奥纬咨询-预测分析的新前沿

© Oliver Wyman2A New Frontier In Predictive AnalyticsOver 97% of businesses worldwide have invested in big data. However, only 24% of these companies claimed they use the collected data to analyze and make informed decisions.1 Data management is an integral part of running a business, for year-end reporting and tax purposes, and to comply with laws and regulations.Today the insurance industry is experiencing a fundamental shift in how to define, understand, and quantify risk. Recent technological advancements have led to an explosion of data, which demands new processing and analysis techniques beyond traditional methods to make sense of it all. Consequently, insurers face a new challenge: finding a balance between developing highly accurate models and complying with business and regulatory requirements.Unconstrained models — those with few limitations — maximize data utility and predictive power by leveraging advanced algorithms. These models allow for flexibility, have the ability to capture complex relationships, and offer broad applicability for domains where data may contain deep interdependencies and nuance.When used strategically, unconstrained models can analyze and enhance traditional models to unlock new insights, even in highly constrained or regulated environments like insurance. For organizations that embrace them, unconstrained models present an opportunity to improve risk management and gain a competitive advantage.In this paper, we examine the latest advancements in the insurance analytics landscape. We discuss how unconstrained models can strategically complement traditional models, review modeling constraints, and highlight the importance of strong model governance.1 Kumar, Naveen. “Big Data Statistics 2025 (Growth & Market Data).” DemandSage. June 24, 2025.THE INSURANCE ANALYTICS LANDSCAPE© Oliver Wyman3A New Frontier In Predictive AnalyticsTHE INSURANCE ANALYTICS LANDSCAPEUNLOCK NEW DATA SOURCES FOR MORE ACCURATE RISK MODELINGThe past decade has seen a data revolution characterized by the emergence of new data types as well as increased volume and velocity. Today, smartphones continuously transmit telemetry data for various applications, vehicles provide diagnostics and receive over-the-air updates, and smart refrigerators are poised to display advertisements. As a result, the volume and growth of data have skyrocketed. Data created, captured, copied, and consumed is expected to surpass 180 zettabytes in 2025, as shown in Exhibit 1. For context, Exhibit 2 shows that one zettabyte (1 trillion gigabytes) is roughly equivalent to 1 million copies of the entire Netflix catalog — underscoring that 180 zettabytes is an enormous amount of data. Most of this data is unstructured, meaning it does not have a predefined format and requires the application of techniques like natural language processing (NLP) and large language models (LLMs) to extract value.Exhibit 1: Data volume and velocity, 2015-2025500Actual100150200Data volume (zett