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Insurance Analytics, Fraud Detection and Pattern Analysis

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Complete Guide to Fraud Detection Techniques in Insurance

The Three Major Challenges For Insurance Industry -
  • More Insights from Existing Data.
  • Adding New Sources of Data into Existing Models.
  • Building Real-Time Decision Analytics Platform for Predictive and Prescriptive Analytics.

Actuarial and Underwriting Analytics

Predictive Modeling 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 behaviors.

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 used to detect fraud through the use of Data Mining Tools quickly. These tools 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.

Business Challenge for Implementing Insurance Analytics

  • To implement Insurance Analytics, Fraud Detection, Customer Profiling, and Data Integration on various types of datasets and data sources which consists of PDF, PDF Images, CSV Files, and SQL Server Database.
  • Data Integration platform with functionalities for Machine Learning, Deep Learning and Predictive Analytics using Open Source Technologies, On-Premises and Hybrid Cloud Deployment.
  • For Customer Profiling and Insurance Analytics, require an interactive dashboard for Data Visualization with Python, Flask Framework, Javascript, and D3.JS.

Solution Offered for Fraud Detection and Pattern Analysis

 
  • Implement Apache Nifi as Data Ingestion platform to query database processors to fetch data from SQL server database.
  • To detect various fields, develop a custom Apache Nifi processor along with Java OCR.
  • Use Apache Nifi to join data from various data sources.
  • REST API’s and view the joined data on the dashboard in the form of links using D3.JS. Ultimately perform Link Analysis to identify the fraud claimants.

Guide to Advanced Analytics for Insurance

Claim Analytics involve insights of Claim Processing. Track Real-Time Claim Processing to improve customer satisfaction. Claim Scoring improves assignment and management of claims. Claim Data Processing promotes loss recovery, shortening of the claim cycle. Implementation of analytics to claim cycle yields in Return On Investment (ROI) with cost savings. Real-Time Claim Processing Applications involve -
  • Renewals Tracking
  • Identify Fraudulent and Useful Data
  • Data Mining
  • Claim Forecasting
  • Fraud Analytics
  • Recovery Optimization
  • HealthCare Insurance Claims
  • Claim Analytics
  • Social Network Analytics
  • Public Sector Analysis
  • Data Integration and Business Insights
  • Predictive Analytics
How to build Power Analytics in everyday Decision-Making? Auto Insurance is the key.

Auto Insurance Analytics has evolved

  • Connected Homes such as fire alarms, smoke detection etc. for remote safety.
  • Connected Vehicles to prevent mishap and accidents as well as Usage Based Insurance.
  • Connected Wearables to promote Healthcare Insurance and maintain fitness.

Insurance Analytics Real-Time Applications

  • Risk Assessment
  • Automation
  • Smarter Finance
  • Customer- Centric
  • IoT implementation
  • Real-Time Trigger Based Analytics
  • Hidden Revenue Opportunities

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