Data exists in a variety of forms within silos across insurance business processes, making it difficult to access, interrogate and derive actionable insights and business value. Data as a Service (DaaS) is an increasingly popular solution for data integration, management, storage, and analytics. By embracing DaaS, the insurance industry is making leaps in data workload agility, reducing the time spent gathering insights and increasing their data integrity.
Some insurance companies are leveraging DaaS to speed and simplify obtaining insights from data and achieving better data integration and governance. Let us shed some light on how a data-driven foundation is reconstructing the new era of insurance:
Customer Acquisition
A data lake is a centralized repository that allows storing all structured and unstructured data at any scale. According to Mordor Intelligence, the data lake market value is expected to reach $17.6 billion by 2025. In order to manage Big Data effectively and drive real-time analytics and decisions, insurers invest heavily in data lake services, like AWS (Amazon Web Services).
AWS AI/ML services also provide sentiment analysis or text extraction, analyze the call records, helping take the next action in real-time. Digital ethnography offers much more granular information for customer behavioral analysis. Insurance companies analyze customer behavior data from various online sources, social networks, emails, and feedback. Through deeper insights, insurers create targeted marketing campaigns to bag the right customers through the proper channels with the suitable business offerings in the correct sequence.
Customer Retention
A recent study by IBM shows that Insurance companies are actively investing in customer acquisition rather than retaining old customers, even though they are losing about 16% of customers each year. Retaining an old consumer is less expensive in almost all cases than acquiring a new one. Consumer retention often comes with responsive customer service, digital claim processing, and rapid rollout of policies. Pricing also plays a crucial role in retaining consumers, and competitive pricing acts as a loyalty driver for customers.
In order to predict the early signs of customers’ dissatisfaction, insurance companies use predictive analytics for proactive and personalized customer services. Through obtained data insights, insurers can quickly react to enhancing their services and find a solution to customers’ grievances. Insurers can offer discounts or even change the pricing model for their customers. Timely, appropriate actions can considerably increase customer satisfaction and help retain valuable policyholders.
Fraud Detection
A Gartner report indicates that annual losses due to insurance claims fraud (non-health insurance) are estimated to be $40 billion per annum. Big data analytics can save insurance companies against insurance frauds, including Property and Casualty, Life, and Medicare. By applying a predictive model, insurers can compare a person’s data against past fraudulent profiles and identify cases that require more investigation. Before arriving at a final decision, insurers can utilize big data and use predictive modeling to count on possible issues based on clients’ details and put them into a suitable risk class. Predictive modeling can help to spot the suspect early by exploiting patterns found in historical data to identify risks.
Cost Reductions
According to BARC (Business Application Research Center), 40% of insurance companies leverage data to cut administrative expenses through strategic decision-making and better customer insights. Research shows that businesses that depend on data-driven strategies are gaining insights faster to increase overall revenue and cut operational costs. Insurers are adopting automated data processes to escape the legacy framework. The modernization of claims platforms and the deployment of chatbots, document ingestion tools, and artificial intelligence (AI) for data extraction and acquisition are helping insurers improve productivity and indemnity performance while reducing operational costs. AI-driven innovation is helping harness cognitive learning insights from new data sources to streamline business processes and reduce overhead costs.
Data Governance
A recent report by Gartner indicates that 30% of organizations will be investing in data and analytics governance platforms by 2024. An efficient data governance framework can solve most of the obstacles in executing a successful data strategy. It helps make better decisions, enhance personalization, and improve customer experience. Legacy data captured through manual processes often lack consistency, timeliness, completeness, accuracy, and context, making the insights unreliable for strategic decisions. Insurers can do a granular analysis of the existing data, establish quality baseline standards, and exercise quality improvement initiatives. This can help normalize data, clear out duplicates, and combine multiple datasets for effective processing.
Data Solutions for Insurance Best Practices:
• Data mining techniques can distinguish claims based on their complexity and help ensure appropriate adjusters handle the specific claim.
• A connected insurance platform enables insurers and insurance brokers to create new products services based on usage and behavior data.
• Pay-per-mile insurance is specifically designed to control customers’ monthly car insurance bills based on how much they drive.
• An AI-powered virtual assistant solution popularly known as Insurance chatbots has been designed to cater to customers’ needs, transforming how insurance brands acquire, engage, and serve.
Insurance companies can use advanced analytics to identify subrogation cases from the massive piled-up data distributed in forms, police reports, and adjustor notes. Through more effective data governance and improved data integrity, insures can maintain an edge over competitors and streamline their internal operations.
At Fulcrum Insurance, our 3rd Generation AI/ML-based data extraction platform helps Insurance companies boost productivity, reduce risk, and improve turnaround times through maximizing data extraction, acquisition, organization, and governance with 100% accuracy at 90% automation.