The Good Tech Companies - Lahari Pandiri Advocates Usage-Based Insurance Leveraging AI and Big Data

Episode Date: June 17, 2025

This story was originally published on HackerNoon at: https://hackernoon.com/lahari-pandiri-advocates-usage-based-insurance-leveraging-ai-and-big-data. Lahari Pandiri proposes AI and Big Data to power usage-based insurance, enabling real-time risk profiling, dynamic pricing, and automated claims processing. Check more stories related to business at: https://hackernoon.com/c/business. You can also check exclusive content about #usage-based-insurance, #lahari-pandiri, #ai-in-auto-insurance, #real-time-risk-profiling, #big-data-insurance, #telematics-insurance-model, #claims-automation-ai, #good-company, and more. This story was written by: @jonstojanjournalist. Learn more about this writer by checking @jonstojanjournalist's about page, and for more stories, please visit hackernoon.com. Lahari Pandiri champions AI and Big Data in usage-based auto insurance to personalize premiums, enhance risk profiling, and automate claims. Her framework leverages telematics, predictive modeling, and explainable AI for fairer, faster, and more transparent insurance services, redefining the future of digital insurance.

This audio is presented by Hacker Noon, where anyone can learn anything about any technology. Lahari Pandiri advocates usage-based insurance leveraging AI and big data. By John Stoyan, journalist. In today's business landscape where personalized services and intelligent automation have emerged as buzzwords, the insurance sector has often been at the forefront of these innovations. Lahari Pandiri, an avid researcher and technologist, recommends widespread adoption of usage-based insurance, UBI, powered by artificial intelligence, AI, and big data for redefining how insurers approach critical tasks such as risk assessment, premium calculation, and claims processing

in real time. In her research paper titled, Leveraging AI and Big Data for Real-Time Risk Profiling and Claims Processing, a case study on usage-based auto insurance, she has elaborated how predictive analytics, machine learning algorithms, and telematics data can streamline and modernize auto insurance operations. Her work provides a practical roadmap for transitioning from traditional actuarial model-stow behavior drivendriven and dynamic systems that prioritize efficiency, fairness, and responsiveness. The evolution of auto insurance long rooted in actuarial tradition, auto insurance has typically relied on broad statistical groupings to assign premiums and determine risk. In this approach, the primary indicators of risk are location, gender, vehicle type, age, and historical claims data. Though it is systematic, this approach doesn't take the nuances of individual driving behavior

into consideration. As a result, it can reward riskier behavior when it remains masked by favorable historical data. Similarly, it can inadvertently penalize safe drivers within high-risk demographic categories. To overcome the limitations of this legacy framework, Pandiri recommends a shift toward dynamic, behavior-based models enabled by real-time analytics and telematics.

Usage-based insurance, UBI, calculates premiums not just on the basis of past actions. It also takes current driving habits into account, recorded through GPS systems, mobile applications, and onboard diagnostics. This real-time data creates a feedback loop where the driver's financial outcomes are directly influenced by their behavior, which encourages safer driving habits and allows insurers to align premiums with actual exposure to risk. Real-time risk profiling with AI and big data The synergistic power of artificial intelligence

and big data forms the core of Lahari Pandiri's proposed transformation. Working in tandem, these technologies allow users to unlock deeper insights into individual driving patterns and risk factors by deep diving into real-time behavioral analytics. By process high velocity data streams from telematics devices, deep learning, decision trees, and neural networks can capture variables like braking, acceleration, lane changes, and weather or road conditions. Then this data is structured, interpreted, and fed into predictive models capable of generating individualized risk scores. In her framework, Pandiri has leveraged this

continuous loop of data collection and analyses to enhance the ability of the insurers to identify high-risk zones, forecast accident probabilities, and refine pricing strategies. Through her research, she has also introduced a cascaded risk assessment model. This model assesses severity and accident topologies by extracting a driver's behavioral attributes through AI feature engineering and then passing them through layered analyses. AI-powered automation for transforming claims processing in addition to assessing risk, Pandiri's work also addresses inefficiencies in claims management by integrating eye-driven automation into the claims lifecycle. Her research paper details a case study where a 50% improvement in operational efficiency and a 90% reduction in turnaround time were

achieved by implementing AI in claims handling. Using pattern recognition algorithms, insurers can validate accident scenarios, detect frauds, and flag anomalies in claim submissions. Moreover, key details can be extracted automatically from accident reports, images, and repair invoices by integrating computer vision and natural language processing, NLP. This ensures rule-based and consistent decision-making while speeding up adjudication. Ethical considerations in her paper, Pandiri has also discussed the ethical obligations that come with deploying these transformative technologies. She believes that transparency isth most critical of those issues. AI models using deep learning techniques are often condemned for their black box nature.

In these models, the rationale behind decisions is not easily explainable. This can lead to serious legal and ethical challenges because many data protection regulations grant individuals the right to an explanation for algorithmic decisions affecting them. Pandiri addresses this problem by transitioning to white box AI systems designed to be explainable. These may include interpretable machine learning models such as rule-based classifiers, decision trees or hybrid models capable of balancing performance with clarity. To help trace the logic behind every output, she emphasizes embedding explainability tools

within the AI workflow. Future directions at a time when insurers are under tremendous pressure to up their game, the research by Lahari Pandiri offers a practical blueprint for delivering more personalized, efficient, and transparent services leveraging AI and big data. She envisions further enhancements in real-time decision-making and insurance through deeper integration of the Internet of Vehicles, I.O.V., edge computing, and ethical A.I. As the insurance industry embraces the era of digital transformation, the integration of A.I. and big data is not merely an innovation, it is a necessity. Usage-based models powered by intelligent systems offer a path to fairer pricing, faster

claims, and proactive risk management," she explains. War Research shows that the future of insurance lies in systems that can learn, adapt, and respond in real time to individual behaviors. This shift will not only redefine customer expectations but also recalibrate the industry's operational and ethical standards. Thank you for listening to this Hacker Noon story, read by Artificial Intelligence. Visit HackerNoon.com to read, write, learn and publish.