Insurance companies work on extremely thin margin and a competitive space that using data and analytics has become a key strategy to reduce costs, leakages, improve operational efficiencies and gain competitive advantages. Optimization in every aspect of processes is the driving force for insurance companies.
Some of the usecases that Insurance companies address using BigData, Analytics and ML are
Real-time monitoring and visualization of customer behavior is fundamentally changing the relationship of insurers and the insured. Instead of being reactive and adding more to the costs, Insurance companies are trying to intervene proactively to reduce the number of claims.
For example, in auto insurance, telematics is being used to monitor in real time the driving habits of the insured and use that data to better driving habits. There are examples of insurers who used telematics, seeing a 30 percent reduction in the number of claims.
Insurance providers in need of differentiating themselves and reducing costs, see a lot of value in streamlining and automation of claims processing and payment.
One of the use case that BIRD developed is the automation of claims processing. BigData and ML comes in handy to identify the patterns that lead to approval or rejection of claims. This automation not only reduces the number of resources working on claims processing but also improves customer satisfaction by giving an immediate response.
While every company wants to mitigate Fraudulent claims, it gets too expensive and inefficient to investigate every claim. Also, preventing fraud before the claim is processed is most important for the insurers.
There are multiple ways one can use BIRD to address Fraud mitigation. Companies can use their existing rules of customer behavior, rate cards and priorities to alert the departments in real-time. This enables the claim team to immediately look into the claim and avert any fraudulent activity.
Insurers can also use advanced ML algorithms to further improve fraud detection by automatically extracting deep patterns on Big Data coming from different sources, past labeled data, text data, images etc.