Insurance industry beginning to take advantage of data science
Data is everywhere. While we consume a lot of data every day, we also give away loads of information to the internet during our interaction, no matter how small a time frame. And while we see active use of data science and related technology in fields such as targeted marketing, personalization of technology, customer support, and more, it is now slowly reaching the insurance industry.
The insurance industry runs on statistics and logic as it involves risk management, competition, and event prediction daily. With the integration of big data, IoT, and data science, things are taking a turn for the better, for the customers and the companies.
Better Risk Assessment
The power of AI and data science is now used to predict risks and outcomes in the industry to cut as much gross loss as possible. The prediction, or assessment, of risk is really just identifying the type and reasons of risk to help the industry avoid them with as big a margin as possible. This is all done with the help of data analytics.
The information about the customer (could be a person, a group of people, or an entire company) is acquired and fed into a model. The model, designed using algorithms that combine and understand data, then assesses the risk’s nature, type, and result in a given objective statement. For evaluation, the risk is cast as a result fit for the audience’s consumption, such as a visually descriptive graph, table, etc., thus, ensuring the customer’s profitability.
Reliable Fraud Detection
While an increase in profitability sounds like the ultimate requirement for data science in the insurance industry, something a bit more concerning requires expert attention.
Insurance frauds are quite the talk of the town everywhere, all the time. Not only does it bring a great loss to individuals, but it also brings financial loss to the insurance companies. The leading causes of fraudulent activities are suspicious links, malware, phishing, etc. Thankfully, data science platforms have made it easier for everyone to detect and fight these using various techniques, often AI-driven.
The insurance companies constantly run their history of data into statistical models using algorithms designed to detect fraud. These are conditioned using previous cases of fraudulent activities to understand the same. Using the newly gained intelligence, the algorithm analyzes the ongoing stream of data to filter out even the most subtle instance of fraud that might have gone unnoticed otherwise.
Advanced Health Insurance
But the integration of data science into the insurance industry isn’t an isolated event of cross-industry advancement. With data analysis techniques, we often see IoT tunneling into the health and finance sector.
We live in an age and world that connects us to a vast network through multiple channels. Health insurance companies know that the best. They seek out and collect data from wearable body sensors, such as smartwatches and phones; transactions such as payments made at fast-food joints; data from exercise monitoring systems at gyms; social media content posted by individuals to evaluate their mental health. This endless sea of data we all provide to insurance companies falls into the algorithm that studies us, our behavior, and our history.
This acquired intelligence is used to then provide us with personalized, structured healthcare plans, services, etc., by the companies.
Automation In Automobile Insurance
If you can use data science to learn more about better healthcare schemes, can you do the same for automobiles? Yes, we can.
Risk assessment is as big a part of the automobile industry as any other. Data about various automobiles runs through a centralized database constantly to help customers learn what they need from their insurance company to cover them with the least amount of risk and maximum benefits.
Thankfully, platforms like Salty do the trick. They seamlessly simplify AI for you, understand you and your needs, and provide you with a customized plan to help you stay insured always.
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