A lot of data is generated every second. Your bank details, your insurance details, and your food habits, etc are getting stored at every instance. These lots and lots of data are getting stored in the form of “Big Data Hadoop”. There are techniques to manipulate this Big Data. It is the ever-increasing amount of information generated and stored simultaneously every instance.
The principle of the Insurance industry is based on risk. The information generated from users is analyzed at some level for research and truth identification.
The Car Insurance sector
We know that newer technologies have made driving safer. Self-driving cars, newer models that come up with intuitive AI has uplifted driving to another level.
But data collected from 2018 has something different to say. Accidents have actually increased in the last 8 years.
While public servants and car companies are dedicated to making roadways safer, Insurance companies also have a role to play. They could make car ownership as cost-effective as possible.
ZhongAn, a Chinese firm has initiated a method by which car insurance can be bought online using complex analytical skills and ML.
According to a statement made by Zhong Hu, the online purchase system has been broken into 45 parts. If a user spends too much time in a particular part then that part needs to re-evaluate.
The Ultimate Fraud Detection
Fraud accounts comprise about 5-10% of claim costs. More than half of business leaders say that fraud accounts could increase vehemently in the near future.
Fraud accounts cost billions of rupees annually.
Using machine learning for predictive modeling and strict data management can help the company to save their money. Risk is much lower in cases where Big Data is involved.
The machine is trained with a variable data set and based on it the computer automatically predicts the outcome for another given data set. While it is surprising but this is where fraudulent activities can go wrong. Irrelevant data sets could be identified using mathematical modeling and hence fraudsters can be caught individually.
Nowadays major Indian insurance companies are using AI to fight fraud. ICICI Lombard General Insurance uses ML methods to analyze Big Data to provide instant health insurance claim approval.
Could Big Data Hadoop give faster settlement of claims?
Big data could mean more accurate loss measurement.
In case of a car accident, comparing large data of a healthy car and that of a damaged one is useful for predicting the correct outcome. For example,
- Analysis of heat rates for fuel burn for the identification of turbine efficiency could predict a catastrophic event in the future.
- Count the number of hydrocarbons flared during the aftermath of an accident.
- Collecting temperature data to resolve issues of burning to predict whether the accident was pre-planned or an accident itself.
Big data Hadoop for insurance has always meant faster payouts with quick verification. This reduces the workload and helps the company to prosper in the market.
Analyzing claims and claim histories to modify payout systems. It leads to more customer satisfaction and lowers costs.
The problem faced by insurance underwriters
Providing recommended policies that are user-friendly with the customer is a challenge for an underwriter. With Big Data Hadoop, they can adapt to data analyst roles as well as underwriters. 4 out of 5 underwriters see that accessing real-time claims can improve pricing accuracy. Providing accurate and current insights could have taken days for consolidation.
Big Data is useful for an underwriter.
Is there a disadvantage to this Big data Hadoop?
The increasing amount of IoT connectivity and a large amount of data is staggering to the economy. There is always the potential for something to go wrong if Big Data is involved. There have been cases where a data breach has caused calamitous damage to a company as well as users.
Security servers need to be set up with every company consulting with Big Data. There should be forensic type accountants who should know how to analyze and purify the data for a user-friendly assessment that could be helpful for scrutiny.
Big Data Hadoop has proved useful for every sector and the way the insurance industry is using it is viable that claims could be settled faster and frauds can be exterminated. Underwriters could be trained for the role of data analysts and it is the quite easy and fastest way to adapt. Complex problems could be solved using ML in Big Data. Hence it is evident that our emerging economy will grasp this technology sooner to ease difficult processes in claims.
Why Data Visualization Is a Central Element of Effective Analytics?
Pitching your idea with data visuals is the method any business seeking to sell their stories and product to multiple crowds should utilize. It is an integral part of any business transaction and would help the business owner convey their message to their audience. This method of portraying data is what makes it possible to cause any positive change in business.
Read more about Data Visualization here.