are high. Using asexual reproduction optimization (ARO) algorithm, the authors achieved better results in less time. So the authors achieved less cost in a shorter time. Their framework addressed the problems such as high costs and training time in credit card fraud detection. This simple and effective approach has achieved better results than the best techniques implemented on our dataset so far. The purpose of this paper is to detect credit card fraud using ARO. Design/methodology/approach – In this paper, the authors used ARO algorithm to classify the bank transactions into fraud and legitimate. ARO is taken from asexual reproduction. Asexual reproduction refers to a kind of production in which one parent produces offspring identical to herself. In ARO algorithm, an individual is shown by a vector of variables. Each variable is considered as a chromosome. A binary string represents a chromosome consisted of genes. It is supposed that every generated answer exists in the environment, and because of limited resources, only the best solution can remain alive. The algorithm starts with a random individual in the answer scope. This parent reproduces the offspring named bud. Either the parent or the offspring can survive. In this competition, the one which outperforms in fitness function remains alive. If the offspring has suitable performance,it will be the next parent, and the current parent becomes obsolete.Otherwise, the offspring perishes, and the present parent survives. The algorithm recurs until the stop condition occurs. Findings – Results showed that ARO had increased the AUC (i.e. area under a receiver operating characteristic (ROC) curve), sensitivity, precision, specificity and accuracy by 13%, 25%, 56%, 3% and 3%, in comparison with AIS, respectively. The authors achieved a high precision value indicating that if ARO detects a record as a fraud, with a high probability, it is a fraud one. Supporting a real-time fraud detection system is another vital issue. ARO outperforms AIS not only in the mentioned criteria, but also decreases the training time by 75% in comparison with the AIS, which is a significant figure. Originality/value – In this paper, the authors implemented the ARO in credit card fraud detection. The authors compared the results with those of the AIS, which was one of the best methods ever implemented on the benchmark dataset. The chief focus of the fraud detection studies is finding the algorithms that can detect legal transactions from the fraudulent ones with high detection accuracy in the shortest time and at a low cost. That ARO meets all these demands

Cryptoinsurance

The sharing economy has begun to make inroads in finance. Peer-to-peer lending is growing substantially in volume and in academic attention, though it remains less than a rounding error in comparison to more traditional sources of loans. Meanwhile, Congress passed the Jumpstart Our Business Startups ( JOBS ) Act, which directed the Securities and Exchange Commission ( SEC ) to create regulations allowing crowdfunding in at least some circumstances. The SEC, as of yet, has published only proposed rules, ignoring a congressional deadline, but state regulators have begun to create their own rules for intrastate crowdfunding. Yet, one area of finance has resisted even these tentative first steps: insurance

Cyber insurance as a risk manager

L’objectif de cette étude vise à comprendre comment les compagnies d’assurance Canadienne conceptualisent les cyber risques afin d’être en mesure de quantifier des pertes résiduelles ou en constante évolution. Par l’entremise de 10 entretiens qualitatif avec des professionnel de l’assurance, nous avons trouvé que la souscription à une cyber assurance peut aider les entrepreneurs à gérer les risques causés par la cyber criminalité. L’étude montre que la cyber assurance contribue à la compréhension et à la diffusion de connaissance en matière de cybercriminalité. Ceci est facilité par la recherche continue sur le phénomène et de la mise à jour ces polices d’assurance. Aussi, il a été trouvé que les professionnels de l’assurance facilitent l’application des mesures de prévention cyber. Cette gestion est permise grâce aux outils mis à disposition des assureurs afin d’évaluer les composantes de sécurité pour contrer les cyber attaques. Finalement, la recherche démontre que le milieu des assurances joue un rôle d’envergure dans la surveillance et la gouvernance des cyber risques.The goal of this research is to understand how Canadian insurance companies conceptualize cyber risks to quantify a residual or evolving loss. Through ten qualitative semi-structured interviews conducted with insurance professionals throughout Canada, we found that the purchase of cyber coverage contributes to the risk management efforts. Companies are increasingly looking to implement or enhance their cyber security measures through cyber insurance. In fact, the study found that cyber insurance can serve three purposes. The first is that it allows for a better understanding and diffusion of knowledge through the continuous research on cybercrimes and the revision of cyber policies. The second finding is that insurance professionals work with companies to assess and facilitate the integration of preventive measures. This is based on the tools they use to asses a company’s cyber security infrastructure. Finally, the study found that insurance companies have a considerable societal impact on the surveillance and governance of cybercrimes

In Trusts We Trust: Pension Funds Between Social Protection and Financial Speculation.

les réformes européennes des retraites ont pris pour référence le système américain. cet article propose de comprendre les origines d'une telle croyance persistante dans les vertus des fonds de pension. l'analyse met en évidence le rôle joué par leur structure juridique, le trust, dans la légitimation de la "pension industry".Recent reforms of European pension schemes have largely taken the American system as a reference.The remarks which follow are aimed at understanding the origins of such a persistent belief in the virtues of the pension funds. The analysis brings out the role played by their legal structure, the trust, in the legitimisation of the ‘pension industryFonds de pension; Trust;