Leveraging deep learning with LDA-based text analytics to detect automobile insurance fraud

By Yibo Wang, Wei Xu. 2018

301 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Omer Berat Sezer
2. M. Ugur Gudelek
3. A. Ozbayoglu
4. Pallabi Saikia
5. Vadlamani Ravi
6. Sanchita Basak
7. Saptarshi Sengupta
8. R. Peters

In Electrical and Electronic Engineering

1. A. Ozbayoglu
2. M. Ugur Gudelek
3. Omer Berat Sezer
4. Sanchita Basak
5. Saptarshi Sengupta
6. R. Peters
7. Longbing Cao
8. Weiwei Jiang

In Information Systems

1. Vadlamani Ravi
2. A. Ozbayoglu
3. Omer Berat Sezer
4. M. Ugur Gudelek

Citations in the corpus

• Deep Learning for Financial Applications : A Survey (2020, methodology): “[121] used text mining and DNN models for the detection of automobile insurance fraud.”
• A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends (2019, background): “Zheng et al. (2018)[144] proposed a novel generative adversarial network (GAN) based model to compute probability of fraud for each large transfer so that the bank can prevent potential frauds if the probability exceeds a threshold.”