What are the challenges facing the insurance industry currently? How does Spraoi help eliminate those shortcomings?
In the present-day insurance landscape, organizations house massive data sets that are being underutilized, owing to prevalent legacy technology. Insurance companies wishing to implement technology to mitigate these issues are often stalled by a disconnect between industry expertise and a very high entry point to experiment with new technologies. Machine Learning is a perfect example of one such technology. While machine learning can be a game changer for insurance companies, lack of familiarity with and understanding of the technology and its benefits limits the ability of many companies to implement an effective ML solution. Team Spraoi uses its domain knowledge to understand clients’ business objectives and makes Machine Learning more accessible through their proprietary infrastructure.
What is your solution implementation process and how long does it take to deploy?
Spraoi delivers Machine Learning solutions using a proven methodology. Using a five-step process, Spraoi makes it possible to create a continuous cycle of monitoring and delivery of machine learning predictions. The process starts with identifying the business problems and the questions that need answering, based on which relevant data gathered. The data then goes through a cleansing process to make it machine learning-ready. Following this, the right algorithms are identified to suit the client’s requirements and business models. Spraoi sets itself apart from peers in the market by helping clients operationalize the machine learning model to ensure that clients see a return from their investment.
With an implementation period ranging from 12 to 24 weeks depending on the complexity of the problem, Spraoi takes the client on a journey of machine learning from start to finish. In particular, clients are often impressed by Spraoi’s ability to put the solution into operation without significant disruption while ensuring that the solution is continually optimized for better performance.
In the present day insurance landscape, organizations house massive data sets that are being underutilized, owing to prevalent legacy technology
Kindly provide a case study to demonstrate how your company helps clients.
Spraoi strives to bring in a consumer-grade technology for insurance and this can be exemplified by our work. Aiming to counter fraud, our client was looking for ways to leverage Machine Learning with limited historical data availability. While our team realized that the client lacked rich data to build a machine learning model, we began the pre-processing of the data. We credit the client for their commitment to get on the Machine Learning journey. The outcome of our pre-processing helped the client understand patterns that were not operationally obvious. Later, using these finding patterns in the data, Spraoi deployed a rules-based system that came out of production in 90 days, as a first step in the Machine Learning journey. The data produced using the rules-based system was used to then build a machine learning model. In a period of 9-12 months, our client went from no historical data to leveraging machine learning to identify and predict fraud.
How has the journey been for Spraoi so far? What does the road ahead hold for Spraoi?
Over the last 20 months, Spraoi has engaged with major insurance carriers across the U.S. and built a development facility in Bangalore, India. While we have historically focused predominantly on claims and enrollments, we have plans to expand into customer-focused products through our core solutions. Additionally, Spraoi’s patent-pending infrastructure allows clients to scale their data science teams and continuously deliver Machine Learning models. The goal is to continue to expand our offerings in the market place and provide actionable insights to our clients using Machine Learning.