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
