Predictive Insurance Analytics: Use Cases and Benefits
With the help of advanced analytics tools and techniques, insurance companies are discovering new ways to optimize policy pricing, streamline the claims process, and improve customer service. With insurance analytics powering innovation, companies can now compete against emerging insurtech providers, as well as gain a significant advantage over competitors who have yet to embrace a data-centric approach to their business workflows.
How Analytics Helps Insurers Meet Today’s Challenges
Today’s insurers are facing a dynamic risk environment and significant competition from InsurTech industry disrupters. Here are five ways insurance analytics is helping modern insurance companies leverage their data to solve these challenges and to remain ahead of their competitors:
Risk insights enhanced with third-party data sources
Third-party data represents a massive opportunity for improving the profitability and efficiency of commercial insurance underwriting. Insurers who offer commercial policies can now access insurance industry data that provides deeper insights into the risks associated with an applicant. For example, detailed weather, climate, and sustainability data can help underwriters better understand the natural-disaster risk potential of homeowner property or certain agricultural businesses by using geospatial analytics and ESG data. Risk insights are also used by actuarial teams in the actuarial workflow to determine how much risk the insurer should take on.
Optimized policy pricing and coverage for underwriting
Pricing sophistication is essential to maintaining profitability. Armed with insights based on near real-time data, insurers can optimize their policy pricing. For example, monitoring dynamic variables such as the cost of construction materials, labor costs, and the prevalence of similar claims in the immediate vicinity can help insurers better understand their level of risk, helping them more accurately price their property insurance policies.
Streamlined customer claims processing
Manually processing claims is expensive for insurers and frustrating for customers facing extended wait times. Insurance analytics can be used to automate large portions of the claims process, providing self-service claims submission powered by intelligent document processing, machine learning (ML)–enabled adjudication, and fraud detection. With fewer touchpoints and faster payouts, insurers can reduce operational costs and efficiency while increasing customer experience.
Enhanced insurance fraud detection
Fraudulent insurance claims are a significant problem. Advanced analytics can leverage first- and third-party data to accurately identify fraudulent claims. By combining internal data such as the frequency rate of past claims with data from third parties such as credit agencies, these algorithms can automatically earmark potentially fraudulent claims for a more thorough manual review.
Personalized service for improved customer retention rates
Analytics tools can track the prevalence of certain types of customer service requests, helping decision-makers better understand where to focus resources for improvement. Insurance analytics and data can also be used to create a true 360-degree view of customers by aggregating data including demographics, credit score, mobile app usage, and current insurance products. By developing a more in-depth understanding of customers, insurers are able to not only improve customer retention, but also increase the relevance of their recommendations, boosting the effectiveness of cross-selling and upselling opportunities.
Examples of Insurance Analytics in Action
Insurance analytics helps insurers uncover new relationships in their data, providing the actionable insights needed to thrive in a challenging industry. Predictive analytics tools and techniques have broad applications across the industry, including personal, small business, and large commercial lines.
Personal and property
Many insurers remain tied to traditional risk segmentation models and underwriting criteria for personal and property insurance policies such as auto and home. With fast access to the crucial information needed to provide quotes to customers quickly, insurers can speed up their processes and reduce costs. Advanced analytics techniques have successfully digitized a large part of the underwriting process. Modern ML models can run hundreds of variables and analyze data from numerous internal and external sources, completing complex tasks such as pre-underwriting and prospect loss modeling, enabling insurers to offer more-competitive rates while minimizing risk. ML also enables a more custom-friendly application process with fewer questions to answer and a quote in minutes.
Small commercial insurance lines often require heavy involvement from insurance agents and underwriters, resulting in a burdensome and time-consuming process for moving small-business lines from application to issue. Advanced analytics is changing that. Using third-party data, companies can now quickly assess an applicant’s level of risk, moving the majority of applications to straight-through processing (STP). With only high-risk applications flagged for manual review, the need for manual underwriting is drastically reduced, creating a faster, more streamlined process for the agents and customers.
Midsize to large-scale commercial operations
Due to the complexity of the midsize to large-scale market, manual underwriters are still heavily involved in the process of evaluating their insurance applications. Although fully automating the processing of these types of policies has not yet been realized, some insurers are embedding data-driven insights into the application programs their underwriters use to review applications. Using data to augment the underwriter’s own discernment helps improve decision quality by highlighting a business’s potential exposure to risk that may have otherwise slipped by.
Insurance Analytics Powered by Snowflake
With the power of the Financial Services Data Cloud, insurers are leveraging more data to better assess risk, optimize pricing and policies, and launch new products with data science and ML-enabled automation. With Snowflake, you can automate routine tasks in underwriting workflows, monitor portfolios, and provide teams with easy access to additional data sources. With a single data platform for scaling multiple workloads, from risk assessment and advanced pricing to application triage and processing, you are empowered to enjoy the benefits that advanced insurance analytics can offer.
Watch this webinar to see how CARTO uses Snowflake to improve actuarial analysis and risk assessment workflows. You’ll also learn why location data intelligence gives insurance companies a competitive advantage in identifying risk.