Insurance analytics

What is insurance analytics?

Insurance analytics refers to the usage of data analysis, real-time data monitoring, machine learning and AI techniques to get insights from insurance data. How does these insights help – make insurance processes better, faster, and more efficient – from underwriting to fraud detection to claims management to customer retention.

• With the help of insurance analytics, insurance and non-banking finance companies can:
• Predict customers behavior accurately
• Manage risks better
• Reduce fraudulent claims and speed up the approval process
• Monitor real-time status of existing operations and improve its efficiency. With too much data lying around, insurance analytics gives insurance companies a way to utilize them to improve their profit & revenue and thrive well in a competitive market.

Types of data used in insurance analytics

• Customer data (basic details of customer, contact details, income, occupation, etc)
• Policy data and financial information (policy type, coverage amount, duration, terms, etc)
• Behavioral data (purchase patterns, health habits, driving habits, and anything trackable)
• Festivals, seasonality, and other seasonal periods where demand spikes
• Sensor and fitness tracking data
• Other third-party data (credit scores, weather data, etc.).

Benefits of insurance analytics

• Faster claims processing, reducing and streamlining effort for claims processing, document validation, and other decision-making.
• More accurate risk assessment that considers a dynamic range of factors, unlike traditional risk monitoring that’s more about fixed set of rules.
• Insurers can combat fraudulent claims better and save more money, with less human effort.
• More satisfied customers who stay and renew, hence reduced churn – a major headache for many insurance companies.
• With centralized data management, there will be better auditing and compliance management. Any breaches and possible penalties could be avoided.
• With insurance monitoring and day-to-day tasks being streamlined, there will be more space for product innovation, market expansion, and research.

AI-powered insurance analytics

Insurance analytics is one way to begin with getting value out of data. Adding ML and AI on top of it, insurance companies could benefit more, based on following: Demand forecasting & predictive models: Like retail, eCommerce, and manufacturing businesses, insurance companies can predict demand, sales, or even likelihood events (high risk applicants, possible lapses, etc) with forecasting models and insurance analytics. Risk assessment and policy pricing: With advanced analytics and risk profiling, companies can assess risk levels and set policy pricing accordingly.
Lifetime value prediction: Insurance analytics and AI can help insurance companies predict how much a user may spend as a customer.
Automated data extraction: Similarly, data from documents could be auto-extracted with the help of AI-based OCR, that saves time wasted on manual entries. Claims automation: There are many processes in insurance processing that’s time-consuming, complex, and requires more resources, for example, document processing, data extraction, verification, validation, etc., all of which could be automated, digitized, and managed well with AI. Dynamic pricing: By assessing risk levels and other patterns through insurance analytics, personalized premiums could be set. This data could also help with upselling, cross-selling, and bundling products. Claims management: It takes a large number of people and time to manage claims and its related documentation. But users expect smooth and convenience processing and fast clearances. Insurance analytics could help the team monitor the turnaround time and other SLAs, prioritizing the critical ones. A severity prediction model could make this better so the right applicant could benefit sooner.
Chat-bot assisted claims: It’s challenging & expensive to set up a 24/7 support team. Even more problematic to leave customers queries unanswered or not be there for them in trouble. The solution is customized chatbots, combined with insurance analytics use cases, where chatbots can help users with claims, applications, submissions, and getting updates.

Document verification & fraud detection in insurance analytics

The annually reported insurance fraud costs are U.S. $308.6 Billion according to a 2021 survey. It shows how common fraud and fake claims are becoming and how insurers must set up application and triage procedures that are too hard to break through.
With fraudsters becoming more advanced, insurers also need strong AI & ML based analytics solutions. These could indicate any possible suspicious patterns with applications, underwriting, and claim submission. Here's how they could help.

• Anomaly and fraud detection & monitoring in real time.
• Fraudulent claim modelling
• Automated document verification and image analysis
• Network analysis

This leads to fast resolution and causes zero interruptions that could possibly spoil customer experience. Only when the model spots any anomalies, the team gets notified inviting further inquiries. Similarly, data verification and validation could also be automated with AI, ensuring less chances of document malpractices, fake submissions, signatures, and the like. Insurance analytics is a powerful tool, if used to fuller extent, can become a decision-making aid for Insurance companies.
How to implement insurance analytics? Insurance analytics solution requires integrating and unifying all insurance data from their sources. Once it's processed, cleaned, and formatted, you can use visualization tools to project data and turn them into visual reports.