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How AI and Machine Learning Drive Innovation in Insurance Industry

When you hear about artificial intelligence, insurance is probably not the first area of application that comes to mind. However, this industry has been consistently fast to adopt any promising technological and societal trends, and AI is not an exception. In fact, insurance is probably the most active in its adoption and application among non-tech-heavy industries. So how exactly do artificial intelligence and machine learning alter the shape of this market?

1.    Natural language processing

Computers can process information millions times faster than the most gifted mathematical savant can ever hope to. However, they are also notoriously bad at gleaning any sort of meaning from the data that is not already arranged in neat tables. Unfortunately, most human-generated data is far from being this orderly – usually it is presented in textual form. While insurance experts can find a lot of useful info in documents, legal and otherwise, emails, social media chatter, chat logs and suchlike, humans cannot do it reasonably fast, and computers cannot do it at all. Enter natural language processing, or NLP – research direction aimed at teaching machines to find meaning in unstructured texts. It has seen a few important breakthroughs in recent years, and even though the technology is still in its infancy stage, it already shows results. While for now they are limited to streamlining query response systems based on the analysis of previous data, in the future we can hope to see the AI that truly understands human language and reacts to it.

2.    Computer vision

Computer vision is a technology that allows computers to extract meaning from visual data. For insurance companies, it is mostly interesting due to its significant time- and labor-saving applications. For example, it is already used in car insurance: one can use a special app to take a photo of a car after an accident to assess damage and repair costs. The system uses an algorithm trained through analyzing tens of thousands car crash photos to make accurate predictions that can be used in claim settlement. Life insurance can benefit from this tech as well – for example, in-car camera feeds can be analyzed to detect dangerous behavior such as talking on the phone or texting while driving, which then influences the driver’s insurance policy’s conditions.

3.    Behavioral policy pricing

The advent of the Internet of Things with its ubiquitous sensors means access to loads and loads of highly personalized and automatically collected data. Some people are happy about it, others are concerned about the end of privacy as we know it, but for insurance companies there can be no two opinions – they are among the most obvious beneficiaries here. It will be possible to heavily customize the conditions of an insurance policy for every individual client while leaving nothing to chance. It means it depends on the life or general liability insurance policy companies are focused on. As life insurance policy is often the cornerstone of a business’s succession plan. While business liability insurance policy typically provides insurance coverage to small businesses for, among other things, third-party bodily injuries, medical payments, and advertising injuries. 

Insurance companies can reap significant benefits from overhauling their core IT systems. Deciding which approach to choose depends on a range of considerations. 

Right now, insurance brokers have to operate based on limited information, but in the future, it will change. Smart cars will collect data about driving habits, meaning that safer drivers will pay less for auto insurance, wearable devices will collect data about one’s habits and lifestyle, meaning that people with higher life expectancy will pay less for life insurance.

4.    Customer Data Solutions for the Insurance industry

If you know who your customers are and how they want to be treated, you may:

  • Build hyper-personalized relationships with first-party data;
  • Deliver frictionless engagements with customer management and identity;
  • Scale and optimize your B2B value network and reduce risks;
  • Offer seamless, personalized B2C engagements
  • Provide the data privacy and trust customers demands;
  • Address CCPA and other regional data privacy requirements
  • release of SAP Customer Data Cloud solutions – which includes the SAP Customer Identity, SAP Customer Consent and SAP Customer Profile solutions

With SAP Customer data cloud you have the possibility to identify your customers more easily, try to get to know them and to turn them into loyal customers and even true ambassadors of your brand.

SAP Customer Data Cloud consist of three pillars:

  • SAP Customer Consent: Be transparent, gain loyal customers, and protect your business
  • SAP Customer Identity: Identify, engage, and protect your customers
  • SAP Customer Profile: Power trusted digital experiences with first-party data

The SAP Customer Profile connects this data into various channels, including SAP Marketing Cloud. While SAP Customer Identity manages the customers and the SAP Customer Consent manages the consent.

This solution will help you to collect the customer’s data, contact customers at a time, create a Target Group to use it in a campaign: like email campaigns to send follow-ups to the client, phone calls, follow-ups on Sale Clouds.

 

5.    AI interfaces for customer experience personalization

For now, the use of AI for direct interaction with the customers is mostly limited to rather primitive chatbots (which is already a useful achievement, as they can answer typical questions, allowing human employees to focus on tasks that are more complex). However, in near future, we can expect interfaces that will replace humans in many other situations. They will be able to recognize clients based on image recognition and processing social data to customize client interaction and verify their identity. Eventually, these solutions will make it possible to make the entire insurance policy shopping experience entirely online or app-based. As far back as in 2017, an insurance startup Lapetus has already been offering its clients to buy life insurance using a selfie. It uses an algorithm that looks for the signs of harmful habits and lifestyle choices like smoking and modifying premium costs accordingly.

6.    Speeding up claim settlement and decreasing fraud

The scale of losses causes by insurance fraud is unknown, but the industry specialists estimate it at about 10 percent of all claims expenses. Even if it is a bit lower, it is still an enormous share, and anything that can help the industry battle fraud should be among its primary concerns. AI and machine learning vastly broaden the toolset used for that purpose. By analyzing vast amounts of data, smart algorithms can find common tendencies and automatically look for signs of fraud, either directly indicating criminal activity or warranting further investigation.

CONCLUSION

These are just a few of the most obvious ways in which AI and machine learning are going to change and are already changing the insurance industry – real applications are undoubtedly going to be much more numerous. Thanks to SAP Customer Data Cloud, you will understand your customers better, know their personal preferences, master the new customer landscape and build trusted and valued relationships with them.

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