Overview
The Three Major Challenges For Insurance Industry -
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More Insights From Existing Data
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Adding New Sources of Data into Existing Models
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Building Real-Time Decision Analytics Platform for Predictive and Prescriptive Analytics
Actuarial and Underwriting Analytics
Predictive Modeling is used for actuarial and underwriting analytics. It is a process whereby statistical and analytical techniques identify patterns that are then used to develop models that predict the likelihood of future events or behaviours.
Claim Analytics
Predictive Modeling is used to improve the claim process and detect fraud and provider payment abuse. Predictive Analytics is used to analyze the data for claims, fraud, and abuse detection.
Fraud Analytics
Data Mining is used to quickly detect fraud through the use of Data Mining Tools. These tools can be used for tracking millions of transactions to spot patterns and detect fraudulent transaction.
Big Data Technologies
Big Data Technologies enables Insurance Industry to modernize data systems and data infrastructure to build Real-Time Data Integration and Analytics Platform and integrate data from different data sources like sensors, Images, Videos and other sources for data science driven predictive and prescriptive analytics for finding customer needs, interests and defining risk-based pricing models and faster claim processing.
Problem Statement
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The customer has various types of datasets and data sources which consists of PDF, PDF Images, CSV Files, and SQL Server Database. The customer needs to do Insurance Analytics, Fraud Detection, Customer Profiling and Data Integration
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The customer needs Data Integration platform with functionalities for Machine Learning, Deep Learning and Predictive Analytics using Open Source Technologies and On-Premises and Hybrid Cloud Deployment.
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For Customer Profiling and Insurance Analytics, The customer needs an interactive dashboard for Data Visualization with Python, Flask Framework, Javascript, and D3.JS.
Solution Offered
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Apache Nifi as our data ingestion platform in which we used to query database processors to fetch data from SQL server database.
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To detect various fields a custom Apache Nifi processor along with Java OCR was developed.
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Apache Nifi was used to join data from various data sources.
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REST API’s and user viewed the joined data on the dashboard in the form of links using D3.JS. Ultimately client performed link analysis to identify the fraud claimants.