Insurance and Data Science:Overview

Challenging economic conditions, unparalleled competitive pressure and rising customers are drivers for data science and insights deployment in Insurance Sector.

DnI Consulting provides Data Science and Insights based services to Insurance clients in improving profitability, customer service and regulatory compliance. DnI Consulting brings in Insurance Industry knowledge, Statistical & Machine Learning Expertise and Analytical & Visualization tool experience for providing actionable insights and recommendations.

Data Science and Insights for Insurance:-
Sales & Acquisition Management Underwriting & Risk Management Customer Analytics Operational Excellence
Agent Performance & Productivity Analytics CAT Modeling Customer Life Time Modeling Claim Volume Forecasting
Agent Reward Optimization Risk Concentration and Severity Analytics Customer Retention Management Loss Forecasting
Insurance Lead Identification Risk and Premium Models Cross Selling Model and Analytics Claim Cycle Resolution
Lead Profiling and Segmentation Application Fraud Model Social & Media Claim Fraud Model
Marketing Mix Modeling Web Analytics Claim Resource and
Campaign Analytics Switchover Modeling
Examples of Data Science & Insights deployment:-
Offering Use Cases
Sales & Acquisition Management One of the key acquisition channels for Insurance provider is independent agents and brokers. An appropriate reward program is designed for acquiring customer volumes at right profit level.
Underwriting & Risk Management Insurance Policy providing protection and reducing risk for policy holders. But it is very critical for Insurance Underwriters to estimate risk involved and policy premium. Survival Curves for various segments are built to estimate risk involved for the segments.High Relapse rate could have severe consequences on portfolio profitability and liquidity reserve requirements. Relapse Predictive Model could help Insurance providers in regulatory capital calculation under Solvency II.
Customer Analytics Due to ease of switching insurance policies across providers especially for P&C Insurance, building predictive model to find the likelihood of customers switching policy to competitors could help in talking proactive actions.
Operational Excellence Insurance providers across world including US are losing money due to Fraudulent claims. Insurance providers can leverage tools, technology and unstructured & structured data sources for building sophisticated predictive models for segregating fraudulent claims from the genuine claims.
Point of Views:-
Problem Statement Our Point of View
Fragmented Data Sources: Due to archaic and disparate systems, Insurance providers does not have 360 view of the customers Integrating data sources and creating Master Data Management system is critical for developing 360 degree view of the insurance customers. Steps used for creating single view of the customers are
  • Assignment of common key or identifier is critical before proceeding on advanced.
  • Understand the typical reporting and analytics requirements.
  • Create a list of KPIs which cater to 80-90% 0f the analytics requirements.
  • Create a Master Data Mart for the reporting and analytics requirements – ensure governance& access, data security, data load, and Data Quality Review.
  • Master Data Mart will get inputs from various data sources – Policy, Servicing, Claims, and Fraud data
  • Migrate Reports and Analytics to the MDM created.
  • One view of the truth and reduce time to deliver insights.
  • MDM can be key input to predictive modeling and other analytics needs.
High Switcher on Home Insurance Portfolio: Over 30% of Home Insurance policies were getting closed after first year. Due to high acquisition cost first year premium on Home Insurance is not even enough to cover the cost. Insurance provider need to build strategy to acquire and retention right customers to make money from the home insurance portfolio. Following steps can be built to grow and manage Home Insurance portfolio profitably.
  • Identify internal and external data sources for building Predictive Model.
  • Exploratory Data Analysis (EDA).
  • Building Predictive Model to identifying home insurance policy holders who are more likely to switch.
  • Profiling of the switchers - how these policy holders are acquired?
  • Leveraging Predictive Model for retaining right policy holders and also re-looking acquisition strategies to acquire right policy holders.