In recent years, the insurance industry has witnessed a significant transformation driven by Artificial Intelligence (AI) and Machine Learning (ML), resulting in digital-led insurance solutions across various use cases, including automated underwriting, claims processing, and fraud detection. In part, this can be attributed to the evolving needs and expectations of policyholders and employees waking up to a fast-paced, interconnected world.
The penetration of AI and ML applications across the insurance value chain has brought about a paradigm shift on both the demand and supply sides of the insurance value chain.
Here’s how the supply side is being reshaped:
Underwriting and risk assessment: Underwriters can analyze diverse data sources, including claims data, social media, telematics, and IoT, for accurate risk assessment, premium provisions, and transparency.
Claims processing: Automated workflows can expedite secure claims settlement by analyzing claim details, documentation, and historical data.
Service quality and personalization: AI-powered chatbots provide 24×7 support by handling queries contextually, assisting with policy management, and delivering customized experiences.
Customer retention and revenue opportunities: By leveraging demographic and purchase data, insurers can predict behavior, identify cross-selling or upselling opportunities, and optimize marketing efforts.
Risk management: AI can extract relevant risk information, identify underwriting risks, and optimize risk selection, thereby reducing allocated loss adjusting expenses (ALAE) in claims.
On the demand side of the insurance continuum, AI and ML can help in simplifying the customer journey through:
Personalized experiences and add-ons: According to a study, 85% of customers prefer personalized insurance services. AI and ML algorithms can analyze customer data, including driving habits, health metrics, and property characteristics, to suggest personalized products, leading to informed decisions, cost savings, and improved self-protection.
Reduced turnaround time: Interactive portals and automated workflows expedite policy approval and claims settlement. For instance, AI-led onboarding applications or claims processing can help in improving overall speed and accuracy.
Enhanced risk management: AI and ML deployments offer real-time insights and recommendations, helping customers mitigate risks proactively. Examples include accident prevention warnings based on driving speed, suggestions for preventive health measures, and highlighting potential property risks before purchase.
Building customer-centric insurance journeys
In moving beyond just ‘offering products or services,’ insurers must also focus on enhancing the overall insurance processing journey. This allows them to create tailored products with personalized pricing and targeted risk strategies, where improved underwriting accuracy can also enhance customer satisfaction.
Imagine this scenario: Stella encounters an accident while driving to her office, and she knows that registering her auto claim through the insurance IVR service is a lengthy process. The claim settlement usually takes 7-15 days due to paperwork and procedures. In an alternate scenario, Stella uses HTC’s iFNOL accelerator. She uploads images of her damaged vehicle, which the AI/ML platform analyzes and categorizes instantly. It provides real-time claim approvals and accurate cost estimates. This results in a hassle-free experience and better repair cost estimate for Stella.
Fueling process efficiency through AI/ML-led iFNOL Solution
HTC’s AI-enabled iFNOL (First Notice of Loss) solution integrates with core and ancillary insurance systems. The cloud-ready, custom-built accelerator enables customers to create an FNOL in real time, even with limited information. It also handles telematics data and offers unique features such as roadside assistance, towing support, and other claims-related services. So how does the iFNOL solution elevate process efficiency?
Omnichannel access and claims opportunities: Interactive channels that handle claims requests, including e-mail and image uploads via APIs.
Chatbots: AI-driven chatbots assist with tasks of varying complexity, thereby improving communication and enhancing the customer journey.
Computer vision: The uploaded images are analyzed and matched with previous incidents for efficient vehicle damage and repair cost assessments through AI adjudication.
Optical character recognition (OCR): Unstructured customer data, including handwritten documents, can be extracted and converted into a structured format, which simplifies processing.
IoT/telematics: Screens the location, time, and date of a car accident and verifies information through a smart vehicle’s memory.
While HTC’s iFNOL solution takes care of simple to mid-complex tasks, claim adjusters can devote their time and undivided attention to more complex claims. It speeds up the claim intake processes wherein insurers can have it both ways: happy customers and reduced costs.
Potential benefits of AI-enabled iFNOL process
Insurance stakeholders have much at stake, with policyholders prioritizing ease of buying and multiple benefits. The iFNOL solution offers various benefits that can enhance process efficiency and customer satisfaction.
Negligible-to-zero intervention: With automated and STP-based claims processing and payment disbursement, human intervention is reduced significantly, and by extension, manual errors are minimized.
Improved operational speed: Automated workflows can minimize errors, resulting in faster claims processing and enhanced system efficacy. AI/ML-led Fast Track Claims improves claims processes by 3X to 7X, reducing average processing time from 7-15 days to 1-2 days.
Reduced risk exposure and cost: Effective data analysis enables faster, more accurate insights for risk assessment, fraud detection, and efficient operations. Automated claims processing reduces costs by 40-50% per claim.