How AI is transforming InsurTech

As AI continues its ascent as a transformative force in the business landscape, the InsurTech sector has found itself as the latest realm aiming to tap into its remarkable potential. FinTech Global recently spoke to a host of industry experts in a bid to unravel the profound impact AI is poised to have on the space. 

As AI continues its ascent as a transformative force in the business landscape, the InsurTech sector has found itself as the latest realm aiming to tap into its remarkable potential. InsurTech Analyst recently spoke to a host of industry experts in a bid to unravel the profound impact AI that is poised to have on the space. 

Over the past decade, advanced technologies such as Machine Learning (ML), and AI have been embraced by companies across the globe. Now, with efficiency seen as a vital component to success, those same technologies are perfectly placed to have a transformative impact.  

Insurers are now looking to streamline day-to-day functions, ushering in automation and improved process efficiency. 

Furthermore, these technologies empower insurers to harness treasure troves of customer data, enabling sophisticated analysis and informed decision-making. This, in turn, facilitates the provision of more tailored and profitable insurance offerings, amplifying customer satisfaction and business performance. 

Max Stratmann, Chief Revenue Officer, Scanbot SDK, explained the transformative impact the technology is having on his firm. He said, “AI technology, especially machine learning, is a cornerstone of our InsurTech solutions. In particular, we use it to further refine our mobile document scanning and data extraction solutions. Some key areas of improvement are an enhanced user guidance and automated post-scan processing, such as cropping and perspective correction.” 

These gains, while notable, are widely considered to be the tip of the iceberg. AI is an ever-growing industry – and the market has witnessed remarkable growth in recent years, securing a valuation of $136.55bn in 2022. 

Forecasts suggest that this is just the start of an exponential rise, with a projected compound annual growth rate (CAGR) of 37.3% from 2023 to 2030, reaching an estimated $1,811.8bn by 2030, as per Grand View Research

Moreover, PwC predicts that AI will contribute $15.7tn to the global economy by 2030, surpassing the combined output of China and India. With such staggering figures, it’s no surprise that AI is being widely adopted in the InsurTech sector, with its potential now recognised as almost undeniable. 

Jamie Wilson, Head of Pricing and Innovation at hyperexponential, weighed in further on the technology’s remarkable potential, explaining, “According to McKinsey, AI technologies could add up to $1.1trn in potential annual value for the global insurance industry.” 

Scott Holmes, Quantee‘s Sales Director, UK & Ireland, went one step further in his appraisal of the technology, claiming that he felt that the adoption of AI was “basically mandatory” to stay competitive. 

He said, “InsurTechs typically have very data-driven propositions, and without the use of AI it is very challenging to really maximise the potential of this data. By utilising AI, InsurTechs will be able to make far more precise underwriting and risk assessment decisions in rapid time, allowing InsurTechs to operate their underwriting operates effectively and efficiently. Because of this, in my opinion, it is now basically mandatory to utilise AI in order to stay competitive.” 

Vitali Yurkevich, CEO of Symfa, echoed this sentiment, illustrating how the software development firm is leveraging AI to unlock significant efficiency gains for an international insurer. By employing AI, Symfa is planning to liberate a staggering 30% of its underwriting capacity—a figure too substantial to overlook. 

“An innovative project we’re involved in now is an AI-based solution that enables a major international insurance company to free up to 30% of its underwriters’ capacity. This solution compares insurance policies and creates customised quotes for brokers within the predefined bounds of profitability.

“This project also aims to enhance the attractiveness of policies while ensuring acceptable profitability margins. During the 1st phase, we structure all data from quotes, consolidate all variables involved in the financial calculation formulas into a comprehensive dataset, then train a neural network-powered AI bot on internal computations to ensure the precision of the calculations. This enables the company to analyse their policies against competitors, rank them based on customer appeal, and propose revised customised quotes which are sometimes even more profitable than the initial ones,” Yurkevich explained.

The potential pitfalls of AI adoption 

As InsurTechs increasingly embrace AI technology, they unlock a myriad of benefits that promise to revolutionise the insurance landscape. From enhanced data-driven decision-making to streamlined underwriting processes, AI offers unprecedented opportunities for efficiency and profitability. However, amidst the allure of these advantages, it’s crucial to acknowledge the flip side. For every benefit AI brings, there lurks a corresponding challenge or disadvantage that demands careful consideration. Balancing the promise of innovation with the realities of implementation is essential for InsurTechs navigating the complex terrain of AI integration. 

One such concern is that insurers have now confronted the reality that they were late with their adoption of the software, as revealed in a report by ISG. The acknowledgment of this tardiness has stirred concerns about the risk of being outpaced by competitors, which may prompt a potential rush toward AI integration. 

The urgency to bridge this gap is evident, with 61.5% of insurance firms intending to allocate resources towards creating digital customer experiences, followed by 51.3% prioritising investment in cloud transformation and 46.2% focusing on data, machine learning, and AI technologies, according to the survey. 

However, reacting too quickly, and not analysing the benefits of the system could leave customers disillusioned with a company’s latest product. 

hyperexponential’s Wilson gave his view on the subject, stating, “Insurers must avoid getting caught up in the hype of looking everywhere to apply AI, and instead align their AI initiatives closely to broader strategic objectives. It’s crucial for insurers to have a clear vision of how AI can enhance their value proposition, improve customer experience, or streamline operations. Misalignment could mean that significant investments in AI do not translate into competitive advantage or operational improvements. On the other hand, moving forward in a pragmatic way with a strong business focus helps to ensure investment has the biggest reward. 

“The successful integration of AI into existing operations also hinges on the organisation’s ability to adapt its culture and workflows to new possibilities. Resistance to change is a common obstacle, especially in industries with long-standing traditions and practices. Companies must foster a culture of innovation and continuous learning to fully leverage AI’s transformative potential,” he continued.  

Moreover, concerns can arise when firms rely too much on AI. Many companies that haphazardly incorporate AI into their solutions often view consumers merely as data points. 

They rely heavily on customer metrics and scorecards to set up their AI-driven service processes. Consequently, customers often encounter impersonal experiences that lack authenticity and fail to meet their needs or expectations – an issue that would only be exasperated in the hyper-personal world of insurance.  

The potential pitfalls do not stop there. AI algorithms, while powerful tools, have the potential to inadvertently perpetuate biases inherent in historical data. 

This can result in discriminatory outcomes across various aspects of insurance operations, including pricing, underwriting decisions, and claims processing. 

By relying solely on historical data without careful consideration and mitigation of biases, AI systems may unintentionally reinforce existing inequalities and lead to unfair treatment of certain demographic groups. Such biased outcomes can undermine the principles of fairness and equality in insurance practices. 

Ashleigh Gwilliam, Director of Insurance Industry Growth at FullCircl explained, “Sole reliance on AI leads to potential issues, AI lacks ethics, there are no exceptions to the rules and by its very nature is extremely bureaucratic; the true success comes from combining AI with human knowledge and oversight, utilising its vast computing power as a tool, not as a leader.” 

Ultimately, recognising the limitations of this promising, yet fledgling technology, is crucial in ensuring responsible and ethical deployment of AI across various industries, including insurance. 

Ilya Mokin, Head of R&D at Symfa, reasserted this point, declaring, “While the potential of this technology is significant, it requires extensive fine-tuning and testing efforts to ensure accuracy and reliability.

“Let’s consider such a use case where AI facilitates reporting and builds easy-to-read graphs retrieving insights from a mass of unstructured data stored in an ERP or a CRM system. Say, if you have multiple tables and require a graph with grouped data, you can ask the ChatGPT bot to generate the report. The bot will determine which APIs to call, retrieve the data, group it, and generate the report. Code stability and data omission would definitely be the problems you’re going to face. Another issue –  whenever input is inaccurate, the reporting outcome will be affected accordingly.”

What does the future hold? 

As we look ahead, there is little doubt that AI will soon become a ubiquitous commodity in the Insurance realm. 

Scanbot SDK’s Stratmann admitted that he felt that AI was well on its way to becoming the true competitive differentiator in the space as we look toward the rest of 2024 and beyond.  

He said, “The future of AI in InsurTech lies in the automation of most operations, from underwriting and claims processing to customer service. This shift promises to not only increase operational efficiency, but also to provide a competitive advantage through cost reduction and improved product quality.” 

Quantee’s Holmes concurred, claiming that we are on the brink of some “amazing offerings” due to the technology’s development. 

“I believe we will see significant investment into AI in the immediate future. The drive towards efficient processes, personalised and data-driven propositions, will mean that AI is developed to become more and more intelligent and able to accommodate more areas of an insurance operation. I think we really are on the brink of some amazing offerings in the InsurTech space through smart incorporation of AI,” said Holmes.  

However, others felt that despite the attention-grabbing headlines, we are still yet to fully understand the potential AI can offer. While FullCircl‘s Gwilliam admitted that AI will see “rapid advancement” in the coming years, he stopped short of claiming it will be the best option for industry incumbents in the short-term. 

“AI will continue to advance, following Moore’s law we can expect to see rapid advancements over the next few years. However, AI is very much in the “Hype” phase at the moment, and we are likely to experience disillusionment and disappointment as people understand its true limitations and abilities. In the future, we will see rapid advancements and widespread adoption of AI, but in the meantime, a savvy investor should look at more tried and tested variations, like Natural Language processing and Robotic process automation,” suggested Gwilliam. 

It [AI] will help organisations meet the requirements of the modern age, when more data, deeper insight, and faster processes makes the difference between carving out a competitive edge and falling behind.”

Copyright © 2024 InsurTech Analyst

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