Insurance

Operating a complicated model where multiple lines of business cover a plethora of risks, Insurance companies generate massive amounts of data that need to be consolidated, cleansed and classified. The various distribution channels and third party agencies further create more data in a wide number of formats making data integration and categorization a leading requirement.

Our experience of having worked with both large and small sized insurance companies positions us uniquely to solve the complete range of key business challenges in the areas across claim processing, marketing, underwriting, sales and more through self learning ML systems. Our data engineers create data lakes, warehouses and ETL pipelines on both structured and unstructured data – that are highly available and scalable, providing decision makers the right insight at the right time to make the most informed and accurate business decisions.

Our Data Engineering enables Insurance companies in the areas like:

Data unification
High performance ETL processing
On-premise to cloud migration
Real-time analytics

Providing End-to-End Data Solutions at each stage of the Insurance Value Chain

Price Analytics and Optimization Market Analysis Loss Modelling
Prophet Modelling Economic Modelling Customer Profiling and Analytics
Data Driven Attribution Marketing Effectiveness Campaign Management Customer Segmentation
Customer Acquisition and Retention Cross and Up-selling Recommendation Engines
Sales Forecasting Product Selection Channel Optimization Agent Performance and Rewards Candidate Prioritization
Risk Analytics Fraud Analysis Electronic Underwriting Pricing Analytics
Claims Prediction Claims Evaluation Loss Analysis
Customer Relationship Management Sentiment Analysis Churn Prediction Propensity Modelling
Insurance Reprofiling Chatbot

Our Expertise

Risk Analytics

Determine likely areas of risk and avoid them by identifying risk quantification and reasons through matrix models and ML algorithms that forecast risks in the system

CLV Prediction

Predict Customer Lifetime Value (CLV) with the help of behavior-based ML models that assess and forecast retention, identifying opportunities to cross-sell and upsell

Fraud Detection

Analyze large volumes of customer datasets and claims information to search and identify fraud patterns, and also initiate immediate action through robust ML frameworks

Effective Data Warehousing

Create an efficient architecture that captures and analyzes data from various touchpoints like emails, call center records, social media, etc to help create a customer’s policy

Cloud Migration

Improve business agility and ensure cost savings by migrating to a cloud environment best suited to meet your data quality, availability, security and privacy requirements

Price Optimization

Capture, store and process all attributes of historical data like customer background, expenses, claims, and risks to create algorithms that dynamically calibrate premiums

Predictive Analytics

Leverage both historical and real-time data to proactively predict any impending disasters, determine risks, warn customers about the danger and prevent costly claims

Personalized Marketing

Improve marketing ROI by using advanced analytics to create personalized insurance offers and experiences on the basis of a customer’s preferences and behavioral data

Recommendation Engines

Simplify a customer’s purchase journey by generating targeted insurance propositions aimed to influence decisions based on risk profiles and psychographic data

Success Stories

Lead Buying

Delivered an ML-based approach to Lead Buying for a leading life insurance brokerage firm
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Group Risk Scoring of Patients

Built a system with 50% faster turnaround time that assigned overall group risk score using diverse data at multiple levels
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