In this three-part blog series, we’ll explore three key areas where AI decisioning delivers the most significant return on investment and business impact for insurance companies: operational efficiency, fraud detection, and compliance. AI decisioning platforms automate complex decisions using machine learning, process automation, and a business rules engine. We’ll begin with an overview of operational efficiency gains, then dive into fraud detection and compliance management, two strategic areas ripe for efficiency improvements.
- Part 2: Decisioning in Insurance Fraud Detection
- Part 3: How AI Decisioning Platforms Enable Insurance Compliance
- E-book Revolutionizing Insurance with an AI Decisioning Platform
How AI Decisioning Fuels Operational Efficiencies in Insurance
The only constant in the insurance industry these days is change–and it’s coming from all directions. Record-high inflation and interest rates, ever-mounting climate-related losses, geopolitical unrest, velocity of cyberattacks, and rising costs of claims are making business lines more unpredictable and creating more complex risks.
Concurrently, customers and competitors are becoming more empowered through digitalization and AI. These technologies generate enormous amounts of real-time data for consumers and allow digitally native companies to move faster than ever before.
The good news is that these market conditions can provide growth opportunities if insurers are in a position to take advantage of them. However, these market developments challenge the insurance industry’s historic risk-averse management culture. The age-old responses to market challenges of premium hikes and dropping coverage are not enough in today’s market. Insurers need to evolve technologically and operationally by modernizing and streamlining their infrastructure and processes to be more agile, resilient, and innovative.
AI decisioning is fast becoming a solution that insurance business leaders are adopting for operational impact. While insurers have been working with decision tools for several years, it wasn’t until recently that insurance companies realized the value of strategically embedding automated decisioning and business rules engines throughout the organization and value chain using solutions like an AI decisioning platform.
What is AI Decisioning?
AI decisioning platforms combine business rules engines, machine learning, and process automation to automate complex decision-making processes. By integrating vast amounts of data from various sources, applying sophisticated algorithms, and generating insights, these platforms enable faster, more accurate decisions across multiple insurance operations.
AI decisioning platforms are flexible, low-code tools that enable actuaries, underwriters, and other subject matter experts to easily build, test, and deploy automated decision logic, all while remaining in control of decision-making and business outcomes.
AI decisioning platforms include several key components that facilitate optimizing decisions and streamlining operations. At the solution’s core is a business rules engine that allows business leaders and domain experts to define, adjust, and manage decision logic in a transparent, business-friendly language.
Machine learning enhances decision accuracy, predicts customer behavior, and helps insurers perform several critical functions based on data-driven insights. Process automation streamlines repetitive tasks and flows, such as underwriting and claims management, to reduce manual work and minimize errors.
Finally, integrating the platform with other enterprise software, such as a CRM, ensures smooth data flow across systems and allows companies to extract more data from their current investments.
Operational Efficiencies Gained Through AI Decisioning
Manual processes across the value chain and customer lifecycle have long burdened insurance companies. AI decisioning enables efficiency gains for insurers by eliminating repetitive manual tasks, analyzing complex data quickly, and automating decisions. The result is increased productivity, process acceleration, and reduced costs associated with human time, errors, and risks.
Five areas ripe for efficiency gains include:
- Underwriting: AI decisioning platforms rapidly evaluate various underwriting criteria and make decisions almost instantly. They enable underwriters to build, iterate, and adjust complex automated underwriting decision flows much faster than the traditional manual review approach without writing code or waiting for IT to make changes. Enabling the underwriting team reduces the turnaround time for policy approvals, risks exposure, and associated costs.
- Claims management: AI decisioning significantly accelerates the claims processing workflow by optimizing human-in-the-loop processes, automating routine decisions, speeding up claims assessments and payouts, and automatically flagging potential fraud. Automation also reduces the chances of inaccurate claim payments and other costly human errors.
- Customer service: Insurance companies today are vulnerable to customer attrition. With a very high cost of customer acquisition, customer satisfaction and retention is critical. By accelerating and improving services such as quotations, claims processing, policy approval, and document delivery, agents and customer service reps can focus on solving more complex customer problems for customers in a personalized way. Better service improves overall customer satisfaction and reduces customer attrition.
- Decision-making: AI decisioning platforms process and analyze vast amounts of structured and unstructured data, providing insurers with deeper insights for more consistent, optimized, and agile decision-making. A data-driven decision approach leads to more accurate risk management, pricing, and portfolio management.
- IT: Insurers can reduce development and maintenance costs of rules-based applications by as much as 20% to 50% through freeing up IT and development time. With low-code logic capabilities, business users no longer need IT programming time, which can take several weeks, to make decision logic changes. This significantly reduces time to market for new customer-facing applications and improves market responsiveness.
Insurance Operations Business Cases
Several insurance companies have successfully implemented AI decisioning systems and are seeing the bottom-line impact of AI decisioning. Through cost and time savings and accelerated decision-making, many companies have experienced a payback period of less than six months after implementing AI-decisioning. Aon has reduced application development and maintenance costs for its custom policy management system by using InRule to encode, verify, and maintain its core application decision logic. The company reduced development time for one of its insurance coverage rating programs from 120 hours to just 14 by enabling actuaries to build and test logic independently.
Learn more about Aon’s success with AI decisioning.
Embrace Pet Insurance replaced its entirely manual and error-prone paid claims calculations, which were run in Excel, with InRule’s AI decisioning claims management capabilities. This switch reduced the time required to adjudicate a claim from more than five minutes to less than one minute, resulting in an average of $6,000 claims-related savings per month.
“In our case, it would have taken a developer 6-9 months to do what we did in 6 weeks using InRule. More significantly, we can make changes on the fly as needed.”
Melissa Ing, Business Process Manager/Claims Manager
Learn more about Embrace Pet Insurance’s success with AI decisioning.
Hollard Insurance Group has also been able to centralize and structure its complex rules using InRule AI Decisioning, accelerating processes and responses to market changes and reducing turnaround times drastically. Whereas they previously needed several days or weeks to make changes to decision rules, with AI Decisioning, they can now typically make changes in less than an hour.
“Before InRule, developers had to interpret complex insurance rules and code them into Applications. That created several back-and-forth processes to end up where we needed to be. We’re much faster and more accurate when completing a project with InRule at our disposal.”
Simba Chinyani – Business Rules Architect
Learn more about Hollard’s success with AI decisioning.
Tokio Marine HCC’s Cyber and Professional Lines Group faced significant challenges with their legacy underwriting platform, which required extensive configuration using hard-coded logic. The platform created long change cycles and relied on outdated rating logic processes. Now, more than 90% of the Tokio Marine Cyber and Professional Lines business runs on the InRule Decision Platform. Running thousands of rules every hour, the unit has automated Tokio Marine’s underwriting and rating processes, minimized errors, and enhanced the consistency of decision-making across the organization.
Learn more about Tokio Marine’s success with AI decisioning.
These operational improvement results demonstrate the significant impact on operational metrics and financial indicators that AI decisioning can deliver. From reduced processing times to increased accuracy and customer satisfaction, the business case for AI decisioning in insurance operations is compelling.
For leading insurers, this technology is becoming increasingly crucial in staying competitive and achieving key business objectives. By leveraging AI decisioning, they are transforming their operations, driving both efficiency and growth across multiple facets of their business.
Ready to learn more about the transformative power of AI decisioning in insurance? Download our comprehensive ebook Revolutionizing Insurance with an AI Decisioning Platform for an in-depth guide on how to unlock the benefits of this tool. Or come by our booth #2176 at ITC Las Vegas where Tokio Marine and other industry experts will share their insights and experiences with AI decisioning systems.
Stay tuned for our next blog in this series, where we’ll explore how AI decisioning is revolutionizing fraud detection in the insurance industry, helping companies save millions while protecting their customers and reputation.
