REINVENTING INSURANCE IN THE DIGITAL AGE

Limited data and extensive manual validation was the hallmark of traditional actuarial modeling. More sophisticated data models can be built today capturing structured and unstructured data to predict claim frequency, claim value, and a host of other outcomes accurately. Whereas in the past actuaries relied on their experience for risk management and cost optimization, today they can rely on a variety of models to develop profitable products and determine optimal pricing. Machine learning speeds the entire process from modeling to insight.

From life expectancy estimations based on actuarial formulas, underwriting has moved to rigorous risk profiling and evidence-based decision-making. Rich customer data drawn from multiple data sources, including medical devices and social media, has made precise individual risk calculations possible. Pricing algorithms can generate dynamic rates based on customer behavior, rewarding good/low-risk behaviors with cheaper quotes or better coverage. Automation has further reduced the discovery-to-quote time from days to minutes.

Fast-tracking claims processing and creating a seamless customer experience have both cost-saving and reputational benefits. Data extraction, entry, and validation—the most labor-intensive processes at the heart of claims handling—can be automated combining Optical character recognition (OCR), natural language processing (NLP), and robotic process automation (RPA). The benefits of claims automation are manifold: Fewer human errors, faster processing and settlement, lower costs, and significantly happier customers.

Combating fraud, which deeply undercuts profits, is a top priority for insurers. Heuristic models and rules-based systems have guided insurers for decades but as fraud itself is becoming more sophisticated, insurers have to widen their search beyond known fraudulent scenarios. Machine learning algorithms can unearth hidden correlations in data and help perform advanced threat analysis. Predictive models based on Artificial Neural Networks (ANN) further enable insurers to step up their vigilance.

Insurers who understand the value of customer satisfaction are rushing to fill the vacuum with simpler digital products, automated underwriting, straight-through processing (STP), self-service chatbots, and omnichannel experience. Such a customer-centric approach is new to insurance but it is the new competitive proposition, particularly where price differences are nonexistent. This can be achieved through data-enriched and technology-driven solutions that focus on smoothing insurer-customer interactions, speeding claim settlement, and creating transparent processes.

Marketing based purely on customer demographics or life stages is out. Targeted marketing based on customer segmentation is in. Modern customer acquisition and retention strategies are tailored to microsegments, which can be unearthed using advanced clustering techniques. High-value clients and prospects for upselling and cross-selling can be identified through customer analytics merging data from various touchpoints and social media. Since the search for products almost always starts online, insurers also need to relook their digital marketing strategy and tune it for various geographies and segments.