The insurance sector continues to grow while simultaneously experiencing challenges aggravated by multiple factors, including the pandemic. These challenges affect the sector's business models, services, and profitability.
Unfortunately, the conventional methods of underwriting insurance policies are time intensive. Insurance customers expect faster policy decisions and a seamless experience. As a result, underwriters are under pressure to price risk more swiftly and accurately.
Therefore, to improve life insurance underwriting's productivity, insurers must upgrade their core system and embrace new-age technologies. Here is an in-depth analysis of the top life emerging insurance underwriting trends poised to change the face of underwriting.
1. Big Data Analytics
The use of big data analytics in the insurance sector is increasing. Insurance companies are expected to have a $56.97 billion expenditure by the end of 2022. But those same companies want their spending to be more efficient. Data and advanced analytics can assist insurance companies in saving the time and resources used in insurance underwriting by automating the processes.
Insurers can work more efficiently on client accounts due to big data technology. They can:
- Review the customers' history
- Choose appropriate risk classes
- Develop pricing models
- Automate claims processing
- Offer top-notch services
In addition, insurers can collect and analyze data using futuristic technologies like IoT devices like sensors and wearables.
Advanced data and predictive analytics systems assist insurance companies in making data-driven business decisions. For example, underwriters can leverage high-level data and social media data and information to price risk more effectively.
Companies that have invested in big data analytics have noticed a 60% increase in fraud detection rates, 40%-70% cost savings, and 30% more efficiency. Big data analytics benefits both insurance companies and customers, allowing insurers to target clients more precisely.
2. Artificial Intelligence (AI) and the Internet of Things (IoT)
AI and IoT are fundamentally disrupting and transforming the insurance underwriting landscape. The future of underwriting will differ heavily from how it’s seen today for personal and small-business offerings across casualty and life and property insurance products.
The process is reduced to a few seconds once underwriting is automated and supported by a combination of machine models designed within the technology stack. Such models are powered by internal data and a wide set of external data accessed via application programming interfaces, external data, and analytics providers.
Pricing becomes available in real time depending on usage and a dynamic, data-rich risk assessment. This empowers insurance clients to decide how their actions influence coverage, insurability, and pricing. It is why 56% of insurance executives are confident that AI will improve operational efficiency.
P&C (property and casualty) insurers price risk by studying the statistical models of previous loss trends and scenarios. The Internet of Things creates a situation where real-time data is part of the risk assessment. Additionally, the IoT can introduce fresh, insightful data types to the business of assessing and calculating risk.
Data generated by IoT devices and smart wearables can assist in seamlessly tracking and analyzing consumer behavior and preferences. Alternative data subject to regulations allows for the dynamic pricing of insurance policies. It also will enable underwriters to reduce premium rates, offer discounts, and provide cashback and bundled offerings depending on the client's needs and preferences.
Telematics from vehicles, wearable-equipped workers, and smart facilities allow underwriters to conduct more accurate risk assessments in a short time. Sophisticated sensors also monitor different environmental factors—like humidity, temperature, and smoke—to prevent property damage. More importantly, IoT devices can help mitigate fraud and minimize costs significantly by providing accurate and reliable data.
3. Machine Learning and Underwriting
Insurance underwriting involves navigating massive data amounts, file formats, and software to calculate policy premiums. This can be time-consuming and costly. Machine Learning (ML) can help underwriters classify cases as standard or substandard risks while dealing with the related complexities.
Machine Learning can help underwriters to determine quantifiable risk factors since training goes only as far as helping to keep risk scoring consistently. In addition, machine learning algorithms can make underwriting more cost-effective and elevate accuracy levels.
Machine Learning's prospective applications in insurance underwriting include:
- Understanding risk appetite
- Fraud detection
- Expense management
- Premium leakage
As such, insurers can embrace Machine Learning to stay competitive and increase operational excellence. Machine Learning, AI, and IoT are growing insurance underwriting trends since they share common goals for users.
4. The Platform Approach
The complexity of the underwriting process needs to be reduced to elevate underwriters' productivity. A platform with low-code capability augmented by RPA (Robotics Process Automation) capabilities can minimize human intervention and automate the end-to-end policy underwriting process.
Furthermore, AI-powered systems can assess applicants' data automatically. The system can also automatically detect and raise concerns against suspicious cases. This helps in saving time on challenging tasks like data entry, duplicity checks, and authentication.
In addition, skill-based case allocation allows underwriters to focus on where human intelligence is needed. Low-code process automation helps insurance companies improve the underwriting process's efficiency and correctness. Insurers can leverage the low-code process automation on the cloud, on the premises, or via hybrid deployments.
Essentially, the perfect platform can change insurers' experiences by bridging process, content, and communication silos in a company. The platform's abilities — for example, mobility, AI, blockchain, RPA, cloud, analytics, and social analytics capabilities — can accelerate an insurer's journey and give them a competitive edge.
5. The Evolution of the Underwriter
Because of these insurance underwriting trends, the underwriter's role is evolving. The required skill set constantly shifts as data sets grow and process automation goes on the rise. Cultivating the proper talent is becoming as important as implementing the right data and technology.
Evolving underwriting talent is becoming more dynamic in order to include different skill sets, such as behavioral economics, data science, etc. As a result, there is currently a need for insurance underwriters who can think out of the box.
With the change of tools in the industry, such as AI, IoT, and Machine Learning, there is the need to acquire underwriters who can grow with these tools. Fortunately, most insurers are discovering and implementing the best ways to have the best and evolved underwriting talent on their teams. These include:
- Capitalizing on their built-in underwriting expertise
- Recruiting a different kind of talent
Take the Next Step to Incorporate Emerging Insurance Underwriting Trends
Evolving insurance marketing trends give insurers a competitive edge and better marketing positioning. Pilotbird empowers insurers to engage customers and help in fraud detection. With decades of experience and expertise, Pilotbird is better poised to help insurers embrace and implement these emerging insurance underwriting trends.
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