The insurance industry being very traditional in its way of functioning is faced by the increasing modern demands on the rising customer expectations, fraudulent activities, claims processing inefficiencies, and outdated risk models. It requires innovative solutions that can benefit not only in streamlining operations but also improve customers’ experience.
Do you often ask such questions to yourself as an insurance provider:
- How can I streamline these workflows to serve my customers faster while reducing mistakes?
- With the increases in operating costs, how can I remain profitable without compromising on quality of service?
- How can I cut costs while still ensuring the quality service?
- How do I reduce my fraud-related financial and reputational damage without raising my administrative burden?
The answer lies in leveraging agentic AI services.
According to a report by McKinsey & Company, AI-enabled claims management can reduce claims-processing time by up to 70% and lower the cost of claims handling by 30%.
Agentic AI represents a change force for insurers, enabling them to leverage automation, predictive analytics, and real-time data insights. It helps insurers become more competitive in a rapidly digitally transforming world, with changing customer expectations. It helps redefine the concept of operational resilience, improve customer loyalty, and ensure long-term growth.
In this blog, we will discuss how leveraging agentic AI services can transform the insurance industry by addressing key challenges and driving transformative outcomes. We will explore its use cases, look into the challenges that insurers face, how Agentic AI resolves them, and the tangible business impacts it delivers. Whether it’s increasing operational efficiency, mitigating risk, or improving customer satisfaction, this blog will explain why agentic AI is an important enabler for the future of insurance.
What is agentic AI?
Agentic AI is artificial intelligence with a decision-making ability that does not rely on human input. Agentic AI is typically defined as systems that operate with autonomous decision making, with reinforcement learning, natural language, and contextual analysis being prevalent. In contrast to traditional AI, which relies heavily on predefined rules, Agentic AI dynamically adapts to complex situations, learning and improving over time.
Agentic AI: Redefining the future artificial intelligence
Explore agentic AI in depth. Learn about the key differences between traditional and agentic AI. Understand its workflow and benefits of agentic AI.
Use cases of agentic AI for insurance
Automated claims settlement
Challenge: Manual handling of claims proves to be time-consuming and prone to mistakes. Moreover, settlements usually get delayed thereby affecting customer satisfaction.
Total losses combined with claims processing expenses account for up to 70% of the premium collected. – AWS
How agentic AI helps:
- Automates end-to-end claims lifecycle, including assessment, validation, and approval.
- Uses image recognition and natural language understanding to analyze claim documents and damaged photos instantly.
- Flags anomalies for potential fraud in real-time, which allows for faster resolution.
Business impact:
- Reduces claim processing times from weeks to days or even hours.
- Improves operational efficiency through reduction of manual touchpoints.
- Increases customer satisfaction and retention through fast settlement.
Risk assessment
Challenge: Classic models are based on static information and do not respond promptly to changing risk factors like climate change, economic unrest, or even customer’s behavior.
In a sector still defined by a high degree of manual processes and legacy systems, we expect a 10 to 30 percent increase in productivity across the risk and compliance function in insurance. – McKinsey & Company
How agentic AI helps:
- Real-time data feeds, such as IoT or geospatial data makes more accurate predictions.
- With predictive analytics, simulating possible risks, it enables policymakers to devise customized options.
- Adaptive underwriting processes allow updating risk levels in real time.
Business impact:
- Improves risk profiling accuracy. Thus, less mispriced or over-priced policy risk gets incurred.
- Attracts high-quality customers by providing personalized policy offers.
- Reduces claims payouts through proactive identification and mitigation of risks.
Fraud detection
Challenge: Insurance fraud continues to be a persistent problem, costing billions of dollars annually and affecting brand trust.
Fraudulent claims account for 5-10% of all claims and losses at a cost of ~$34 billion every year. – WNS DecisionPoint
How agentic AI helps:
- Analyzes big data to identify patterns of fraudulent behavior.
- Monitors claims submissions in real-time through anomaly detection and behavioral analytics.
- Collaborates with blockchain technology to validate transactions and the authenticity of claims.
Business impact:
- Reduces financial losses resulting from fraud.
- Strengthens regulatory compliance and internal audit processes.
- Improves the reputation of the insurer by maintaining transparency and trust.
Suggested: Role of AI in fraud prevention
Customer engagement and retention
Challenge: Standardized insurance products rarely cater to the diversity of modern customers, causing dissatisfaction.
According to Accenture, less than 29% of insurance customers are satisfied with their current providers.
How agentic AI helps:
- Utilizes customer data to provide hyper-personalized policies and recommendations.
- Engages policyholders proactively through intelligent chatbots, ensuring continuous communication.
- Uses sentiment analysis to understand customer concerns and refine offerings.
Business impact:
- Increases customer retention by creating meaningful and personalized experiences.
- Improves cross-sell and upsell opportunities with data-driven insights.
- Cultivates loyalty, since the insurer is basically a reliable partner in a customer’s financial journey.
Reduces operational costs
Challenge: The costs of manual workflows, overheads, and regulatory compliances keep piling for insurance companies.
G&A expenses typically account for approximately 20% of total operating costs in a property and casualty insurance company, and 30% in a life insurance company. – McKinsey & Company
How agentic AI helps:
- Automates administrative routine functions like updating of policies, approving claims, and reporting compliances.
- Makes resource management optimal to prevent wastages and optimize productivity.
- Improves continuous learning that lessens errors and decreases cost of reworks.
Business impact:
- Saves significantly in operational expenditure.
- Improves profitability through reduction in cost of internal operations.
- Permits scaling without corresponding hikes in cost.
Proactive policy adjustments
Challenge: Insurers are unable to keep policies relevant to policyholders’ changing circumstances, which leads to missed opportunities for customer engagement and revenue growth.
Roughly 40 percent of insurance customers who considered canceling their policy were considering doing so because they believed the policy was not necessary or did not provide sufficient value. – McKinsey & Company
How agentic AI helps:
- Continuously monitors policyholder behavior and external events, such as relocations, vehicle usage changes, or driving behavior, through IoT data and AI analytics.
- Automatically recalibrates coverage or pricing to match real-time circumstances, ensuring policies stay aligned with customer needs.
- Uses predictive modeling to anticipate future changes, offering timely recommendations.
Business impact:
- Enhances customer retention through adaptive and relevant coverage.
- Improves revenues through cross-selling and up-selling based on the proper adjustments in policy.
Dynamic underwriting
Challenge: Traditional underwriting depends on static, historic data, and therefore creates a generic policy structure. This leads to inefficient risk evaluation for many customer groups and results in incorrect pricing.
According to a report by McKinsey, real-time underwriting processes can improve operational efficiency by 30-50%.
How agentic AI helps:
- Analyzes real-time data streams about applicant behavior, market trends, and environmental factors.
- Leverages advanced machine learning algorithms to predict and classify risks dynamically.
- Automates the generation of personalized policy recommendations suited to individual needs.
Business impact
- Risk evaluation accuracy is improved in terms of underwriting loss.
- Attracts a broad section of customers with highly tailored products.
- Raises profitability by linking the premiums to the actual profile of risks.
Personalized insurance products
Challenge: Standardized insurance products fail to address the needs of the customer, thus creating dissatisfaction and loss of revenue opportunities.
Personalization most often drives 10 to 15 percent revenue lift (with company-specific lift spanning 5 to 25 percent, driven by sector and ability to execute). – McKinsey & Company
How agentic AI helps:
- Builds a detailed profile of the customer using data from multiple sources such as social media, IoT devices, and purchase history.
- AI-driven insights help predict individual preferences and risk tolerance.
- Dynamically creates customized policies, such as pay-as-you-go car insurance or health plans based on lifestyle and fitness data.
- Continuously updates product recommendations based on changes in customer circumstances.
Business impact:
- Personalized policies encourage loyalty by catering to specific customer needs.
- Increase in revenue through cross-selling and upselling.
- Demonstrate customer-first approach that gives the insurer an edge above all competitors.
- Operational efficiency with automatic product customization, decreasing effort and speeding up time-to-market.
Suggested: To know more about why agentic AI is the key to future-proofing your business, click here.
Key benefits of agentic AI in insurance: Why you should leverage agentic AI services
- Personalized experience for customers: Tailored policies and real-time adjustments enhance customer satisfaction and retention.
- Efficiency gains: Automates underwriting and claims processing tasks to help reduce costs and speed up workflows.
- Fraud detection: Detects fraudulent transactions through sophisticated pattern recognition to prevent huge financial losses.
- Risk management: Analyzes real-time data for accurate risk assessment and dynamic pricing.
- Revenue growth: Promotes cross-selling and upselling with data-driven recommendations.
- Scalability: Adapts quickly to market changes and handles high data volumes seamlessly.
Suggested: Top use cases of AI virtual assistants across various industries
Adoption strategy for agentic AI in insurance
Assess organization readiness
Before embarking on implementation, it is important to assess the existing infrastructure, workforce skills, and data ecosystem. This will ensure that the company is ready to utilize AI effectively.
- Infrastructure assessment: Check if your IT systems, data storage, and processing capabilities are adequate for AI deployment.
- Workforce readiness: Identify gaps in employees’ skills and invest in training to create an AI adoption culture.
Pilot small-scale projects
Start small to avoid risks and create strong insights on how the scalability of AI can go forward in the organization.
- Target use cases: Select a high-impact yet manageable region, such as claims processing, or fraud detection for ease of starting.
- Measure success KPIs such as:
- Processing times
- Cost saving
- Accuracy
Set data foundation
Agentic AI feeds off quality data. Insurers must focus on building an efficient data infrastructure that underpins the smooth running of AI operations.
- Data integration: Integrate data from different systems into a centralized repository that includes legacy systems.
- Data cleaning and labeling: Datasets should be accurate, complete, and structured for accurate machine learning models.
Partner with experts
Partnering with AI consulting companies like Softweb Solutions can make the process simpler and ensure best practices.
- Vendor selection: Select suppliers who have a history of insurance-specific AI solutions.
- Co-creation: Collaborate with partners that offer advanced AI capabilities to tailor AI models to your business needs.
Emphasize on change management
Change in AI implementation can be met with resistance from employees or customers. A good change management plan helps alleviate such fears and increases adoption.
- Stakeholder buy-in: Explain the value of AI to executives, employees, and customers.
- Ongoing training: Provide continuous learning programs to help employees adapt to new workflows.
Ensure compliance and ethics
AI systems must align with regulatory standards and ethical guidelines to avoid legal or reputational risks.
- Regulatory compliance: Use the expertise of legal teams to comply with local and international regulations like GDPR and HIPAA.
- Bias minimization: Conduct routine audits on AI models to identify bias.
Scale and optimize
Once a pilot project is proven, roll out AI across a range of business functions and continually evolve the processes.
- Phased rollout: Deploy agentic AI solutions in underwriting, customer service, and risk management.
- Continuous improvement: Implement employ feedback loops and performance metrics to improve precision and efficiency of AI over time.
AI system monitoring and maintenance
AI systems must be constantly monitored to continue working effectively and meeting evolving business requirements.
- Performance monitoring: Periodically measure KPIs to evaluate how AI is impacting operational efficiency and customer satisfaction.
- Model updates: Regularly retrain AI models with new data to maintain their relevance and accuracy.
Embrace the future of insurance with agentic AI
The insurance industry is on the brink of a revolutionary shift, and agentic AI is spearheading the change. With the potential to automate risk evaluation, streamline claims, and offer customized policies, this technology will eliminate inefficiencies and create growth. As competition increases and customer expectations grow, leveraging agentic AI services is no longer an option-it’s a necessity. Now is the moment to redefine how insurance operates; the future of this business will be determined by people who take on this technology.
Softweb Solutions can help insurance firms unlock the full potential of agentic AI by offering customized solutions for dynamic underwriting, automated claims processing, and personalized policy management. Our expertise in AI consulting services enables insurers to streamline processes, enhance customer experiences, and drive cost-effective growth. Connect with our experts to know more.
