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Insurers use machine learning to improve business efficiency
In recent years, the application of machine learning in the insurance industry has expanded significantly, as highlighted by DataRobot's research. Insurers are now leveraging these technologies across a wide range of areas such as claims forecasting, dynamic pricing, fraud detection, product recommendations, risk assessment, and even enhancing security and privacy measures. These applications are helping insurers gain a competitive edge through more accurate insights and improved decision-making.
George Argesanu, head of personal property management at AIG, emphasizes that senior leaders in the insurance sector must proactively address potential losses to allocate resources effectively. Machine learning, he notes, can uncover patterns and insights that traditional methods often miss, enabling more precise risk evaluations and better strategic planning.
For instance, Praedicat, an insurance tech company, collaborates with Allianz by integrating its predictive modeling tools with Allianz’s risk data to assess future liability risks. Their machine learning models analyze thousands of scientific publications to evaluate the litigation risks associated with products or substances throughout their lifecycle.
Insurance companies are also using machine learning to build more rational pricing models and make smarter pricing decisions. MetLife Auto & Home's UBI car insurance program tracks drivers' behavior through a mobile app, offering real-time feedback to encourage safer driving and lower premiums.
Shinft Technology, a startup, applied machine learning to analyze 13 million insurance claims for the European Insurance Industry Alliance, identifying over 3,000 cases of potential fraud—some involving large-scale, long-running schemes that had cost millions in losses.
Transamerica's Enterprise Marketing and Analytics Platform (EMAP) combines internal insurance, pension, and investment data with external sources like consumer income and social media information. It uses Cloudera’s Enterprise Data Hub to manage structured, semi-structured, and unstructured data efficiently.
Cytora, an Australian insurtech startup, utilizes AI and open-source data to help insurers reduce loss ratios, increase premium revenue, and improve cost efficiency. Its Risk Engine leverages both internal and external data to generate risk scores and predict expected claims across an insurer’s entire portfolio.
Aetna, a U.S.-based insurer, has introduced advanced security systems for mobile and web users. These systems go beyond traditional passwords and fingerprints, analyzing factors like device software, operating system versions, typing behavior, and location to detect user activity and verify account access securely.