Impact of AI on Insurance Industry:

What are the primary use cases of AI in insurance?

Claims processing

Claims processing includes multiple tasks, including review, investigation, adjustment, remittance, or denial. While performing these tasks, numerous issues might occur:

• Manual/inconsistent processing: Many claims processing tasks require human interaction that is prone to errors.
• Varying data formats: Customers send data in different formats to make claims.
• Changing regulation: Businesses need to accord to changing regulations promptly. Thus, constant staff training and process update are required for these companies.

Appeals processing

After claims are processed, some claims can result in appeals that can be automated with the combination of AI and automation technologies.

Work Fusion claims that they can automate 89% of appeals processing with a 99% accuracy rate, as seen in the below image. Of course, these ratios need to be taken with a grain of salt as they would change based on the complexity of appeals and vendors tend to be selective in picking their case study figures.

Application processing

Application processing requires extracting information from a high volume of documents. While performing this task manually can take too long and is prone to errors, document technologies enable insurance companies to automatically extract relevant data from application documents and accelerate insurance application processes with fewer errors and improved customer satisfaction.

Insurance pricing

AI can assess customers’ risk profiles based on lab testing, biometric data, claims data, patient-generated health data, and identify the optimal prices to quote with the right insurance plan. This would decrease the workflow in business operations and reduce costs while improving customer satisfaction.

Document creation

Insurance companies need to generate high volumes of documents, including specific information about the insurer. While creating these documents manually consumes time and is prone to errors, using AI and automation technologies can generate policy statements without mistakes. 

Claim fraud detection

29% of insurers have admitted to lying to their car insurance company to gain coverage in the US. AI-powered predictive analytics and text analysis tools might detect fraudulent claims based on business rules with data captured from the claimant’s story.

Top 10 Use Cases for AI in Insurance:

1. Insurance writing and pricing

The correct pricing of insurance products is vital to managing risk and the ultimate profitability of insurance companies. Traditionally, pricing and premiums are determined according to statistical modeling approaches, such as generalized linear models.

As we are seeing a rapid increase in the availability of customer data, large benefits can be had by applying machine learning techniques to the data of individual customers. In fact, machine learning becomes necessary to be able to detect patterns in large volumes of data as these approaches are able to exploit non-linear relationships in datasets with many features.

2. Claims Reserve Optimization

Insurers need to set aside claims reserves to pay policyholders who have filed or are expected to file legitimate claims on their policies.

In some cases, such as permanent health insurance, settlement occurs over a large number of months or years, presenting an ongoing liability for potentially long-lasting claims. Reserves are set aside, typically on a conservative basis, to account for future liability.

3. Claim Fraud Detection and Prevention

Claim fraud is an unfortunate reality that costs the insurance industry (and ultimately consumers through increased premiums) billions of rands each year. In South Africa, the Insurance Crime Bureau estimates that up to 20% of the R35 billion paid in short-term insurance claims could have been fraudulent in 2019. In the United States, non-health insurance fraud costs an estimated $40 billion per year.

Machine learning algorithms are ideally suited for fraud detection and can identify claims that should be subjected to further investigation.

4. Accelerated Claims Processing 

The insurance industry has been around for quite some time with many insurers having existed for decades and even centuries. Unfortunately, some are slow to adopt new technologies and are hampered by bureaucratic and outdated processes.

Many companies still require slow, time-consuming paperwork to fill out claims and are lagging behind in digitalization.

5. Automated Inspection 

The use of automated inspection is becoming more prevalent to validate underwriting and claim decisions. This may be during pre-cover, post-inception, or renewal.

Deep learning applied to geospatial imagery provides risk and value information related to properties. In agricultural insurance, drones and unmanned aerial vehicles are being utilized to access the damage to crops, allowing accurate assessment of the extent of the damage.

6. Damage and Repair Cost Estimation

Closely coupled with automated inspection, is the automated estimation of damage and repair costs.

After an inspection a report can automatically be generated, containing a list of damages and an estimated repair cost. Such an approach standardizes damage and cost allocation, as current approaches based on the use of human assessors could vary greatly.

7. Efficient Customer Support 

The application of machine learning to customer support can drastically reduce the amount of Labour intensive effort, which saves time and reduces costs significantly.

8. Personalized Recommendation Systems

Customers have different needs, preferences, and lifestyles. They expect personalized policies, loyalty programs, and recommendations, based on their individual preferences and attributes.

In health insurance, wearable devices are now being used to track health vitals, with automated reports being generated to provide healthy living advice and risk reports.

9. Marketing and Propensity Analysis

In marketing, propensity analysis allows the use of data to predict whether a person will take a particular action, for example, make a purchase or accept an offer.

Propensity analysis may be used to find the most likely candidates for a new product or offering, allowing marketing and sales efforts to be directed efficiently.

10. Churn Prediction

The acquisition cost of new customers is substantially higher in the insurance industry than in many other sectors. It is much less expensive to keep an existing customer than to acquire a new one.

Insurance companies are utilizing churn prediction to predict when customers may churn, enabling them to take proactive measures to keep their clients.

There are 27 AI in Insurance startups in India. Here is a list of the 10 most exciting ones:

Vymo

Provides virtual assistant for sales representatives.

Vymo provides a virtual assistant for sales representatives. It also offers customer resource management software for field sales teams. The product is a cloud-based field sales management software with modules such as lead management, relationship management, service management, etc.

The product features include AI-based solutions, geo-tracking, activity detection, sales metrics, sales analytics reports, etc. Notable clients include SBI Life, HDFC Bank, Bajaj Alliance, Yes Bank, Varthana, Treebo, Swiggy, etc.

Gradatim

Software solutions for the banking & insurance industry.

Gradatim offers software solutions for the banking and insurance industry. The company offers an end-to-end digital banking system that facilitates secure interaction between customers and banks; between employees and the lines of business (LOB); between the lines of business (LOB) and departments.

It can be used for retail banking, SME loans, gold loans, and microfinance. Also offers an end-to-end digital insurance solution that can be used for life insurance, non-life insurance, deposit insurance and social schemes.

Aureus

Big data analytics products for the BFSI sector.

Aureus provides big data analytics products for the BFSI sector. It provides predictive analytics and big data-ready platforms for insurance companies and banks. It allows users to create, publish and execute analytical models.

The features of the product include fraud analytics, detection, and prevention, AI-based behavior prediction, API integrations, real-time consumer insights, etc.

Clinikk