Customers are switching to insurance providers who can provide the fastest claims processing, best pricing, and most responsive customer support. Executive, product, and engineering teams at insurance companies can achieve this using Machine Learning, but face several hurdles:
Insurance providers are leveraging Artificial Intelligence and Machine Learning (AI/ML) to automate painful and costly manual workflows. Skymind is a platform that helps product and engineering teams ship those ML capabilities into enterprise applications faster.
Empower engineering teams to use the language to build ML functionality for Java applications in enterprise environments, using the language and environments they already know.
Eliminate dependencies and interoperability issues between data science and engineering teams. Get enterprise-grade model reliability, performance, and scaling without maintenance, all on the JVM stack.
Build ML-powered capabilities into existing insurance workflows using streaming or stored data from ERP and CRM systems, data lakes, Spark, Kafka, and elsewhere. Improve model performance over time with quality monitoring, A/B testing, and winner selection.
Engineers can integrate and deploy Python-based models developed by data science research teams into JVM environments and applications, or build their own models in a tracked and collaborative environment.
According to Forbes, claims processing is a notoriously laborious process. Along with the manual data entry of printed forms, the claims process tends to miss cases of fraud which result in over $40 billion dollars in losses per year. The sheer volume of claims makes it impossible for human analysts to properly vet each case in a timely manner.
On top of claims processing, insurers face competitive pressures to improve the customer experience. For example, underwriting is still largely done manually over the phone. Insurance advisory requires a limited supply of human experts to recommend plans that best fit a customer’s unique circumstances. Customer acquisition and support require call centers which can have long customer wait times. All of the above create negative customer experiences.
Machine Learning is a powerful tool that insurers can use to automate manual tasks, increase the efficiency of human analysts, and improve the customer experience.
Common applications of Machine Learning for insurance companies include: