The difference between data collection and true digital transformation is analysis. Multiple layers of manual data processing make customers wait and employees frustrated as they try to make sense of complex spreadsheets and documents. Organizations can unlock the door to analytical insights when they use data and artificial intelligence (AI) to drive workflow and decision making in policy and claims processes. Ultimately, this will make an organization more adaptable to market conditions and more responsive to consumer needs.
Here are some potential use cases for harnessing AI in insurance:
1. AI and Claims
Claims processing is a critical operation for insurers and one of the most often cited as a source of pain for customers. Use of AI holds the potential for improvements on both fronts. According to the Forbes article, Harnessing the Power of AI in the Insurance Sector, insurance companies implementing AI into their claim validation processes have seen up to 99.99% improved claims accuracy, a 60% increase in operational efficiency, and a 95% improved customer experience.
AI in the form of machine learning models can be used for predictive analytics to speed up claim settlement timelines by completing the upfront evaluation of incoming claims to determine validity, severity, and next steps. While the algorithm may determine, based on the data and programmed conditions, that a claim requires agent review, smaller and more unremarkable claims would be eligible for straight-through processing, requiring no human intervention.
AI also can identify unusual patterns and anomalies in data that might indicate misrepresentations on insurance claims. This helps insurers identify fraud and reduces the overall risk in their portfolio.
When AI is integrated into the insurer’s workflow through the core system, automation can propel a claim forward. The predicted outcome for this given claim, actions taken, and actual outcome also become data points for future scoring and decision-making.
2. AI and Policy Underwriting
AI can enhance workflow automation by adding an extra layer of data investigation and insight to routine underwriting tasks such as data entry, evaluation based on the company’s appetite for risk, policy verification, and administrative processes that move a new application through onboarding. Underwriters are then free to focus on more complex cases, making the turnaround time for these quotes quicker as well.
The following are some ways AI can be applied to enhance underwriting:
- Data Analysis and Predictive Modeling: AI can process vast amounts of data from various sources, including social media, internet-connected devices, and historical insurance data. By analyzing this data, AI can identify patterns and trends that humans might miss, leading to more accurate risk assessments and better predictive modeling for determining policy pricing.
- Risk Assessment and Pricing: Properly trained AI-powered algorithms can analyze an individual or a business’s risk profile comprehensively. This includes factors beyond traditional demographics, such as online behavior, purchasing patterns, and real-time data from connected devices. This enables insurers to personalize policies and pricing more accurately, reflecting the specific risk exposure of each prospective customer.
- Decision Support Systems: AI-powered systems can assist human underwriters by providing recommendations and insights based on historical data and current trends. This helps underwriters make more informed decisions and reduces the potential for human biases.
Customer Experience: With more accurate risk assessments, personalized policies, and streamlined processes, AI can enhance the overall customer experience. Customers can receive more tailored offerings and quicker responses to inquiries.
3. AI and Risk Mitigation
The relatively untapped area for expansion for insurers is risk mitigation and AI’s ability to guide businesses around or through potential threats.
The integration of connected devices further amplifies the capabilities of AI-driven risk management. For example, these devices can provide granular data on factors like heat indexes, pollen levels, and wildfire incidents. By fusing this data with location-specific information, AI can identify historical patterns and forecast potential risks. Additionally, AI can identify anomalies or spikes in environmental indices and then correlate these findings with historical claims data, enabling insurers to predict future risks and take preemptive measures to mitigate their impact. This same intelligence could be applied to different industries, such as healthcare, and provide an insurer the ability to assess a population to anticipate underwriting needs and claims predictability.
Claims, underwriting, and risk mitigation are just three areas in which insurers can utilize AI to improve analytical capabilities. To enable these analytical insights, insurers must first evaluate the technologies they have in place and the degree to which those technologies integrate with one another. Flexible, modern core systems enable insurers to not only access their data in real-time but also integrate third-party AI technologies quickly through custom application program interfaces (APIs).
For further information on how insurers can clean up their organizations’ data to make way for AI, read our “Get Your House In Order For…AI” e-book.