Artificial Intelligence (AI) has revolutionized claims assessment by enhancing speed, accuracy, and efficiency.
Automation has expedited settlements, reduced labor dependency, and minimized operational costs, while AI’s fraud detection capabilities ensure integrity in claim approvals
Artificial Intelligence (AI) has revolutionized claims assessment by enhancing speed, accuracy, and efficiency.
“For example in health insurance, AI examines hospital bills and compares them to standard treatment costs to detect anomalies and ensure fair payout,” says KV Dipu, senior president & head-operations & customer service, Bajaj Allianz General Insurance.
Also, AI minimizes biases and human errors, ensuring policyholders receive fair settlements. For example, our AI models analyze historical data, policy terms, and market rates to offer precise compensation.
Similarly, insurers have integrated AI-powered tools that assess motor insurance claims through image recognition. Customers can simply upload photos of their damaged vehicles via our app, and the AI estimates repair costs up to a certain amount within seconds. “This approach reduces the average claim processing time from seven days to mere hours and significantly reduces operational costs. McKinsey reports that insurers leveraging AI in claims processing can reduce costs by 25-30 per cent. Automating routine tasks like document verification and fraud detection allows human resources to focus on complex cases, further optimizing expenses,” says Dipu.
“Automation has expedited settlements, reduced labor dependency, and minimized operational costs, while AI’s fraud detection capabilities ensure integrity in claim approvals. AI is no longer a luxury—it’s the cornerstone of modern claims management,” says Sharad Mathur, MD and CEO, Universal Sompo General Insurance.
Challenges Of Using AI In Insurance
Integrating AI into the insurance industry offers great potential but comes with several challenges.
Data Quality And Quantity: AI’s effectiveness depends on vast, high-quality data. Incomplete or fragmented customer data can hinder its potential. To overcome this, it's crucial to implement robust data consolidation strategies and use predictive analytics to fill gaps, ensuring AI can deliver accurate results.
Change Management: Resistance from employees and intermediaries is common when introducing AI. Addressing this requires clear training programs and highlighting AI’s benefits, such as faster turnaround times and improved workflows.
High Initial Investment: AI tools often involve significant upfront costs. However, these costs are justified by the long-term benefits, including reduced fraud, improved operational efficiency, and enhanced customer experiences. Investing in AI now leads to long-term gains that make the investment worthwhile.
“We mitigate these issues through robust data governance, workforce training, and a hybrid approach that blends automation with human oversight to manage complex claims. For the industry at large, AI has empowered insurers to settle claims faster and fairer, ensuring a customer-centric approach while protecting against fraud,” says Mathur.