New insurance technology trends present both opportunities and threats to traditional market players. Successful insurers will put these tech trends at the center of their strategies and advance digital transformation efforts quickly.
One such company was NEXT Insurance, which revolutionised small business insurance by creating an online platform offering instant, accurate, and tailored quotes. By employing artificial intelligence technologies to optimize workflows across their enterprise and provide significant advantages.
Artificial Intelligence (AI)
AI technology is helping insurers improve operational efficiencies, customer service and risk management solutions while simultaneously mitigating challenges and risks associated with AI implementation. However, adopting this new technology does present its own set of difficulties and risks that need to be understood and managed appropriately by insurers.
Insurance companies are using artificial intelligence (AI) in three primary areas: customer experience, pricing and underwriting, and claims management. AI helps automate repetitive tasks and streamline processes while creating more engaging experiences for their customers.
Gen AI allows insurers to reduce costs by automating manual work and decreasing employee turnover, better assess underwriting risks and detect fraud with its large data sets analysis, manage claims efficiently by analyzing damage reports and calculating payouts with minimal human involvement, as well as manage claims efficiently using it’s damage reporting capability and calculate payouts with minimal human intervention.
Machine Learning (ML)
Machine Learning (ML) is the next revolution to transform the insurance business. Consisting of algorithms designed to learn over time, machine learning allows insurers to analyze customer data and predict customer behaviors more accurately while helping reduce fraud and loss, enhance underwriting procedures, and streamline pricing practices.
Machine learning (ML) can also be utilized for security, by detecting anomalies that could indicate data breach or suspicious activities. This feature is especially important for insurance companies which manage sensitive customer information like health and life details.
Machine learning (ML) can also help insurers to accurately anticipate customer trends and needs, enabling them to tailor their offerings and offer superior services. For instance, this tool may identify which product recommendations would be the most useful after experiencing a major life event.
Chatbots
During the COVID-19 pandemic, many insurance companies responded to market and business needs by exploiting existing technologies or developing and deploying new ones – often at considerable expense (Eling & Strandvik 2020; Nam 2018). Their initiatives were often defined by relevance and aggressiveness (Eling & Strandvik 2020; Nam 2018).
Insurance chatbots are always on call, ready to respond immediately to simple customer enquiries without needing to wait on hold for someone to call back – saving both time and allowing agents to focus on more complex matters.
Chatbots can also identify and report suspicious activity to an agent, significantly reducing fraud costs that ultimately pass onto consumers. These functions typically rely on rules-based programming or machine learning technologies; however, more advanced versions exist as well.
Internet of Things (IoT)
IoT devices are revolutionizing the insurance industry by improving customer experiences and speeding underwriting and claims processing. By collecting real-time data on insured assets, these IoT devices provide insights that help insurers mitigate risks more effectively.
IoT sensors in cars, for instance, can monitor driving behavior and offer personalized auto insurance rates. Furthermore, these solutions increase efficiency by automating repetitive tasks via Robotic Process Automation (RPA), thus eliminating human error while freeing staff up for more complex and strategic work.
IoT devices generate vast streams of information that may pose privacy risks for their owners and insurers must understand all possible ramifications before investing heavily in IoT technology. They should partner with risk management to ensure it’s used in an effective manner that doesn’t compromise client privacy.
Low-Code/No-Code Methodology
Low-code insurance platforms enable insurers to build apps quickly and cost-effectively without incurring high upfront costs associated with full technology overhaul. They also scale effortlessly to meet increased workloads while offering easy updates quickly for meeting customer demands.
As not all LC/NC platforms are created equal, insurers must carefully select an LC/NC platform with suitable capabilities for their purposes. They should check for robust integration capacities that facilitate seamless integration of existing systems; also look for platforms which enable customization of applications to avoid inefficiencies or data silos; insurers can rely on such platforms to democratize programming while decreasing IT backlogs while giving business technologists ownership over digital innovation.