Insurtech 101: What is Insurance Technology?
What is insurtech?
A new wave of technology-savvy players is entering the insurance sector, bringing a robust set of innovative, disruptive, and opportunity-rich capabilities. In the same way that fintech has changed banking with its emergence, it will change the playing field on which insurance companies compete.
Fintech companies operate at lower costs than traditional banks. Fintech began with several startups with similar goals. These new financial tech companies paved the path for technology integration in traditional corporate businesses – birthing a more modern era by developing innovative products that are scalable and delivering them digitally.
In the insurance industry, a similar path is paved for brokers, agents, and IMOs. Insurance and technology, or insurtech, have come together due to rapid technological advancements and disruptive innovation strategies in the insurance industry.
There is a steady rise in competition between the traditional insurance industry and digitization. However, this change could be particularly lucrative with partnering opportunities. Artificial intelligence, machine learning, and big data are some of the innovative technologies that have been applied to the insurance sector in recent years, as well as insurtech startups offering these types of solutions to create disruptive insurance products.
The rise of insurtechs
As digital transformation increasingly reshapes how customers interact with businesses, their expectations of instant, seamless transactions across all channels are becoming the new norm. And while insurtechs have not yet made deep roots in the insurance sector, they are snowballing and stand to capture a significant share of the value pools within a few years.
How quickly IMO and independent agents adapt to these inevitable market changes will determine the size of their share in the next generation of the insurance industry. Those who can quickly adapt and provide a superior digital customer experience will be rewarded with a larger share of the pie.
So what does this all mean for IMO and independent agents? First, they need to up their digital game to remain competitive, which means investing in the tech tools for life insurance brokers to provide a superior customer experience. Those who don’t adapt will be left behind, so IMOs and independent agents must make the necessary investments to stay ahead of the curve.
Like fintechs in banking, insurtechs initially focused on serving retail customers: 75 percent of their business serves retail clients, while the remainder serves commercial clients. In addition, as millennials become a more targeted market, online, social media, and digital technologies offer the opportunity for an increase in clientele in retail. As a result, insurtech investments have grown fivefold (from $5 million in 2011 to $22 million in 2015).
When fintech began gaining attention, banks feared it would take away their market share but realized that it would help them earn more instead of replacing them. The exact sequence of events is happening with insurtech. Insurance technology is mainly service-based, including consulting, support, and maintenance for insurance businesses. Managed services also account for the most significant portion of what insurtech companies do. Insurtech startups are not here to push out the traditional leaders in the industry. Instead, they are here to improve, grow, and support the transition from conventional to modern digitization. To meet the rising demand for insurance and maintain a competitive edge, roughly 86% of insurers plan to update and innovate their business models by 2022, according to Accenture. Insurtech is here to stay for good, paving the path for those willing to adapt to the current consumer demands.
Types of Insurtech
Artificial intelligence
In artificial intelligence, computers and algorithms are used to solve challenges traditionally handled by human intelligence, such as speech recognition, image analysis, and complex decision-making. Through machine learning, computers can learn from experience and improve their prediction accuracy over time.
AI and machine learning are used in insurance companies to improve customer experiences and streamline operations. Chatbots can communicate with humans in various situations and platforms. Besides providing customer service, companies can also use bots for product suggestions, sales, scheduling, and engagement. Through chatbots, natural language processing handles customer inquiries and requests over the phone, via text, or on social media.
Machine learning
One area that has seen significant development in recent years is machine learning (ML). Machine learning is artificial intelligence that allows computers to learn from data and improve their performance over time. Machine learning systems use algorithms instead of explicit programming to learn from data rather than explicitly from a set of specific instructions. It allows insurance companies to extract more value from the client data they are collecting.
The insurance industry is no stranger to technological innovation. In recent years, the rise of insurtech has disrupted the way insurance companies do business, giving rise to new startups and products that aim to make the insurance process easier and more efficient.
Insurance companies are starting to use machine learning to help them with various tasks, from fraud detection to pricing. In the future, machine learning is likely to have an even more significant impact on the insurance industry as it becomes more sophisticated and is used to solve more complex problems.
Risk modelling
Most people think of insurance as a way to protect themselves financially in an accident or unexpected event. But risk management and predicting risk is insurance’s longest-standing pain point. With the advent of big data and machine learning, insurers can now process vast amounts of data from multiple sources to predict risk more accurately than ever. Data points indicating risk include policy contracts and claims data to weather parameters and crime data.
This improved ability to predict risk means that insurers can provide better coverage at a lower cost. It also allows them to be more proactive in preventing accidents and events from occurring in the first place.
Demand modelling
Customer Lifetime Value or CLV is a complex metric that represents the value of a customer to an organization. It considers the revenue gained from the customer minus the expenses incurred – all projected onto the entire relationship with a customer, including the future.
Insurers can now predict CLV using customer behavior data to assess the customer’s potential profitability. For example, behavior-based learning models can be applied to forecast retention or cross-buying, all critical factors in the company’s future profitability. This data-driven approach to customer lifetime value is a game-changer for the insurance industry and will help companies make better and more informed decisions about their customers.
Processing claims
We all know that insurance is a necessary part of life. Whether it’s your car, your home, your health, or your business, insurance protects you from the unexpected. And when something does happen, the last thing you want is a lengthy and complicated claims process.
Unfortunately, claims processing is often one of the most problematic and time-consuming aspects of the insurance process. Insurers need to collect a lot of information to process a claim, which can often lead to delays and frustration for policyholders.
There’s no doubt that insurers could improve their business model by streamlining the claims process with ML and AI, making it simpler and more efficient. As a result, businesses would save money and improve customer satisfaction.
So how can insurers streamline the claims process? Here are a few ideas:
1. Automate wherever possible
2. Reduce the amount of information that needs to be collected
3. Improve communication with policyholders
4. Make use of technology, such as artificial intelligence, to speed up the process
5. Streamline the appeals process
By making simple changes to the claims process, insurers can make a big difference to their bottom line.
Underwriting
Underwriting is a critical part of the insurance industry, and it is increasingly being done with the help of machine learning. With ML involved with insurance-based underwriting, businesses can scan through billions of data points to determine the prospective client’s policy terms – ML models analyze data on the internet about these customers to select specific risk factors. Automated AI underwriting applications can use these factors to formulate policies with guidelines that minimize risks while providing just enough information to help customers make decisions.
For example, in a particular region, AI-based applications may discover that future customers of a certain age are likely to die from a specific health condition by analyzing digitized medical records for people of that age group. As soon as the situation is revealed, insurance companies can underwrite terms to manage their risks and losses while keeping the policy useful for future policyholders.
Detecting fraud
There are several ways in which machine learning can improve fraud detection techniques. In addition, ML can also enhance security in the following ways:
- Quick processing time for data analytics
- Provides information about how various factors can be connected in ways that are not detectable by the human eye
- Discovers new fraud schemes by using various data analysis techniques
While machine learning borrows principles from statistical models, it is primarily a prediction-based approach. To make predictions, “ground truths” is analyzed from known outcomes. Unstructured and semi-structured data, such as claim notes and documents, can also be analyzed by ML to detect fraud.
As well as helping insurers decrease fraud costs, ML can be used by insurers to detect suspicious patterns in claims processing and customer background checks which may save them a great deal of money.
The insurance industry relies heavily on insurtechs focusing on privacy and cybersecurity to protect sensitive information. Incorporating machine learning and AI innovations can reduce the risk of security breaches and provide peace of mind to potential clients that their sensitive information is safe within your company.
Is insurtech easy to use?
Simply: Yes.
As with any new technology, systematically improving the user experience is crucial for insurtech. They must keep trying to make it easier for customers to find the right product and get started quickly by reducing the number of forms to fill out and eliminating long wait times on the phone. For most people, insurtech will be the most straightforward and convenient option.
Some insurtech that many brokers must adopt within a company may demand a change of habits and general setup for the company. However, most technology companies aim to create products and platforms that are easy to use and offer customer support, demos and tutorials for whole teams and individuals. Of course, it’s best to use tech that resembles what you already use but overall, it’s worth it.
About Finaeo
Insurtech is one of the most exciting things in the insurance industry in recent years. This new insurance technology can make pricing more competitive and tailor insurance policies to each individual’s needs.
At Finaeo, we are committed to making insurance accessible and affordable for everyone. We believe that insurtech can help achieve this goal by enabling people to get the coverage they need at a price that fits their budget.
We are excited to be at the forefront of this new industry. If you have questions about how this technology can benefit you, please don’t hesitate to contact us. We would be happy to chat with you about your options. Book a demo to learn how insurtech can support your business.
