fraudulent cases constitute a small percentage of the overall transactions

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TestBank wants to catch suspicious credit cards transactions for further fraud investigation. The bank decided to invest in a new system, their budget for the system is 1m. The bank provided us with their historical transaction data with a set of attributes for each case that flags a transaction either being normal or fraudulent as in the dataset that you can download from the link. Fraudulent cases constitute a small percentage of the overall transactions. Each case that is nominated as fraudulent and it turns out to be not fraudulent costs the bank around 1k, such cases harms the bank customer satisfaction. The bank is willing to put an annual budget for those cases of £30k. (hint: convert these to a false positive percentage) Each case that is not nominated as fraudulent and it turns out to be fraudulent costs the bank around 10k, such cases harms the bank reputation and costs them future customers. The bank is willing to put an annual budget for those cases of £50k. (hint: convert these to a false negative percentage) Their main requirements are 1- to catch at least 90% of actual fraudulent cases, and 2- at least 70% of the predicted cases for further investigation to be actually fraudulent. Your mission is to devise a model that is able to satisfy their needs as much as possible. Use suitable measures in order to report your results accordingly. You need to use two techniques and compare them for the bank. At least one the techniques must be advanced: Neural Network or Logistic Regression. You are required to identify, implement, evaluate and compare two intelligent techniques suitable for solving a realistic case study. At least one of the techniques must be advanced.

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