Credit Card Fraud Detection through Data Mining

Credit Card Fraud Detection through Data Mining Abstract With the increasing fraudsters day to day, the fallacious transaction is rapidly growing thus making the frauds in this scenario a matter of high importance. Huge database patterns are identified by various data mining models assisting to strengthen and detect the credit card fraud. This research primarily focusses on Credit Card Fraud Detection analyzing two different methods used to detect fraudulent systems and implement the new technologies to the system to minimize frauds in credit card transactions Keywords- Data Mining, Methods, Credit Card, Fraud Detection, Hybrid Technology, Neural Network, Support Vector System Data Mining is filled with application growth opportunities and research which are reliable and usable from the data. Rapid development of e-commerce, usage of credit card has become well known mode for online and regular transactions, increasing the credit card fraud simultaneously. Fraud detection is a complicated problem as unwanted transactions are hidden in the authoriosed transaction. Due to security reasons and also to gain trust of users credit card fraud detection has now become important to companies. Neural Networks [1], Bayesian Network [2], Hidden NaÃÆ' ¯ve Bayes Network [12], Dempster Shafer [10] are few methods to be justified in detecting the credit card fraudulent system. With the new technologies, it has now become easy for the companies and banks to detect the fraudulent system. With the growing credit card fraud problem in the industry this literature review will help us to understand and detect the techniques involved in detecting the fraudulent system. We will be describing two different approaches Neural Network and Support vector machine approach thus learning a new method to minimize the fraudulent system. This paper helps us to analyze data mining methods with respect to credit card fraud system. A. Neural Network Approach Neural network fraud detection method is primarily based on working of a human brain. Just as the human brain is capable of learning things from the previous experiences and uses the knowledge to decide things occurring in day to day problems the same strategy is used while detecting a credit card fraud with Neural Network system. Neural Network can reflect a small part of complexity and regulation Banks use this kind of network method to detect the credit card fraud. The moment a transaction takes place there are a set of attributes attached to it characterizing the account holder, the amount and the merchant. Considering an example, for the Mellow Bank Fraud Detection Feasibility Study a particular archived amount of data was used for model development as the authorized data wasnt easily available due to security reasons. P-RCE [3] neural network technique is used. P-RCE is used for pattern recognition as it helps to describe what exactly the human brain is thinking about. P-RCE has a single cell layer which outputs a numeric response called as Fraud score. The lower the threshold the more no. of credit card fraud is detected. Higher the credit card detection threshold less no. of fraud is detected. With 2000000 transactions of Mellon Banks data from Oct-Nov 1991, nearly measuring 50 accounts per day 40% of the fraudulent transaction was observed but prior to use of P-RCE method in Mellon Banks feasibility study the result came out to be 1 fraudulent transaction per week on reviewing 750 account per day. The improvement in the fraud detection performance was undeniably considerable. The p attern recognition method can actually help the banks to reduce 20-40% (in total) credit card fraud losses. B. Support Virtual Machine Approach An online transaction has four entities: credit card holder, credit card, the seller and the buyer. There is an independent history of transactions with each set of entities in the data set. Each entity keeps almost a consistent behavior pattern in authorized transactions. The risk of a transaction can be estimated by calculating the inconsistency of a transaction from the history of authorized transactions. Let us define Xe(t) {a1, a2, à ¢Ã¢â€š ¬Ã‚ ¦, an) as the transactional set in the entitys history is equivalent to f(t) beingF(t) {seller, buyer, holder, card) set of entities. We calculate the score of transactions, by defining l = (l1 , l19) [3] with c as value of a feature, as SC(l,e,t) = count(c,Xe(t)) The idea to calculate how near a transaction is related to past authorized transactions therefore we consider only those transactions that are proved legitimate from Xe(t). To train and test a classifier (weight and score) we have a vector in conjunction with the classification: SVM (Support Vector Machines). They are essentially supervised learning models used for analyzing data and recognition of patterns. Using the traditional methods, raw data was classified with SVM to check the impact of general weight in the outcomes and reduce SVM complexity as well. SVM classifier was used in classifying the transactions as fraud or authorized resulting in 40-50% in most months with false alarm rate 10-12%. The online credit card fraud system can be detected and improvised with the Big Data Technologies framework. The main aim is to achieve the goal of fusing various detection methods to enhance the accuracy. A workflow was proposed [13] containing common designs of fraudulent system thus making it easier to integrate identification of fraud system. In recent years, big data platforms were released to process and operate data including MapReduce and Apache Hadoop frameworks (open source for MapReduce). Two components are primarily considered: spouts and bolts. The source of streams is referred to as spout which reads and sends tuples from external source into topology. The data processing is done by Bolt. With the reference, the paper proposes a hybrid structure with Big data efficient in solving challenges related to performance and integration. The basic workflow is defined [13] in the figure. QF- Quick filter, DSA Demper Shafer Adder (combining different fraud scores and generating a merged result), EF- Explicit filter. This workflow is designed by combining different algorithms together for a higher accuracy like two DSAs are combined so that we can aggregate their fraud score to get better accuracy; 2) Another aspect can be considered by combining supervised unsupervised fraud approaches to examine a good cover of types of fraud; 3) The filters used: QF detects only the behavior of card holder whereas EF detects the historical data in the whole model. So, to achieve faster filter we can combine QF and EF as well as to get better efficiency the combination is good to go. In this paper, we have reviewed two data mining detection methods of credit card fraud. The research papers which arent considered here might have comprehensive methods to research and implementations of new detection techniques. This research paper describes: The Neural Network can be implemented in banks to reduce the credit card fraudulent system with it P-RCE algorithm. 2) Support Vector machine can detect the frauds in ecommerce system real time but isnt much reliable for complex frauds. 3) Hybrid technology framework, the workflow is essential to detect frauds in offline system as the method but can improve accuracy, performance and efficiency. To develop a credit card fraud system, the neural network method is best suited according to my understanding as it is efficient, accurate and cost effective thus implemented in Mellon Bank. Neural Network method has some failures as well but gradually with new technology it can overcome. But to develop a strong fraud detection system using credit card we need to combine few more complex detecting methods. References [1] S. Ghosh and D. L. Reilly, Credit card fraud detection with a neural network, in System Sciences, 1994. Proceedings of the Twenty-Seventh Hawaii International Conference on, vol. 3, Jan 1994, pp. 621-630. [2] G. F. Cooper and E. Herskovits. A Bayesian Method for the Induction of Probabilistic Networks from Data. Machine Learning, 9(4):309-347, 1992. Santiago, Gabriel Preti, Adriano Pereira, and Roberto Hirata Jr. A modeling approach for credit card fraud detection in electronic payment services. In Proceedings of the 30th Annual ACM Symposium on Applied Computing, pp. 2328-2331. ACM, 2015. Online Credit Card Fraud Detection: A Hybrid Framework with Big Data Technologies You Daià ¢Ã‹â€ -, Jin Yanà ¢Ã‹â€ -, Xiaoxin Tangà ¢Ã‹â€ -, Han Zhaoà ¢Ã¢â€š ¬Ã‚   and Minyi Guoà ¢Ã‹â€ - à ¢Ã‹â€ -Department of Computer Science and Engineering, Shanghai Jiao Tong University, China à ¢Ã¢â€š ¬Ã‚  School of Computer Science Technology, Huazhong University of Science and Technology, China Philip K. Chan, Wei Fan, Andreas 1. Prodromidir, and Salvotore 1. Stalfo, Distributed Data Mining in Credit Card Fraud Detection 2016 IEEE TrustCom/BigDataSE/ISPA H. Michael Chung Fredric C. Gey Data Mining, Knowledge Discovery, and Information Retrieval, Proceedings of the 34th Hawaii International Conference on System Sciences 2001 Agrawal, Ayushi, Shiv Kumar, and Amit Kumar Mishra. Implementation of Novel Approach for Credit Card Fraud Detection. In Computing for Sustainable Global Development (INDIACom), 2015 2nd International Conference on, pp. 1-4. IEEE, 2015. [2008] Abhinav Srivastava, Amlan Kundu, Shamik Sural and Arun K. Majumdar, CreditCard Fraud Detection Using Hidden Markov Model IEEE, Transactions on Dependable and Secure Computing, Vol. 5, No 1., January-March D. L. Reilly and L. N. Cooper, An overview ofneural networks: early models to real worldsystems, in An Introduction to Neural and Electronic Networks, ed. S. F. Zometzer, J. L. Davis and C. Lau, 227-248, Academic Press, (1990). S. Panigrahi, A. Kundu, S. Sural, and A. Majumdar, Credit card fraud detection: A fusion approach using dempstershafer theory and Bayesian learning, Information Fusion, vol. 10, no. 4, pp. 354 363, 2009. Z. D. Zhao and M. s. Shang, User-based collaborative-filtering recommendation algorithms on hadoop, in Knowledge Discovery and Data Mining, 2010. WKDD 10. Third International Conference on, Jan 2010,pp. 478-481. [12] Jiang, Liangxiao, Harry Zhang, and Zhihua Cai. A novel Bayes model: Hidden naive Bayes. IEEE Transactions on knowledge and data engineering 21, no. 10 (2009): 1361-1371. [13] Dai, You, Jin Yan, Xiaoxin Tang, Han Zhao, and Minyi Guo. Online Credit Card Fraud Detection: A Hybrid Framework with Big Data Technologies. In Trustcom/BigDataSE/Ià ¢Ã¢â€š ¬Ã¢â‚¬ ¹ SPA, 2016 IEEE, pp. 1644-1651.IEEE, 2016.

Oskar Schindler: A Hero Study Essay -- essays research papers

CONFLICT AND RELEVANT BACKGROUND Oskar Schindler faced many conflicts in his life. The main conflict he faced was overcoming the Nazis and saving over one thousand Jewish People. Schindler, with out a job at the time, joined the Nazi Party and followed on the heels of the SS when the Germans invaded Poland. This is when Schindler took over two previously Jewish owned companies that dealt with the manufacture and sales of enamel kitchenware products and opened up his own enamel shop right outside of Krakow near the Jewish ghetto. There, he employed mostly Jewish workers, which saved them from being deported to labor camps. Though twice the Gestapo arrested him, he got released because of his many connections and with many bribes. Most importantly, he helped save an entire race of human beings. CHRONICLER AND MEDIA We know of Oskar Schindler through many different types of the media. We know of him through the television, newspapers, books, and by word of mouth through the Jewish people he had saved. There was even a movie based on Oskar Schindler called Schindler’s List. There are several books about him, many were even written by some of the Jews he saved themselves. EPIC HERO ELEMENTS IN THE LIFE OF OSCAR SCHINDLER Oskar Schindler was born on April 28, 1908 in Zwitlau, which is now part of the present day Czech Republic, to his father and mother, Hans and Louisa Schindler. They were a deeply religious family, which resulted in a strong catholic household for Oskar and his younger sister Elfriede. The Schindler family was one of the richest and most prominent in Zwitlau and elsewhere. Mostly due to the success of their family owned machinery business. Oskar Schindler wasn’t necessarily born of noble birth. He was basically an ordinary guy, especially when his father lost his business, who did extraordinary deeds for people who he has never even met. One can also see how Schindler was a hero in is character traits. Schindler was always thoughtful and humane in dealing with Jews. He saved the most Jewish people during World War II. The society at that point of time was going downhill and almost came to a big crash. Hitler could have destroyed a whole race of people if it wasn’t for the help of Oskar Schindler. Although he did join the Nazi’s, Schindler still had a heart and a mind to know what Adolf Hitler was doing was wrong and immoral and to try and he... ...ers. Further more, there is a universal theme, such as good and evil. The good would definitely be Oskar Schindler who helped the Jewish people and didn’t need, nor want any type of reward for it. The evil side would be Adolf Hitler who was trying to kill an entire race just because of their religion, believing that they were the evil ones. He only thought this way because he was ignorant of the Jewish religion, and other types of people, and therefore feared them, which caused him to hate and want to kill them all. At the end though, as it is most commonly believed to be the evil should die and the good should live. Though most of the Jews died, a few thousand still lived while the evil side, Adolf Hitler, died by committing suicide. DEATH OF OSKAR SCHINDLER Oskar Schindler died in October 1974, and is buried in the Latin cemetery on Mount Zion, Jerusalem. Over one thousand Jews owe their lives to this singular man. On his grave, there lie many stones, each from many Jews who he saved and many other Jews who think of him as hero. I do not know he died how he died exactly but it is basically from natural causes. Schindler is still adored as a hero from many of the Jewish people.

Demise of the Weimar Republic

The demise of the Weimar Republic did not occur due to one sole reason, but because of a number of short and long term factors. The economic, political and international pressure placed on the people of Germany ensured the loathing of the forced governmental rule. The immediate impact of the Great Depression of 1929, which has been debated and considered by most historians to be the dominant factor in the downfall of the ‘democratic experiment’ which subsequently led to continuous political instability ultimately saw the collapse of the first democratic government of Germany.Some historians generally blame the fall of the Republic on president von Hindenburg’s abuse of constitutional powers. Historian David Martin states that the â€Å"survival of democracy depended on his (Hindenburg’s) integrity and decisions. Unfortunately, his actions made a major contribution to its destruction†. More deadlocks within the Reichstag and civil unrest gave Hindenbu rg no choice but to give the people what they wanted, which was a strong leader in Adolf Hitler. The Weimar republic was not welcomed by Germany after her defeat in world war one in 1919.Germany had never had a democracy before and felt that the Allies had forced this treaty upon them. Even Prince Max of Beden did not want a democracy for Germany. Because this new government had signed the treaty they were now known as the ‘November Criminals’ and were loathed by everyone. According to Henig, ‘It was the acknowledgement of defeat.. which they found so hard to accept. ’ The historian Friedrich Meinecke observed that, for true Germans, loyalty to the ‘Fatherland required disloyalty to the republic’.In a sense the Weimar Republic was a â€Å"democracy without democrats† Treaty of Versailles The military defeat of Germany in November 1918 came as a genuine shock to most Germans, who had believed that Germany would be victorious in the war. W hen seeking armistice in the same month, Germans expected the peace settlement to be based on President Wilson’s 14 points and although they also did not expect a soft treaty, it was greater then imagined. The Treaty was a complete humiliation for Germany and was a dent to national pride.It had to be accepted however as the allies threatened To resume war again and still maintained a naval blockade so consequently on the 28th of June, 1919, the Treaty was signed. As a result of the Treaty, Germany lost all of its colonies including Alsace and Lorraine. The army was reduced to 100,000 men, with further restrictions placed on naval and air force. 33 million dollars of reparations had to be paid and these economic provisions of the treaty effectively weakened Germany’s capacity to pay by 1924.The Weimar Republic and the democracy were weakened because both were always associated with the Treaty of Versailles. This was a constant theme used by those who opposed the republi c and the democratic system. Because this new government had signed the treaty they were now known as the ‘November Criminals’ and were loathed by everyone. According to Henig, ‘It was the acknowledgement of defeat.. which they found so hard to accept. ’ Period of Hyperinflation Hyperinflation gave the people of Germany another reason to find fault in the democratic Weimar Government.Louis Snyder wrote that it was â€Å"these people who later turned to Adolf Hitler as the messiah to lead them out of financial chaos†. Immediately after the war, Germany had little capacity for economic recovery as key industrial resources had been lost in the Treaty and had little export trade and consequently unable to attract any major foreign investment loans. To get more finance, the German government’s response was to print more money so the value of German currency continued to fall. Before the US dollar has been 4. 2 marks, by 1920 it was worth 64. marks thus German taxation increased. Anyone with access to even a few pounds became a trillionaires over night. The losers of the hyperinflation were those with a fixed income, the working class, people living off their savings and wage earners. By June 1923, the government had lost control of the economy, as strikes swepped the country as workers protested about the rising unemployment and the failure of their wages to keep up with the cost of living. In August, the govenment finally fell, with President Ebert called or the leader of the German people party, Streseman, to form a new party. He formed the ‘great coalition’ which brought together left and right wing. In the next six years, German society picked up with the induction into the League of Nations, the Dawes plan of 1924 which provided foreign loans to Germany and the Young Plan which saw the reduction in Germany’s reparation figure by 95 million. The downside of Germany’s new economic prosperity mean t that society was living off borrowed money and was over-dependant on foreign loans and investment.The economic power was concentrated in the hands of influential industrialists and bankers and employment was still on the rise. Within these six years, the Weimar republic surprisingly enjoyed a period of relative political stability. President Ebert’s sudden death in 1925 followed President Hindenburg into the role. He’s election reconciled many in the army to support the republic goldern years– that â€Å"this was merely nothing more than depictive stability† d. packart. The Depression Historian W.M Simon wrote, â€Å"The depression had created a situation in which parliamentary government broke down, opening the way to presidential government by decree†¦The more openly the political parties became parliamentary pressure groups, the more the parliamentary process (and the democratic process) as a whole fell into discredit†. The great depressi on of 1929 occurred very suddenly and ended the period of relative affluence for Germany. The depression was the result of substantial amounts of investment from overseas and the high interest rates of 1928 which consequently left the economy with a reduction in economic activity and employment.Consequently social spending reduced and people were left without food and shelter which created overall bitterness. In response the government chose to follow a policy of deflation in which they cut expenditure and attempted to balance the budget. Throughout this extremely stressful time, the fear of communism had increased enormously. Membership of the Communist Party grew enormously and most feared that if a revolution occurred, German people would have their private property confiscated.Therefore, most were heartened by the strongly anti communist position taken by Hitler and his Nazi Party. â€Å"dependent on the American economy, made Germany vulnerable to the instabilities of the worl d markets† W. link 1970. Rise of the Extremists and Hitler/National Socialists – Failure of the German Democracy 1930-33 By 1930 Mullers government had failed to deal with the Depression. Bruning was appointed chancellor and in July his budget passed the Reichstag using article 48. At the same time the Nazi’s attacked the republic, critizing its weak parliamentary system.They promised to revitalize Germany with determination and conviction, offering hope to the middle class, small business operators and rural/farming population â€Å"The Depression put the wind into Hitler’s sails† (Taylor). In September 1930, the Reichstag Elections saw the Nazi’s win 107 seats in the Reichstag in which they previously held 12. the Reichstag became virtually unworkable as the Nazi’s, communists and Nationalist all opposed Bruning. Another critiscm of the Weimar constitution was Article 48 where in an emergency the president could override any laws, th e opinions of the Reichstag and the people.He could also have total control of the army to ‘restore order’. With the army on his side and the republic disregarded, the President would have total control. Evidently from the above paragraphs one can conclude that the Weimar Republic did not collapse due to one reason but was a process consisting of a number of long and short term events. The Republic was flawed and weak from its beginning in 1919 and it was a miracle that it survived until 1933 in a country that opposed the Republic as a whole.

Providing the World with Drinking Water in the 21st...

Each day, over 5,000 children die from diarrhea-related diseases developed from unsafe drinking water. Approximately one billion people do not have access to clean drinking water; one billion people about equates to one out of every six individuals. The deaths resulting from unsafe drinking water are greater than the number of deaths caused by war. We all must work together to find new sources of freshwater so that everyone in the world will have adequate supplies of safe drinking water. This essay will outline current and future technologies that will be available to resolve this problem in the coming years. Water covers nearly three quarters of the Earth, yet people still die everyday from the lack of fresh water. How is†¦show more content†¦Modern desalination projects use a method called â€Å"reverse osmosis,† which separates the salt from ocean water by pushing the water through a membrane at high pressures. The idea of desalination is not a new one; today, over 12,000 desalination plants operate around the globe . However, while the idea seems sound, the actual implementation of the process costs extraordinary amounts of money due to the large amounts of energy needed to run the process. Many of the 12,000 desalination plants that are in operation are located in coastal regions of high affluence. Yet, the areas in dire need of freshwater include the Middle East and northern Africa, which areas of limited water access and financial support. If the costs of energy use in the desalination process cannot be lowered it will not be able to solve the fresh water prob lem. Countries such as the United States, China, and India have employed large-scale operations to divert water from regions with an abundance of fresh water to regions lacking in fresh water supplies. These programs have been successful, yet the success is defined only in the short-term. 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