4 key emerging technologies in Insurtech
The growing impact of digital solutions on the insurance sector can be seen everywhere. Startups and scaleups have been investing heavily in software-based solutions, making it hard for incumbent players to compete. So, if you want to keep your business afloat, there is an urgent need for new technologies to be incorporated — below you will find four of those technologies that offer the biggest potential to be drive your business forward in the coming days.
The entire insurance market depends on risk assessment and predictions so it is little wonder that a growing number of companies are using data mining, predictive modelling and machine learning to analyse current and historical data to make predictions about future events.
Predictive models work by trying to find patterns in both historical and transactional data sets. By analysing many factors the algorithms can draw conclusions from certain circumstances, identifying risks and opportunities, which in turn help to guide the decision-makers. For instance, insurance businesses which rely on processing huge amounts of data use predictive analytics to cover areas such as actuarial models, pricing strategies, underwriting or claims.
- Infinity Property & Casualty Corporation reduces its payments on fraudulent claims and improves its ability to collect payments from other insurance companies. Thanks to predictive analytics provided by SPSS the company was able to achieve 400% ROI within six months.
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Machine learning (ML) is the study of computer algorithms that improve automatically through experience. In other words: ML it is the science of getting machines to learn and “think” in a similar way to humans while also autonomously learning from real-world interactions. Using mathematical models based on a sample, known as “training data”, the algorithms can make predictions or decisions without being explicitly supervised.
Extensive usage of Machine Learning by the insurance companies dealing with health, property and casualty insurance helps them to vastly improve their performance in customer service, fraud detection, and operational efficiency.
Many use cases for these Machine Learning insights can be found, from identifying opportunities for cross-selling and up-selling to assessing the damages based on the current and historical data.
Furthermore, it is also helpful in fraud detection – by using deep anomaly detection which learns by analyzing genuine claims the algorithms can identify any fraudulent patterns.
- Lemonade uses ML to underwrite customer risk and handle claims. Thanks to its commitment the company was able to generate $100M just in 2.5 years.
Chatbot technology is the main driving force behind innovation in customer service across many sectors. According to Grand View Research, by 2025 the global chatbot market is expected to reach $1.25bn (£0.96bn). There is also an enormous demand from insurance companies for its quick implementation. Why? Due to its ability to offer a customized experience 24/7 with lower processing time and faster resolution it is expected to save over $8bn (£6.1bn) by 2022.
Chatbots can be of tremendous help when trying to handle lead generation and conversion. Thanks to their capabilities they can easily identify the query and consumption patterns to suggest tailored offers.
Furthermore, they can be used to address the customer’s insurance claims, additionally following up with customers regarding existing claims and payment notifications. Not to mention providing customers with the answers to FAQ’s (i.e policy comparison or policy suggestion).
- Sensley introduced a chatbot-based platform to assist plan members and patients with insurance services. As a result, the company was named a 2019 “Cool Vendor” in Healthcare Artificial Intelligence by Gartner.
Blockchain is a type of data structure that identify and track transactions digitally, sharing this information across a distributed network This cryptographic tool is resistant to unauthorised modification of data and typically managed by a peer-to-peer network collectively adhering to a protocol for inter-node communication and validation of new blocks. Once recorded, the data in any given block cannot be changed retroactively without changing all subsequent blocks, which in turn requires consensus of the network majority.
There is no doubt that the implementation of blockchain in the insurance industry is becoming more prevalent. Companies are using it to provide automatic verification of claims or reinsurance which are based on the genuine information stored in blockchain.
There are many use cases with secured Peer-to-Peer insurance payment from the investor to the customer in case an insurance demand event occurs. Whilst the majority of blockchain-related activity in insurance has to do with internal POC projects, industry players are trying to investigate its further potential with the EU-based Blockchain Insurance Industry Initiative (B3i) paving the way. Its goal is to accelerate the development, testing and commercialisation of blockchain solutions in insurance.
- RYSKEX uses blockchain to secure payment transactions between contracting parties, combining it with artificial intelligence to assist in premium calculation and evaluation in the occurrence of loss. The company claims that in 5 years the coverage process, as well as claims handling process, will be fully automated.
Ever-growing competition within the insurance market requires incumbents and new players to introduce new measures, making a shift toward digital-first business models. As many use cases show only when relying on technologies such as predictive analytics, machine learning, chatbot or blockchain can companies thrive by securing their interests and providing quality products to their customers.
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