Insurance fraud and delays in claims processing costs the Insurance industry billions of dollars each year. Can MLOps solve this challenge? In this session, we’ll dive into how machine learning operations can help insurers create a faster, more accurate, more cost-effective and less risky process for their DS teams and their customers.

Leading Insurers in 2023 understand the benefits of ML and are investing heavily in Data Science and Machine Learning teams. However, there remains the key challenge of getting a return on this costly investment in the next generation of technology. MLOps (Machine learning operations) has emerged as a powerful approach to help automate and streamline the deployment and management of machine learning models.

In our practical session focused on solving real-life challenges experienced by our customers in the Insurance industry, we’ll dive into how to deliver practical use cases and ROI. From fraud detection to claims processing and from customer experience to risk assessments, Seldon has helped teams across Insurance organisations achieve their goals at scale. We’ll cover the full production ML lifecycle by deploying, managing, monitoring and explaining a claims processing model.

Why use Seldon in the Insurance industry ?

• Faster claims processing: Automate and accelerate the process and use explainers to surface model decision-making for both internal compliance and customer value
• Improved fraud detection: Batch processing can efficiently detect and prevent fraud by analysing large volumes of data with minimal team resources to identify patterns and anomalies that can save organsations millions
• More accurate risk assessment: Using data science monitoring tools, visualizations, and alerts, organisations can analyze far wider sets of data to assess risk, from social media activity to information recording from smart homes and autonomous vehicles
• Better customer experience: automation can speed up routine checks as well as create a more personalised and transparent customer interaction
• Cost savings: reduce costs through the automation of manual tasks, improving accuracy and reducing the need for human intervention and compliance challenges