1. simplified claims processing
Intelligent automation generates the best return on investment for attention-demanding, standardized, and repetitive workflows. claims management is a great example of this.
This is why:
Largely paper-based and rarely digitized from start to finish, the claims management process can absorb up to 50-80% of premium revenue.
Being primarily manual, claims processing is also prone to errors and inefficiencies, further increasing insurers’ operating costs.
As Mckinsey said in early 2019, the larger insurance companies have not fully addressed the costs of providing services:
Honestly, in 2021 many insurance companies set plans to achieve greater operational efficiency with the help of emerging technologies including:
- ai (machine learning and deep learning)
- rpa (robotic process automation)
- iot (internet of things).
- initial claims routing
- claims triage
- detection of fraudulent claims
- complaint management audit
- geospatial data (gis) data, collected by satellites
- high definition images or videos, recorded with a drone
- iot datasets including temperature, pressure, object position and more
- 69% of customers now prefer to shop for car insurance online
- 61% would also like to buy health insurance online
- 58% consider buying life insurance online
- model a potential market with 83% accuracy
- reduce processing time in subscription by 10 times
- improved case acceptance by 25%
- Predictive cost analytics for claims: Leverage machine learning techniques and data science to estimate the average cost of claims by different customer segments. adjust premiums accordingly and better manage your cash flow.
- driver performance monitoring: by analyzing behavioral data from connected car systems (internal and external cameras, telematics and ADA systems), you can gain more insight into the behavior of individual drivers. and delight them with personalized rates and product lines.
- real-time crash support. Insurance companies can provide superior levels of service to drivers by receiving automatic access to accident data and providing rapid semi-automated response. for example, an ai chatbot can direct the driver to the best actions for recovery, automatically notify the medical team if needed, or call a two-truck service.
- personal injury liability claim rate by 4-25%,
- the rate of civil liability claim for property damage caused by traffic accidents between 7% and 22%
In particular, increased connectivity (telematics and on-board computers in cars, smart home assistants, fitness trackers, wearable health care devices, and other types of iot devices) now enables insurers automatically collect more comprehensive data from customers.
They can then infuse it into their underwriting and claims management tasks to make them faster, more agnostic, and less error-prone.
more data equals better decision-making and less risk.
however, at the same time, larger data volumes require more advanced (and secure) means of processing them.
That’s where artificial intelligence algorithms come to the fore.
machine learning algorithms can effectively scan all incoming data, interpret it on behalf of insurance agents, and provide faster settlement to end users.
the best part?
With enough training data, machine learning and deep learning algorithms can improve over time without explicit programming, meaning your teams gain access to even more accurate and complex information.
Some of the popular use cases for AI in claims management include:
Just take a look at fukoku mutual life, a Japanese life insurer that incorporated an AI-powered application for medical claims processing.
fukoku mutual life: harnessing artificial intelligence to manage claims data
based on watson ibm, the application can automatically access all medical files related to the case, extract them for relevant information and automatically calculate accurate payments, based on all the knowledge gathered. the payment is sent to a human agent who approves and releases it.
After adoption, staff productivity improved by 30% and payment accuracy rates also changed positively.
fukou mutural life is not a strange case: every year, more and more insurance providers consider implementing artificial intelligence solutions for their claims processes.
Lemonade: Using Artificial Intelligence Chatbots to Compete with Giant Insurance Companies
lemonade, an insuretech startup, valued at $3.9 billion during the initial public offering in 2020, is another strong example of artificial intelligence in insurance.
The startup relies on a wealth of big data analytics and machine learning models to drive a variety of end-to-end insurance tasks.
In doing so, it has allowed them to undercut the bigger players in terms of price, speed of customer acquisition, overall customer experience, and customer engagement. A simple, fully digital and seamless insurance buying process has made Lemonade one of the best insurers for younger consumers.
for example, jim, ai’s claims experience bot, can handle the entire claims process seamlessly. In 2019, Jim handled 20,000 claims and other customer inquiries and paid out over $2.5 million without human involvement.
Implementing AI solutions, such as AI-enabled bots, can work across multiple lines of business: Chatbots can help improve customer service, collect and analyze personal data, or process claims, all while lowering the workflow in business operations and reduces costs.
2. expedited claims adjudication
insurance providers and customers want fast cycle times.
artificial intelligence plans to increase that speed by taking over some of the labor-intensive and often downright dangerous inspection tasks.
why does it matter?
just consider this—
in the united states, property insurance rate adjusters are injured 4 times more often than construction workers. pretty crazy, huh?
Artificial intelligence systems, along with supporting hardware for data collection, can make testing and evidence collection sessions much safer and faster.
Property adjusters use drones equipped with computer vision technology to more efficiently assess roof damage and provide an estimate of repair costs to the homeowner. they can also do the same to inspect industrial equipment (for example, oil pipelines), fields and crops, or a preliminary overview of an area and assets affected by the natural disaster.
AI-based claims management systems can effectively process:
all of these data sources can provide a complete picture of the assets on the site.
Furthermore, these data sets can be more accurately evaluated with ml/dl algorithms, rather than just the human eye.
Let’s take a look at the company that used artificial intelligence and machine learning to master this process in the auto insurance industry.
tokio marine: implementation of advanced image recognition to estimate repair costs
Tokio Marine auto insurer recently implemented an artificial intelligence-based computer vision system to examine and assess damaged vehicles.
The average cycle time for auto accident claims in Japan is 2-3 weeks.
tokio marine expects to significantly shorten processing time by relying on AI-rendered estimates for repair, paint, and blend operations produced based on damage images.
Other insurers, such as allstate, metlife, and esurance, among others, also accept vehicle photos as part of the claims submission process. however—
Not everyone takes advantage of image recognition to speed up the appraisal process and improve customer satisfaction by demonstrating faster and more accurate settlements.
3. quick document scanning with ocr
ocr stands for optical character recognition, a technological process for recognizing digits and handwritten text.
As legacy insurers still rely heavily on paper forms and paper documents, ocr can be a game changer to improve operational efficiency.
Instead of manually retyping information, insurance agents can be empowered with automated systems, accurately capturing and reconciling data from paper forms, and supplementing it with input from other sources.
when combined with computer vision, ocr technology can accurately represent each pixel and translate it into a respective digital input. then validate the submission against other entries in the database.
Such a high state of automation can deliver up to 80% cost savings for individual processes.
In addition, ocr applications can be implemented to improve new customer onboarding and the kyc process.
all the necessary data can be extracted from the ID photos and added to the customer profile in seconds instead of days. In this way, insurers can digitally onboard customers through web portals and mobile apps, like lemonade, and greatly reduce onboarding costs, while increasing speed and customer satisfaction factors.
As the pandemic has added a new premium on insurers’ performance, optimized customer acquisition is not an area you want to skim.
key insurance industry insights for 2021 report that:
The numbers don’t lie, and the companies that take them seriously are the ones that stay ahead.
axa cz/sk: leveraging deep learning to improve data ecosystems
axa cz/sk recently ran a poc pilot of a deep learning-based platform to extract data from incoming unstructured scanned documents.
The AI application automatically sorted all incoming documents, extracted values from hand-printed fields, and submitted the data for further analysis with an accuracy rate of 96%.
When successfully scaled, such an ocr system can save hundreds of hours of productive agent time and deliver measurable operational savings.
4. faster and more accurate underwriting
When it comes to the underwriting process, rule-based evaluation and risk engines are no longer enough to provide accurate estimates. especially as insurance scenarios become more complex (for example, usage-based insurance pricing for shared assets) and levels of fraud more elaborate.
Granted, increased connectivity across industries allows digitally mature insurers to devise better ways to appraise.
computer vision technology, coupled with iot data, can help insurers carefully record asset status at underwriting and continue to make adjustments in near real time.
By connecting a gis data stream to your analytics system, your business can not only eliminate in-person property inspections, but also monitor property condition over time to adjust policy pricing.
More elaborate scenarios can be used to assess industrial infrastructure for damage and operational mishaps. for example, the oil and gas industry now produces terabytes of operational data daily:
Insurance companies can connect past data to predictive analytics systems to anticipate degradation levels, perform automated defect inspections, predict potential failure rates and other operational risks, and adjust premiums accordingly.
Illustrative case: A global reinsurer created a machine learning algorithm to effectively predict the probability of flooding in the area, using historical and geospatial data and input from digitized documents.
such configuration has allowed them to:
5. detection and prevention of insurance fraud
American insurance companies lose more than $40 billion a year to fraudsters, and that’s not counting health insurance fraud.
fraudulent claims are really a plague.
The numbers are clearly staggering, but understandable given that most still rely on outdated rule-based systems, incapable of detecting elaborate fraud schemes.
AI-powered fraud detection systems address the shortcomings of previous applications, as well as help increase the judgments of human analysts by providing them with valuable insights.
Inherently, machine learning and deep learning systems are very capable of identifying recurring patterns. such capability makes such algorithms strong contenders for capturing unusual behavior within systems or between individual clients.
an algorithm previously trained on network and computer usage data of employees can monitor their behavior during the workday. once it detects a certain degree of deviation from standard ways of working (for example, multiple unauthorized access requests), such a security system can flag the user and alert the security team for further investigation.
Ai fraud detection applications can be employed to run quick and automatic background checks during the customer onboarding stage to carefully calculate the risks associated with individuals or businesses.
anadolu sigorta—ai on insurance for fraud detection
Turkish insurer Anadolu Sigorta recently tested a predictive fraud detection system from Friss.
Originally, the company spent over two weeks manually reviewing all submitted claims for signs of fraud. Since they were processing more than 25,000 to 30,000 per month, the processing costs were quite high.
After switching to a predictive system, the insurance company gained the ability to identify fraud in real time. achieved a 210% return on investment in just one year and attributed more than $5.7 million in saved fraud detection and prevention costs to the new AI system.
6. win insurance customers with competitive premiums for drivers
Connected vehicles now produce, store and transmit terabytes of valuable data that insurance companies can use to offer more competitive prices or switch to new business models based on consumer demands:
Some of the emerging use cases for AI for auto insurance include:
This real-time connectivity can be especially crucial in saving lives. According to the OECD, 44% of car accident deaths could have been prevented if emergency medical services had real-time information about the type and severity of your injuries.
sara assicurazioni and car club: ai for car accident insurance
sara assicurazioni and the automobile club of italy are enticing drivers to install adas systems in exchange for a 20% discount on insurance premiums.
Adas systems not only reduce the chances of injury-causing collisions, but also help drivers adopt safer driving habits.
a recent study indicates that adas systems can reduce:
ant financial: artificial intelligence technology for the new generation of insurers
One of China’s so-called “super apps,” a company that offers an ecosystem of connected digital product offerings and services, ranging from social media to banking, uses even more data points to create highly detailed customer profiles .
Artificial intelligence algorithms assign each customer car insurance points, similar to a credit score. Apart from the usual factors such as driving experience, age and car model, the system also takes into account “lifestyle factors” to create a complete risk profile for the customer.
These include credit history, spending habits, profession, etc. of the policy holder. upon input, the app assigns a personalized score and provides hyper-personalized insurance pricing, services, and an overall customer experience.
the future of AI in the insurance industry
The insurance industry is under severe pressure after the pandemic.
neither artificial intelligence (ai) nor other related technologies are a silver bullet for all underlying stressors. however—
AI insurance use cases outlined in this post have great potential to improve operational efficiency, contain costs, and enable insurance companies to pivot to digital-first customer experience and enhanced product lines with technology.
💡 read below:
27+ Most Popular Machine Vision Applications & Use Cases in 2022
65+ of the best free datasets for machine learning
what is data labeling and how to do it efficiently [tutorial]
the complete guide to cvat—pros & cons 
5 alternatives to scale ai
annotating with bounding boxes: quality best practices
and if you’re interested in learning more about AI applications in other industries, check out:
- 7 Use Cases of AI Saving Lives in Healthcare
- 6 innovative applications of artificial intelligence in dentistry
- 8 practical applications of AI in agriculture
- 6 viable use cases for AI in insurance
- 7 AI apps ready for construction work
- 9 revolutionary applications of AI in transport
- 7 out-of-the-box applications of AI in manufacturing
- 6 AI apps shaping the future of retail