The market size of the insurance analytics has shown the inclination from 2017 to 2023 where it was 6.06 Billion USD in 2017 at the compound growth rate(CAGR) of 12.5% during the forecast period. The proliferating adoption of data-driven decision-making and advanced analytics techniques are driving the growth of the insurance analytics market. However, data quality, security problems, and the lack of a technical workforce are the factors hindering the market of Insurance Analytics. For instance, if an insurance company knows about the customer’s future travel plans, with the help of Insurance Analytics, it can create a personalized travel package to handle all the travel expenses.
Insurance is the asset which common man buys too for the important things to buy the house, car, life, etc. The global industry of Insurance is almost $5 Trillion. But, as the Insurance industry is growing rapidly, it also faces the complexities such as fraud, high costs, and pricing. Therefore, machine learning and advanced analytics are offering the solutions for the above challenges.
Key factors driving machine learning in insurance:
- Open Source for transparency
- Using the IoT (Internet of Things) in the data
- Automate the applications
- Ability to solve the queries
Importance of Data in Insurance :
Insurance is the customer-centric industry where the importance of data is crucial considerations like insurance policies, customer expectations, investments, etc. Most of the Insurance companies are processing just 10-15% of data most of which is structured data they store in the in-house- databases. That means they are just overlooking the valuable insights from the data hidden in the unstructured data. To mitigate this problem, the insurance companies are using machine learning to analyze & identify the unstructured data which is used to drive better results. And one major advantage of machine learning is that it applies effectively to structured & unstructured datasets. It can be used to analyze the customer behavior, risk, and accuracy of the claims.
AI-enabled technologies used in the Insurance field:
The insurance fraud has escalated in the insurance industry with an estimated total cost of $40 Billion. Different types of atrocities have also increased in the Insurance Industry however most of the time, the purpose is to running away with the money without paying. But the question arises that the insurance agent could also investigate each case of the insurance taker, identifying if it’s genuine or fraudulent. However, this process is time-consuming and costly. The most efficient strategy is the Advanced Computerized system which are been used for different purposes, like Claim processing, Insurance advice, Fraud Prevention, and mitigation of the risks.
1.) Social Network Analysis(SNA):
As per the Wikipedia,
SNA(Social network analysis) is the procedure of investigating the structures of society through the usage of networks & graphs. It is explained in terms of nodes(people, actors & things in the network ) and the links, edges, and ties(interactions & relationships) that will connect them.
SNA allows the company to analyze a large amount of data to show the relationships via the links and nodes. The SNA tool uses the hybrid approach which includes statistical approaches, pattern analysis, network linkage analysis, and organizational business rules. When one looks for fraud in link analysis, one looks for the clusters and how those clusters are linked to them. Public records such as judgments, criminal records, address change frequency & Bankruptcies can be integrated into the model.
2.) Predictive Analytics for the Big data:
The voracious use of Big data has proliferated in Artificial Intelligence and IoT, which provides an endless stream of data from connected sensors that spot trends & access the behavioral patterns of consumers, employees, & devices. ML algorithm spot the patterns to develop & working the real-time models.
Consider the scenario where the car was torched and the valuable items are removed from the car already, so the story narrated by him was just delusional or fake. Predictive analytics includes the use of text analysis & sentiment analysis to look into fraud detection.
Before the claim is put to action, the insurance company makes him write the long report of what happened exactly, then the claim report is scanned easily across multiple pages and text analytics detect the texts of the report. Mainly clues are hidden in the report where some are susceptible through an insurance agent and some questioned are emerged by the technology.
3.) Paperwork to Digitalization:
The insurance industry deals with piles of paperwork where the mediocre storage repositories and files are used to store the documents of the customers who have taken the insurance. Thus, replacing them with digitalization is very important to fast the flow of the issuing's and claims process. Streamlining the process in terms of the minimal human intervention to machine automation. Machine learning is deploying smart reader technology and text analysis to scan the documents. It also uses the OCR(Optical character recognition) to read data & RPA(Robotic Process Automation) to read & extract relevant data from the scanned documents.
4.) Chatbots for Customer Services:
Machine learning plays a vital role in communicating with the customers when it comes to communicating 24/7 and accurately without any hassle of waiting in the line of the telephone. Therefore, the insurance sector is opting for chatbots to send the personalized solutions to customers to review the background of their profiles and recommend tailor-made products. Therefore, the chatbots are available at the forefront to solve the queries.
AI-integrated Chatbots | Companies Should Consider the following features before Choosing the Chatbots
Use-cases of the Insurance Industry using the Analytics Model:
There are many use cases where images are used to analyze, for instance, a car has met with an accident and the bonnet of your car get damaged and you have insured your car, so next, you will contact your insurance agent to claim your insurance. The agent manually takes a picture, submits it to the insurance company, and manually does the paperwork and it would take almost a few days to execute whereas, if it would be automated, the time will be less spend and also it would be reliable & trusted. Let’s dive into some of the examples:
1.) Using Drones for accessing the Damaged Houses:
For suppose your roof of the building is damaged and the insurance agent has to manually examine the damage. When the roof isn't high-risk, it is not that risky but if the roof is a high-rise, it’s very dangerous to examine it. According to the survey of the US, the property adjusters have 78% injuries per million site working hours which are four-time more than injuries of the construction workers. Thus, to avoid these hazardous situations, a drone should be employed to examine these cases. It's safe and reliable.
2.) Automated NLP for Account Onboarding:
Customer onboarding is a time-taking process that is done manual paperwork process. It takes days to complete to process and complete the insurance. It's reported to take around 10 Days manually and on top of that, there are reported ample errors from the customers while evaluating the documents by the customer which hinders the growth of the insurance industry. To overcome this issue, the automated machines are put into the job for scanning the documents, insurance forms, and applications with speed and efficiency. Therefore, the data would be evaluated and extracted and mundane tasks would be automated in the insurance process.
3.) Object recognition for scanning the documents:
Most of the work comes when daily updates about the insurance are updated to the customers and this update happens on a regular basis due to the enhancements in the law procedures and government laws for the insurance. Therefore, to ensure that the information is processed accurately, the documentation should be error-free. To get error-free results, a manual process is difficult so here the digitalization is important. So here, Object recognition technology comes into the picture. It is used to scan the documents to read and extract the information, which mitigates the manual process. For instance, the OCR (Optical Character Recognition) converts the scanned documents or images of the text into computer-readable documents. When the OCR used Object recognition, it would effectively understand each and every pixel. This allows the data to be compared with the database
Object Recognition | Scanning Applications are mitigating the mediocre methods of gaining Information and provides different Count Features used in Distinctive Fields & Companies
The insurance companies are alleviating the issues like frauds, scams, error in the document and images by initiating the Advanced technology like Artificial Intelligence & Machine learning in the functionalities of the Insurance like using drones for accessing the houses, NLP for Account Onboarding and Object recognition for scanning the documents.