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Analytics: 5 Cutting-Edge Use Cases

IndiaFirst Life Insurance’s CTO, Sankaranarayanan Raghavan, reveals how predictive analytics spearheads a transformative journey. Experience the unfolding of visionary insights in this dynamic exploration:

How many times have you watched a movie that popped up on your OTT platform as a recommendation? Have you ever considered the technology that powers your viewing experience and how it predicts your interest based on your previous selections?” prompts Sankaranarayanan Raghavan, drawing parallels between personalized movie suggestions and the transformative potential of predictive analytics in the insurance industry

Reflecting on the evolution of movie-watching habits, Raghavan shares his journey from theaters to the current era where technology allows individuals to choose movies from any country, genre, or language at their convenience. This sets the stage for a vision – utilizing similar technology to redefine the insurance experience for stakeholders at IndiaFirst Life, emphasizing a commitment to creating a hyper-personalized connection between the company and its clients.

5 Cutting-Edge Use Cases

IndiaFirst Life Insurance has leveraged advanced data and analytics capabilities to drive predictive modelling across various key areas, revolutionizing the way they engage with customers and manage risk. Sankaranarayanan reveals the top 5 use cases where these capabilities have made a significant impact:

1. Renewal Premium Prediction: Our predictive models analyze customer data to determine the likelihood of premium renewal. This insight drives targeted engagement plans, ensuring proactive communication with customers who may be at risk of non-renewal.

2. Claim Probability Assessment: By analyzing new business data, we predict the probability of future claims. This enables us to anticipate potential risks and adjust our strategies accordingly.

3. Protection Policy Issuance Probability: Advanced analytics helps us identify the likelihood of issuance for protection policies, allowing us to streamline the underwriting process and improve efficiency.

4. Litigation Complaint Identification: Our models identify complaints with a high probability of escalating into litigation. This proactive approach enables us to address issues early and mitigate legal risks.

5. Litigation Winning Probability: Through data analytics, we assess the likelihood of winning litigation cases, enabling us to allocate resources effectively and optimize legal strategies.

With these use cases demonstrate the transformative power of data analytics in company’s operations, Sankaranarayanan says: “We are continuously exploring ways to enhance our capabilities further. We have upcoming plans to refine our fraud identification models in both claims and new business domains. Additionally, we are looking to incorporate unstructured data into our analytical framework to gain deeper insights and improve decision-making.”

Deep Learning & Analytics

In the realm of insurance, Raghavan highlights the multifaceted applications of advanced data and analytics capabilities, driven by predictive modeling. “The tech allows us to reimagine the way we service customers, recruit and train employees, market and distribute products, evaluate risks, detect frauds, and even underwrite and issue policies,” he states.

Predictive models, compared to neural networks, derive strength from rich data originating from diverse sources, including industry history, proprietary data, third-party data, and AI-generated insights.

Customer-Centric Innovation

Delving into the essence of life insurance, Raghavan emphasizes the role of technology in aiding customers during their lives and beyond. He underlines the necessity for insurance companies to sell the right product based on customer profiles and needs. AI-based predictive models, according to him, play a pivotal role across the customer lifecycle, from assessing propensity to buy to hyper-personalizing marketing collateral and issuing policies instantly.

Raghavan illustrates this with an example: “If a customer is an agriculturist from Punjab, the marketing brochure will talk about an agriculturist’s investment and protection needs.”

Looking ahead, Sankaranarayanan envisions data analytics playing an increasingly pivotal role in the insurance industry’s evolution. As technology continues to advance, he anticipates the emergence of large language models and generative AI as game-changers in the industry. “These innovations will not only break down language barriers but also enhance trust by providing unbiased inputs. Overall, data analytics will continue to drive innovation, improve customer experiences, and enable insurers to stay ahead in an ever-evolving landscape,” he concludes.

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