We have already witnessed the application of artificial intelligence in every sector, whether the industry is BFSI, medical or agriculture. Talking about the insurance sector, artificial intelligence is deeply integrated into it. Most sensitive use cases in the insurance domain like claims, distribution, and underwriting can be resolved using AI systems. According to the FBI’s report, the insurance industry is filled with more than 7000 insurance companies, and the collection of this industry goes more than $1 trillion annually. These statistics tell how big this industry is and survival in this industry requires highly advanced and competitive behaviour from companies. Here AI comes into the picture and ensures the company is performing well in every aspect. In this article, we will look at the significant use-cases of AI in the insurance sector, and the major advancements that AI can provide in Insurance Domain to be discussed are listed below.
Table of Content
- Faster Claims Processing
- Accelerated claim adjudication
- Document Digitization
- Accurate Underwriting Risk Management
- Insurance Fraud Detection and Prevention
- Customer Services
Faster Claims Processing
Artificial intelligence-enabled products are best known as an option for the human workforce where repetitive and attention-demanding tasks are required to be completed. Such products perform such tasks faster and efficiently and drive the best ROI.
For example, manual claim processings are prone to inefficiencies and errors because of extensive paperwork and digitised processing. On the other hand, a trained AI model becomes efficient and prone to a lesser error rate. According to a report by McKinsey&co, it is found that manual claim management can increase the premium revenue by up to 50–60%.
The significant changes in insurance companies are found in the year 2021 when they plan to achieve better operational efficiency using the technologies such as:
- AI
- RPA
- IoT
With the emergence of the above-given advanced technologies in the insurance sector, RPA and IoT have become two significant points for data collection. Telematics and computers enabled in a car, fitness trackers, and healthcare devices are IoT devices that help in the generation of comprehensive data of customers that makes decision-making easy.
At the same time, AI comes into the picture, which is an advanced means to process extensive and comprehensive data. As the data volume increases, AI models’ capacity also increases and the chances of errors decreases.
AI advances the claim settlement procedures by streamlining the processing of incoming data. Data processing in claim settlement can include scanning, interpretation and decision-making. AI models have proven themselves best in such workflow, and as the data volume increases, they also improve themselves without requiring explicit programming and human intervention.
The intervention of AI can make claim processing advanced in the following use-cases:
- Claim routing
- Claim sorting
- Fraudulent detection
- Claim management audit
Fukoku mutual life is leveraging the AI models for the claim processing. The AI-enabled application can access all customers’ medical files and mine information out of them to calculate optimised payouts. After this calculation, the human agents are required to provide approvals.
Accelerated claim adjudication
Claim adjudication is a process where insurance companies decide between yes or no, and how much a claim needs to be settled for any case. However, this all process goes through a cyclic plan whether the insurer or customer wants to execute the cycle faster.
Using AI models, this planning and execution become easier and much faster than manual processing. Many AI models have already been developed, and many are in the development process to perform different inspection tasks. They can be used for car, property, and human inspections during medical claims.
AI intervention in claim adjudication is a compulsion to perform such inspection quickly, accurately, and honestly. Hardware and models involved in AI systems work for data collection and verification so that process of evidence gathering and appraisal sessions can be completed much faster and safer.
For example, an AI-enabled system can use a camera to take pictures of objects, and a computer vision model can optimise these pictures to assess damages more efficiently and provide an estimated repair cost. These models can be utilised with drones to perform building damage, crop damage and industrial equipment damage inspection.
AI models just require data to inspect and analyse such things, which can be collected using equipment like cameras and IoT devices. After the models and systems are verified, reports can be sent to an engaged inspector.
Auto-insurer Tokio Marine is a company that has deployed such systems for examining and appraising damaged vehicles. After verifying them with the AI model, they just collect images and process the settlement.
Document Digitization
This is not only a use case for the insurance domain but also for domains like banking, education and finance. This use case requires optical character recognising systems to extract information from documents and pictures. This extracted information then gets collected and optimised for decision-making.
It is majorly found that insurers are collecting the data in the form of paper like paper-based form, printed documents, and ID cards. At the top levels, it becomes a prominent issue in the management of these papers. In this scenario, OCR can be the game changer and provide high operational efficiencies. Also, OCR can reduce efforts of re-typing information and human mistakes.
This not only enhances the accuracy of work but also allows the human workforce to engage in productive tasks. Furthermore, the management issues with papers are resolved because OCR can extract information, and after translating them into digital form, it goes into the databases in a managed way.
In one of our articles, we have seen that such processes enhance the new customer onboarding and the KYC processes. It only takes a very low to record all the mandatory information of humans from the documents using the AI systems.
According to a report published by EY Insurance Industry Outlook 2021:
- 69% of existing users want to buy insurance online.
- 58% of users consider the online process of buying life insurance, and
- 61% of users consider the online process of buying health insurance.
AXA CZ/SK is one of the significant examples which uses deep learning models to improve the data ecosystems and efficiency and reduce the cost of human labour.
Accurate Underwriting Risk Management
In the insurance sector, underwriting stands for risk evaluation of insuring people or assets. When someone is applying for insurance, underwriters need to make different risk analysis strategies and policies precisely. These processes become very complex for a human to perform.
As we know, AI models can be trained to perform more accurate decision-making when billions of data points are collected and fed to them. In addition, these systems can assess insurance applications against these training data points. They may also help the underwriters to get more clarity on relevant risk factors associated with a customer profile.
Again a large amount of paperwork is required for underwriting that can be reduced using computer vision models with IoT devices. This way, we can record assets more carefully and faster or in real-time. This can also be utilised for eliminating in-person inspections over time.
For example, by connecting a GIS data stream to analysis systems, we can enhance over-time inspection of properties while no in-person meetings are required. Also, we can adjust policies accordingly.
All collected data can also be utilised in the following predictive analysis:
- levels of degradation,
- automatic defect inspections,
- predict potential failure rates
ICICI Lombard is one such application that helps in the cashless claims settlement process and insurance underwriting.
Insurance Fraud Detection and Prevention
According to a report on the official website of the united states government, the total cost of fraudulent activities in the insurance sector is more than $40 billion per year. And as we know, more than 7000 companies are serving insurance facilities in the USA. this amount of fraudulent activities can be compared to the plague.
Looking at the figures one can estimate, it becomes necessary to apply such systems to the traditional techniques that can detect and prevent fraudulent activities. Nowadays, AI systems are developed to complete this necessity and augment human analysts’ judgement by providing them with highly optimised information.
Since machine learning models can identify and optimise patterns from the information, they can be considered strong contenders for extracting out-of-the-ordinary behaviours.
AI systems specially developed for fraud detection help in a quick and automatic background check of the customers either in the onboarding stages or in claim processing. They can also help estimate associated risk with any person or their assets.
AXA is one of those examples of insurance companies that applied AI-enabled applications to prevent fraudulent activities. The applied software can detect distinct patterns based on computer behaviour and employee network.
Customer Services
Nowadays, AI has become one of the most valuable tools for representing a company’s competitive nature in the industry. Of course, that directly belongs to the marketing purposes, and for this also, AI can be utilised in any domain, not only in the insurance domain.
Since the insurance domain is the top data holding domain, AI for customer services and marketing in the insurance domain can work better than in the other domain. AI is a crucial player in customer onboarding in the BFIS sectors. As AI has the power to optimise data, it can be used to predict more competitive prices or offers to enhance business models as per consumer demands.
The only need to apply for the AI program here is to understand the trends with customers; for this, we may require to streamline the data collection and analysis on different channels. The outcome insights can be used for customer onboarding and product and policy designing.
ZhongAn is a Chinese insurance company which utilises AI models and analytics to introduce innovative products and policies. For example, one of their policy insures against cracked mobile screens and shipping return products.
Final words
One thing which has been optimised in many reports is that COVID-19 has put this industry under pressure. AI has managed to hold the industry’s potential by improving operational efficiency, cost management and decision-making accuracy. Also, as the size of this industry is increasing, it won’t be surprising to see AI resolving many more use-cases of it. We have seen how this industry uses AI from enhancing operational efficiencies to increasing customer satisfaction.