In what ways are insurtechs helping transform the face of insurance to a more digital proposition?
Insurtech is now established as a sector in its own right and has made great strides in transforming and digitising all aspects of insurance.
At a high level, insurtechs fall into two main categories; those providing technology to insurance companies to help them to innovate, and then those providing new ways to distribute products, by combining technology with sales, and bundling products into a more innovative package.
Finally, there are a few full-stack insurtechs, such as Lemonade in the US and Element in Germany, that have built a completely new digital business model.
In the case of insurance software platforms, these are now capable of automating every link in the insurance value chain, from product building to distribution, underwriting to claims, and beyond.
This enables insurance providers to test and get products to market more quickly, differentiate their offerings and become more responsive to customer needs. It enables greater efficiency, fewer errors, and a more streamlined, positive customer experience from sales right through to claims.
Insurtechs are also helping to improve the efficiency of back-office insurance processes, such as finance and accounting, to keep track of invoices and payments, alongside detailed data, and reporting on risk and performance.
How are insurtechs able to foster digital transformation without disrupting the flow of daily business at legacy insurers?
In years gone by digitisation was highly challenging for legacy insurers due to their specialised and highly complex systems and processes.
That has now changed thanks to no-code software providers which are providing a modular, drag-and-drop approach, to automate operations and workflows without lengthy development times. Whereas previously digisitising workflows would take months to complete, it can now be done in a matter of weeks.
Digital transformation inevitably brings some level of disruption, but there are ways to minimise its impact. Insurers should aim to simplify technology implementation as much as possible, by utilising modern, no-code solutions with What-You-See-Is-What-You-Get (WYSIWYG) editors to decrease the complexity and stress of technology adoption.
These tools provide intuitive interfaces that enable easier implementation, reducing the disruptive effects on daily business operations.
Insurtech solutions are also now easier to integrate with existing legacy systems through modular approaches and application programming interfaces (APIs).
By selectively integrating specific modules or APIs, insurers can enhance or replace targeted functions within their operations without requiring a complete system overhaul.
Adopting an incremental implementation strategy rather than a large-scale, all-at-once approach helps to minimise disruption, allowing for smoother transitions and adjustments.
Finally, firms should secure team buy-in early on, ensuring that the entire business understands the purpose and benefits of digital transformation.
By emphasising clear communication, you can foster a collaborative environment. Celebrating small victories along the way will help maintain team morale and make the process more manageable.
How are insurtechs using big data in the insurance process?
Big data can help to enhance all aspects of the insurance lifecycle. From a customer perspective, customers are happy to give insurance companies access to their information if they know that it will make their life easier, by not having to fill out lengthy forms or re-key information.
By drawing on different data sources, for example, car registration databases and other public records, insurance companies and insurtechs are making the customer journey as simple as possible.
For example, we are now able to ask for just the car number plate and with that we can quote prices from all the insurance companies in that country, automatically populating all the other details we need.
A comprehensive, real-time database can also help to drive strategic customer decisions.
Information on quote takeup, renewals, claims and customer demographics enables businesses to read the market, quickly test new strategies, and understand what products and services best suit the needs of a given customer segment.
Hypotheses can be tested, targeted, and tracked, to understand which are causing the biggest uptick in customer value.
Big data also enables insurance companies to personalise their communication and interactions with customers throughout the insurance lifecycle, from recommending the most appropriate products, to providing tailored tips and advice on minimising risks, and ensuring that they’re ready to renew their policy at the right time.
Explain the relationship between big data, machine learning and AI. What are the most innovative ways big data is being used?
Overall, the combination of big data, machine learning, and AI has transformative potential across the insurance value chain, in optimising sales, policy administration, support, and claims processes.
In the sales process, big data analytics can be leveraged to analyse customer behavior, and preferences, to improve targeting and customer acquisition strategies.
Meanwhile, machine learning algorithms can draw on that data to assist in predicting customer needs and tailoring insurance offerings accordingly.
In policy administration, big data analysis can help insurers streamline processes, automate underwriting decisions, and detect potential risks.
Machine learning models can be trained to assess risks, calculate premiums, and optimise policy management.
By utilising AI-based software, insurers can efficiently train LLMs to deal with customer queries and requests such as executing Mid-Term Adjustments (MTAs) and providing prompt answers to customer enquiries about policies, claims statuses, and more.
This streamlines processes, enhances customer experiences, and improves operational efficiency.
Finally, big data analytics can enable more accurate claims processing by analysing various data sources such as historical claims data, customer information, and external data. Machine learning algorithms can automate claims assessment, fraud detection, and expedite settlement processes.
At Insly, we have seen the efficiency improvements that are possible with AI. We are using an AI-based large language model (LLM) to configure our software for MGAs and insurers, removing some of the specialist implementation work that was previously done by our team.
Now the majority of this can now be done by an AI agent. This is an example of how insurers can leverage these technologies to enhance their operations and deliver superior customer service.
How can big data be leveraged in even more innovative ways in the future?
In the future, it is plausible that insurance companies may no longer require human employees. When we analyse the tasks performed by insurance companies, there seems to be no inherent need for human involvement that can’t be fulfilled by technology or autonomous AI agents.
Although reaching this level of automation may not be possible currently, it can serve as an ambitious objective for the industry to strive for.
Please also take a look at our upcoming virtual event, InsurTech LIVE, coming on 18th-19th October 2023.
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