Main Reference PaperLeveraging deep learning with LDA-based text analytics to detect automobile insurance fraud, Decision Support Systems, 2018 [Python]
  • The combination of Latent Dirichlet Allocation (LDA) method and the deep neural networks proposed for detecting the fraudulent claims in the automobile insurance industry. The LDA method captures the hidden features from a text description of claim and then extracted feature are trained using the deep neural network for the fraud detection.

Description
  • The combination of Latent Dirichlet Allocation (LDA) method and the deep neural networks proposed for detecting the fraudulent claims in the automobile insurance industry. The LDA method captures the hidden features from a text description of claim and then extracted feature are trained using the deep neural network for the fraud detection.

  • To identify the fraud insurance in the automobile system

  • To explore the hidden feature in the text for the identification of fraudulent claims

Aim & Objectives
  • To identify the fraud insurance in the automobile system

  • To explore the hidden feature in the text for the identification of fraudulent claims

  • To validate the system for various data that helps to capture disparate topics

Contribution
  • To validate the system for various data that helps to capture disparate topics

  • M.E / M.Tech/ MS / Ph.D.- Customized according to the client requirements.

Project Recommended For
  • M.E / M.Tech/ MS / Ph.D.- Customized according to the client requirements.

  • No Readymade Projects-project delivery Depending on the complexity of the project and requirements.

Order To Delivery
  • No Readymade Projects-project delivery Depending on the complexity of the project and requirements.

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