Digital disruption invented and implemented by FinTech companies is a double-edged sword. Companies that went after main-street banks and traditional insurance firms, now face another era. They have to innovate to stay alive. How leading insurance companies make use of artificial intelligence (AI) will make or break their businesses. Customers face new risks and want to make sure the InsurTech sector can calm their fears. This is how you can help them.
Face the customer with a digital-oriented mindset
People – they want it all and they want it now, to quote a famous song by Queen. Open banking and other forms of instant access to vital private information changed consumers’ behaviors. They have everything at their fingertips. People check the price of the insurance and wonder how much time it will take to settle the claim. As we have pointed earlier in our article How Machine Learning is Changing Pricing Optimization, it can even take seconds. These are pressure points for any business – how much does it cost and how much time will it take me to get (or not) for what I’m paying for.
By establishing a digital mindset and building your application around simplicity and transparency, you vastly increase your chances of building a loyal customer base. Maybe even a community. The power of InsurTech lies in prediction and quality service. Before we shine a light on AI, let’s see what digital insurance companies do right. They:
- establish a real-time question-and-answer service to handle first notice of loss
- pre-assess claims and automate damage evaluation
- automate claim-fraud detection with advanced data analytics
- predict local threats and, as a consequence, claim patterns
That’s only the beginning. The real fun begins with machine learning and full-blown AI for insurance companies. As McKinsey says in the article on the future of insurance, there are four important AI-related factors shaping the industry:
- an increasing amount of data from connected devices
- the rise of additive manufacturing, robotics, and 3D-printed… everything
- progress of cognitive technologies
- open source and data ecosystems
It’s easy to understand that this explosion of inter-connectivity will require knowledge, business models, and end-to-end software development. Many companies, especially startups and scaleups, can’t operate with developers on board. They need partnerships – if not for development per se, then for user experience (UX) and user interface (UI) design.
Do you need more proof? Juniper Research claims that investments in emerging AI technologies will increase their annual savings. Up to 4 times in 2023, in comparison to 2019. The major fields that will save money – life, health, car, and property insurances.
How can AI save insurance companies time?
The short answer? By implementing a series of solutions that help evaluate and pay off claims. The long answer? By utilizing these 11 innovative ways to sustain and grow business:
- Insurance pricing. Pricing is never easy. You have to calculate profits and risks. Don’t drive customers away, optimize. Use AI to assess customers’ risk profiles. X-ray them with lab testing, patient-generated health data, biometric and claims information. This level of automation improves the workflow for business operations and generates more revenue. The more information you have, the more tailored and client-friendly the insurance plan will be.
- Application processing. It’s hard to find people that like paperwork. It’s exhausting and feels like a waste of time. That’s why many InsurTech companies replace manual labor with the document capture software. These applications can extract and process relevant information from applications and process them right into categorized fields in a larger informatics system. The process decreases the time needed for processing.
- Chatbots. Properly written and configured, chatbots can even charm someone into signing up. They are more advanced with every passing year. Interacting with customers, they can automate not only the signing but also the onboarding process. Not to mention a mundane question answering. Chatbots can also forward messages to agents that are currently free or most qualified to help with the specific need.
- Affective computing. This is also known as “emotional AI”. The software can assess the client’s mental state and assign resources (claim processing, additional documentation gathering, etc.) Based on the seriousness of a claim (tone of voice, keywords in the conversation), the system can route the call to a specific or more experienced agent to handle the claim. Affective computing can also help detect fraud; partially using previously mentioned methods.
- Deep learning. Companies can create machine learning models to evaluate clients’ risk profiles. Based on that data, they can also offer optimal prices for insurance and additional services. This will free agents from manually sorting documentation.
- Claims processing. This is probably the most ungrateful stage for any insurance company. You have to investigate and review the claim, proceed with the payoff or make a denial. The problem is – a lot can go wrong here. Human labor is invaluable but often prone to error. Simple mistakes can be made. While inconsistencies can give you a headache, unjustified payoffs can create havoc. Different data formats in claim applications are also a problem – it’s time-consuming and mostly mindless. Unstable and ever-changing regulations are also a challenge. Staff needs to be aware of it and updated frequently. Internal training takes time, and time is money.
- Appeals processing. By far, another ungrateful stage. Manual sorting and processing a pile of appeals can be troublesome. The right InsurTech software development, either from scratch or through API development, can save you time.
- Document creation and processing. Manual data extraction is not what you have in mind when you say “efficiency”. That’s why companies like Hypatos or Applica have their own solutions. They claim to save even up to 90% document-related expenses.
- Responding to clients’ questions. According to LarcAI, a South African insurance company called Hollard, reduced their cost transaction by 91%. With an automation level of 98%. There are of course other companies, such as UiPath, to do similar things. Either way – the shorter is the time of user response, the higher level of customer satisfaction. And potential loyalty in the future.
- Personalized services and solutions. As a Code & Pepper, we stand by personalization. Of software services, that’s a given. But even more when it comes to end-users. Around 80% of them claim that purchases are more likely with companies that personalize experiences. Don’t take our word for it, check Accenture’s study.
- Claim fraud detection. Did you know that 11.8m Americans (by their own admission!) lie to their insurers? The study by Finder shows that roughly 29% of Americans lie in case of car insurance and 28% on health-related issues. AI in insurance companies takes care of that. It can detect false claims, even by going through the claimant’s interactions with the InsurTech brand, current location, etc.
Looking for practical examples? We got you covered.
How insurance companies utilize AI?
Tractable uses AI for accident and disaster recovery. The application is trained on millions of car accidents. It can assess claims based on photos. The company claims the software understands accidents and loss in car value, as well as humans, do. It can estimate car repair costs in real-time and produce a real-time car damage estimate.
Bind uses UnitedHealth Group’s resources and data to manage benefits for self-insured employers. Their machine learning algorithms design health plans for every type of coverage. The company scans health and self-employment history and correlates the data to produce an optimized and personalized offer.
CoverWallet is a very interesting option for small and medium-sized business owners. This highly-personalized app offers many types of insurance, dedicated to different occupations. The platform utilized big pools of data and their analytics capabilities to calculate insurance for all – contractors, startups, retail stores, accountants, and more. The more you tell them (number of employees, economy sector, annual revenue, zip code), the more precise the outcome.
These are only a few real-life examples. There are many more. AI solutions for insurance companies are now so advanced, they can identify users through biometrics, photo tagging, searching of objects within photo or video, and product image matching.
A Turkish insurer called Aksigorta leveraged predictive modeling and image processing for its claim investigation process. The result? Increase in fraud discovery by an amazing 66%.
Carpe Data gathers and emerging data sources to enhance all facets of the insurance life cycle.
The good, the bad, and the ugly
Let’s start with the bad, so we can end on a more positive note. Solutions cost money. Either they are AI-driven or not. InsurTech development should address every problem – from frauds to reusable UI components. You will need expertise and a skilled team of specialists. That will make investments and cooperation in Agile. That’s the “bad”, intentionally in quotes.
The ugly – Gartner survey shows that a brand can lose even 38 percent of business due to mismanaged marketing efforts. In other words – if by “personalization” you mean intrusive data mining and other techniques that make people uncomfortable, then it’s not the way.
Brands are looking for solutions that ease customer experience friction. People expect consistency across UX/UI, fair risk tolerance calculations. Also, custom insurance options based on preferences, and mildly-noticeable conclusions drawn from spending patterns.
The good in all this? You can fairly easily establish trust. How?
Trust – the ultimate tool for user acquisition
How can you be successful? By trusting others. Code & Pepper has been here for a while. We know our way around the block. By actively listening to our partners, we can not only develop but also consult. On the app, even on the business itself. AI can save insurance companies time but it can’t save them headaches related to development. We got this covered.