Fraud-Detection-How-Machine-Learning-Systems-Help-Reveal-Scams-in-Fintech-Healthcare-and-eCommerce

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Fraud Detection: How Machine Learning Systems Help Reveal Scamsin Fintech, Healthcare, and eCommerceFinal WordChoosing the right machine learning methoddepends on the problem type, size of a dataset,resources, etc. A good practice is to use severalmodels to both streamline assessment andachieve higher accuracy.For example, PayPal implements expressassessment using linear models to separateuncertain transactions from ordinary ones. Then,all transactions that look suspicious are runthrough an ensemble of three models comprisinga linear model, a neural network, and a deepneural network. The three then vote to arrive atthe final result with the higher accuracy.As of today, antifraud systems should meet thefollowing standards:••detect fraud in real-time••improve data credibility••analyze user behavior••uncover hidden correlationsWhile these qualities can be offered by machinelearning algorithms, they have two seriousdrawbacks to be aware of. They still requirelarge and carefully prepared datasets fortraining and still need some features of rulebasedengines, like checking legal limitationsfor cash transactions. Also, machine learningsolutions usually require substantial datascience skills to build complex and robustensemble algorithms. This sets a high barrierfor small and medium companies to usethe technique leveraging internal talent.The task requires deep technological anddomain expertise. The common practice is toengage third-party data science experts. Dataconsultancy and engineering services acceleratedevelopment, requiring less expenses thanbuilding an in-house data science team from theground up.22
Fraud Detection: How Machine Learning Systems Help Reveal Scamsin Fintech, Healthcare, and eCommerceFurther Reading1. 13 Signs Your Legacy Systems Need Modernization - https://www.altexsoft.com/blog/business/13-signs-your-legacy-systemsneed-modernization/2. An effective approach to preventing application fraud - http://www.experian.no/assets/fraud-analytics/Experian%20Fraud%20Analytics%20Overview.pdf3. Best Practices for Detecting Banking Fraud - http://www.cbai.com/news/Best_Practices_for_Detecting_Fraud_white_paper.pdf4. Comparative analysis of machine learning techniques for detecting insurance claim fraud - https://www.wipro.com/documents/comparative-analysis-of-machine-learning-techniques-for-detecting-insurance-claims-fraud.pdf5. How to Structure a Data Science Team: Key Models and Roles to Consider - https://www.altexsoft.com/blog/datascience/howto-structure-data-science-team-key-models-and-roles/6. Demystfying Machine Learning for Banking - https://hollandfintech.com/wp-content/uploads/2017/03/Demistifying-Machine-Learning-for-Banking-Feedzai-Report-March-2017.pdf7. Detecting Credit Card Fraud by Decision Trees and Support Vector Machines - http://www.iaeng.org/publication/IMECS2011/IMECS2011_pp442-447.pdf8. ID Fraud Hits Record High - https://www.javelinstrategy.com/sites/default/files/17-1001J-2017-LL-Identity-Fraud-Hits-Record-Highs-Javelin.pdf9. Legacy System Modernization: How to Transform the Enterprise for Digital Future - https://www.altexsoft.com/whitepapers/legacy-system-modernization-how-to-transform-the-enterprise-for-digital-future/10. Mastercard Rolls Out Artificial Intelligence Across its Global Network - https://newsroom.mastercard.com/press-releases/mastercard-rolls-out-artificial-intelligence-across-its-global-network/11. Next Generation Ffraud Management Solutions for Banks and Capital Market Firms - https://www.capgemini.com/in-en/wpcontent/uploads/sites/6/2017/07/next-generation_fraud_management_solutions_for_banks_and_capital_market_firms.pdf12. Preparing Your Dataset for Machine Learning: 8 Basic Techniques That Make Your Data Better - https://www.altexsoft.com/blog/datascience/preparing-your-dataset-for-machine-learning-8-basic-techniques-that-make-your-data-better/13. Supervised Learning Use Cases: Low-Hanging Fruit in Data Science for Businesses - https://www.altexsoft.com/blog/business/supervised-learning-use-cases-low-hanging-fruit-in-data-science-for-businesses/14. Synthetic Identity Fraud Cost Banks $6 Billion in 2016: Auriemma Consulting Group - http://markets.businessinsider.com/news/stocks/Synthetic-Identity-Fraud-Cost-Banks-6-Billion-in-2016-Auriemma-Consulting-Group-100222256323
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Fraud-Detection-How-Machine-Learning-Systems-Help-Reveal-Scams-in-Fintech-Healthcare-and-eCommerce

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