CIOInsider India Magazine
Revolutionizing Insurance: AI's Impact on Underwriting, Fraud Detection and Claims Processing
Saurabh Tiwari is a leader who brings insight from both technical and business perspectives in Information Technology. He has been responsible for tech innovations and providing a seamless customer experience to visitors to the website. Prior to joining Policybazaar, Saurabh served as Vice President of Technology at GEP Worldwide. He has also held leadership roles with IBM India and MCSB Systems.
Artificial Intelligence (AI) and Machine Learning (ML) have swiftly integrated into the insurance landscape, transforming traditional practices and ushering in a new era of efficiency and precision. In a comprehensive interview with an industry expert, insights unfold regarding the pivotal role of AI in underwriting, fraud detection, claims processing, and the challenges and future prospects that lie ahead for the insurance sector.
The integration of AI in insurance underwriting processes proves highly beneficial, aiding in identifying cases that could potentially increase loss ratios. Utilizing machine learning AI models allows proactive identification and scrutiny of these cases, ultimately enhancing the overall business portfolio. However, the efficacy of AI models heavily relies on the quality and size of the data set used for training. Small data sets might lead to false positives, posing inherent risks. To mitigate this, employing models that flag cases for human scrutiny could reduce risks associated with erroneous AI predictions. Integrating human oversight alongside AI intervention stands as a viable method to alleviate potential risks in underwriting decisions.
Enhancing Fraud Detection through Machine Learning Models
Fraud detection holds paramount importance in the insurance sector due to its significant impact on reducing loss ratios. In various insurance domains such as life insurance, instances of fraud arise when an individual purchases a term policy under false pretense. Implementing machine learning models that verify the vitality of a person during policy purchase offers a viable solution to address this issue. This will take account of the person’s overall state of health to facilitate better underwriting. Moreover, in general insurance like motor claims, fraudulent activities often occur where claimants manipulate damage levels. AI models play a crucial role here by accurately assessing damage levels, minimizing fraudulent claims facilitated by fabricated images or videos. Additionally, in health insurance, anomalies in
payment patterns or multiple policy purchases under different identities can be swiftly identified through AI models, aiding in early fraud detection and prevention across insurance domains. Overall, AI models substantially contribute to reducing loss ratios by detecting and preventing fraudulent activities in the insurance industry.
AI's Impact on Claims Processing Efficiency
In the realm of claims processing, automation driven by AI expedites the assessment process, fostering swift and accurate claim evaluations. The implementation of proper automation significantly enhances claim processing efficiency within the insurance realm. Delayed processing negatively impacts customer experience, emphasizing the critical role of automation, predominantly driven by AI technology. For instance, in motor insurance, AI-enabled systems analyze submitted accident videos to precisely identify and assess damage, expediting claim evaluations. Similarly, within health insurance, AI assists in swiftly evaluating and verifying claims, which otherwise involve manual scrutiny of paper-based bills by third-party administrators (TPAs). By quickly assessing bill details, AI ensures prompt and accurate settlements for genuine claims, vital for enhancing the overall claim processing speed. While AI models enhance efficiency, ensuring accuracy requires human oversight. Regular scrutiny and adjustments to AI models by human experts are crucial to achieve higher accuracy in claim assessments, blending the strengths of AI and human input for improved overall system accuracy.
The integration of AI in insurance underwriting processes proves highly beneficial, aiding in identifying cases that could potentially increase loss ratios.
In traditional insurance processes, a major obstacle lies in the scarcity of available data, hindering effective AI and ML practices. To bolster these practices, comprehensive digitization becomes imperative, requiring extensive behavioral consumer data that reflects the customer's journey. The lack of such detailed data significantly impacts the potential of AI and ML in insurance. Digitization stands out as a primary solution to augmenting these practices. Additionally, while relying on AI and ML models, a significant challenge arises concerning explainability. Models are constructed on datasets influencing specific outcomes, prompting queries about why particular responses are generated. Explainability becomes crucial, often necessitating reliance on substantial datasets to elucidate possible reasons for specific outcomes. The adequacy of the dataset directly influences the credibility of outcomes, demanding the selection of large datasets to comprehend and explicate model results, a complex task with frequent complexities.
Future Trends and Advancements in AI/ML Shaping Insurance
The recent months have witnessed a significant adoption of Gen AI, reflecting its increasing value within the industry. This trend indicates a shift towards a multi-modal approach within AI and ML practices in the insurance sector. As AI and ML technologies mature, and data analytics assumes a central role in insurance practices, the outcome appears to be a substantial improvement in product formation, especially from a customer-centric perspective. The future trends suggest a move towards personalized product design, leveraging the advancements in AI, particularly in large language models like Gen AI, to cater to individual customer needs effectively.
Amidst the growing availability of data for refining models and enhancing data science practices, an area that demands critical attention from Chief Information Officers (CIOs) is the security of information. Despite the industry's strides in innovation and leveraging data, maintaining a robust data security infrastructure remains a paramount challenge. Continuous efforts towards instituting high governance measures around data are crucial. The emphasis on tightening the security grip around data should persist, given the ever-evolving nature of threats that may expose vulnerabilities, necessitating an unwavering focus on data security.
