Download this white paper to learn how you can take advantage of new data sources and AI to modernize legacy customer acquisition, underwriting, claims and FNOL applications.
The insurance industry is in the middle of a radical, digitally-driven change. Customers are increasingly embracing digital channels to research and purchase insurance as well as to file claims. They expect engaging interactions, quick responses, and personalized offers. But delivering on these expectations can be a challenge.
Technologies such as connected cars, smart homes, and machine learning (ML) have enabled upstart insurers to quickly bring innovative data and analytics-based products and services to the market. Furthermore, their data-driven models have reduced customer acquisition costs, lowered underwriting risks, and reduced claims fraud, giving them a huge cost advantage.
The incumbents, who have traditionally competed on the strength of their brand, their network, and their ability to bundle offerings, are under pressure. As a result, these incumbents are embarking on a digital transformation to improve their operational efficiency, boost marketing productivity, and lower costs, while customizing products and services to meet the evolving demands of the emerging digital economy.
Data science and machine learning technologies are playing a major role in this digital transformation. According to recent research from Genpact, more than half of insurance companies are planning to transform at least some of their existing business processes using machine learning over the next three years. These include smarter underwriting decisions, better risk management, faster fraud detection, accurate pricing, and streamlined customer experiences.
Complete the form above to download a paper on application modernization and what it can mean for your company.