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fraudulent Insurance claims detection using ML

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This presentation is actually focusing on how to handle insurance claims by the way whether it's fake or not .From thousands of data how to make a good algorithm for prediction using machine learning techniques.

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fraudulent Insurance claims detection using ML

  1. 1. Introduction Literature Review Neural Network Building a Algorithm for Spam Classification Result and Conclusions Future works Review spam detection and Fraudulent insurance claims Harsh Data - 2017CS10 Thejajeto Lousa - 2017CS09 Babu Pallam - 2017CS18 MNNIT Allahabad November 15, 2017 Review spam detection and Fraudulent insurance claims Harsh Data - 2017CS10 Thejajeto Lousa - 2017CS09 Babu Pa
  2. 2. Introduction Literature Review Neural Network Building a Algorithm for Spam Classification Result and Conclusions Future works Introduction Introduction Fraudulent Insurance claims. Why did we focus on it? Future enhancements. Review spam detection and Fraudulent insurance claims Harsh Data - 2017CS10 Thejajeto Lousa - 2017CS09 Babu Pa
  3. 3. Introduction Literature Review Neural Network Building a Algorithm for Spam Classification Result and Conclusions Future works Literature Review Literature Review Fraud detection of insurance claims : Only one approach ie.Mechine learning. There are lot of Data Mining Algorithms. Eg : Naïve Bayes Classifier Algorithm, K Means Clustering Algorithm, Support Vector Machine Algorithm, Linear Regression, Logistic Regression, Artificial Neural Networks, Decision Trees, Nearest Neighbours Supervised Learning could be a best approach because ; we have a big database. Neural network can do learning and make prediction accurate. Review spam detection and Fraudulent insurance claims Harsh Data - 2017CS10 Thejajeto Lousa - 2017CS09 Babu Pa
  4. 4. Introduction Literature Review Neural Network Building a Algorithm for Spam Classification Result and Conclusions Future works Application of Neural network Application of Neural network Figure: How it works Neural network can train the dataset using learning algorithms like gradient descent[1] Review spam detection and Fraudulent insurance claims Harsh Data - 2017CS10 Thejajeto Lousa - 2017CS09 Babu Pa
  5. 5. Introduction Literature Review Neural Network Building a Algorithm for Spam Classification Result and Conclusions Future works Optimize data Principle Component Analysis Selecting Principal Components Optimize data Mapping to the form that is familiar to algorithms. Reducing dimensionality using PCA. Review spam detection and Fraudulent insurance claims Harsh Data - 2017CS10 Thejajeto Lousa - 2017CS09 Babu Pa
  6. 6. Introduction Literature Review Neural Network Building a Algorithm for Spam Classification Result and Conclusions Future works Optimize data Principle Component Analysis Selecting Principal Components Overview of PCA The main goal of a PCA analysis is to identify patterns in data. PCA aims to detect the correlation between variables,by the way it attempts to reduce the dimensionality. The desired goal is to reduce the dimensions of a dd-dimensional dataset by projecting it onto a (k)(k)-dimensional subspace (where k<dk<d) in order to increase the computational efficiency while retaining most of the information. Review spam detection and Fraudulent insurance claims Harsh Data - 2017CS10 Thejajeto Lousa - 2017CS09 Babu Pa
  7. 7. Introduction Literature Review Neural Network Building a Algorithm for Spam Classification Result and Conclusions Future works Optimize data Principle Component Analysis Selecting Principal Components Selecting Principal Components Obtain the Eigenvectors and Eigenvalues from the covariance matrix or correlation matrix, or perform Singular Vector Decomposition. Sort eigenvalues in descending order and choose the kk eigenvectors that correspond to the kk largest eigenvalues where kk is the number of dimensions of the new feature subspace (k≤dk≤d)/. Construct the projection matrix WW from the selected kk eigenvectors. Transform the original dataset XX via WW to obtain a kk-dimensional feature subspace YY. Review spam detection and Fraudulent insurance claims Harsh Data - 2017CS10 Thejajeto Lousa - 2017CS09 Babu Pa
  8. 8. Introduction Literature Review Neural Network Building a Algorithm for Spam Classification Result and Conclusions Future works Classification of Dataset classification of dataset Auto insurance Figure: Output of auto insurance claim dataset Review spam detection and Fraudulent insurance claims Harsh Data - 2017CS10 Thejajeto Lousa - 2017CS09 Babu Pa
  9. 9. Introduction Literature Review Neural Network Building a Algorithm for Spam Classification Result and Conclusions Future works Classification of Dataset classification of dataset Classification of insurance claim dataset Figure: Output of insurance claim dataset accuracy Review spam detection and Fraudulent insurance claims Harsh Data - 2017CS10 Thejajeto Lousa - 2017CS09 Babu Pa
  10. 10. Introduction Literature Review Neural Network Building a Algorithm for Spam Classification Result and Conclusions Future works Future works Future works Future is DEEP LEARNING Reduce False positives Review spam detection and Fraudulent insurance claims Harsh Data - 2017CS10 Thejajeto Lousa - 2017CS09 Babu Pa
  11. 11. Introduction Literature Review Neural Network Building a Algorithm for Spam Classification Result and Conclusions Future works Future works William H Wolberg, W Nick Street, and OL Mangasarian. Machine learning techniques to diagnose breast cancer. Cancer letters, 77(2-3):163–171, 1994. Review spam detection and Fraudulent insurance claims Harsh Data - 2017CS10 Thejajeto Lousa - 2017CS09 Babu Pa

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