Aggregation Engine
Aggregation Engine for an Online Insurance Aggregator
Industry
Insurtech
Company Size
1000+
Duration
20+ Months
Aggregation Engine for an Online Insurance Aggregator
Client has an insurtech platform that is an aggregator of insurance policies from different insurance providers. The platform offers the comparison of four wheeler insurance, two wheeler insurance, medical insurance and term insurance quotes from 30+ different insurance providers.
Being one of the leading online insurance aggregator providers in India, the client has processed 150 million claims, bought 1 million+ policies and compared over 30+ million quotes.
What were the challenges?
The product had to get quotes for various products like Four Wheeler insurance, Two Wheeler insurance, Medical insurance and Term insurance in real time from multiple insurance providers, and present an aggregated result to the user.
A quote for a single user would mean the engine has to get 30+ quotes from different insurance providers.
The underlying problem was scalability, as the system could quickly run out of resources, if too many users requested the quotes at the same time.
Proposed Solution
How did we solve it?
We helped the client build the aggregation engine with the help of a purely asynchronous IO model. This allowed one thread to cater to many requests compared to conventional one thread to one request model, allowing the engine to scale to hundreds of users on a single machine. Also, the system was designed on Shared Nothing Architecture which allowed to horizontally scale the system without any limits. We fixed the scalability issue to improve overall user experience and response rate.
How did the client benefit?
Time to market was reduced since the core framework with the shared nothing architecture was built by us rapidly.
While we worked on the scalability issue, the client was quickly able to focus on developing actual integrations with insurance company services.
Client could also go live with a few integrations within a month’s time due to abstraction the core engine provided to integration developers.
By sticking to Java, the client was able to utilize it’s existing resources which were predominantly Java Developers.
With the scalable architecture implemented load on 1 machine could handle loads of 4-5 machines, which saved high infrastructure cost.
Due to improved response rate of the requested quote, client could provide more insurance quotes at the same time.
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