By Kannan Amaresh, Senior Vice President & Global Head, Insurance at Infosys
The onset of the Covid-19 pandemic forced organizations across industries to shift to remote working, resulting in rapid and pervasive digitalization. Insurers, too, are accelerating their AI adoption timelines to bring in efficiencies, support the distributed workforce, and sustain remote operations. AI is estimated to contribute as much as US $15 trillion to the world economy by 2030.
Avenues for AI in Insurance
While there have been several pilot implementations for AI in insurance, enterprises are only just scratching the surface. The opportunity posed by the pandemic is giving impetus to deploy AI in insurance across areas such as:
- Pricing – Deep personalization enabled through AI makes policy pricing much more competitive as insurers are able to tailor premiums based on insights gleaned from a customer-centric focus
- Claims handling – Manual and time-consuming processes around claims management and payouts can be automated for faster time to market
- Fraud prevention – Fraud losses cost insurers nearly US $40 billion per year. AI can play a pivotal role in spotting abnormalities and identifying false information
- Customer experience – Applying AI models to customer data gives insurers deeper understanding of consumer needs and facilitates ongoing support to reduce costs and improve customer experience
The Speed-Accuracy-Trust Framework
Until now, insurers have been focusing their AI and automation initiatives to drive efficiency outcomes. Looking ahead, the focus should shift towards a framework that drives speed, accuracy, and trust. This will allow insurers to differentiate themselves and provide a better customer experience.
For instance, robotic process automation can help achieve speed. The explosion of data from connected devices along with increasing adoption of open source and data systems will improve accuracy. Advances in cognitive tech will drive trust by making AI understandable.
Insurers today are already experimenting with emerging technologies such as big data. However, areas like robotic process automation (RPA) and chatbots need more maturity to better manage risk and enable cost-effective operations.
AI in Three Key Insurance Segments
The use-cases of AI in insurance must be designed specifically for its different segments, as each of these have unique operating models. The success lever within each of these segments is the proliferation of partnerships among different players within the insurance and technology ecosystem. These are driving some exceptional outcomes as described below:
- Property and Casualty (P&C)
This segment is replete with examples of partnerships aimed at enhancing capabilities for greater efficiency, faster time to market, and profitability. The key focus areas for AI in P&C are:
- Voice recognition, unstructured text, and machine learning
- Geospatial data
- Usage-based insurance and life expectancy
- Drone surveillance and IoT
For example, USAA plans to acquire Noblr, an InsurTech company, to accelerate innovation with the right technology and enter the increasingly popular usage-based insurance market. Another recent acquisition, that of Safeauto by Allstate, will add distribution capabilities that accelerate go-to-market while keeping costs in check.
- Life and Annuity (L&A)
Acquisitions and partnerships are helping traditional insurers simplify processes and save time to achieve top line efficiencies. This aligns with the speed, accuracy, and trust framework. Moreover, the explosion in the health data generated from wearables provides new opportunities for insurers. Some recent examples include Swiss Re’s partnership with Appian that combines Swiss Re’s software with the workflow capabilities of Appian to increase efficiency while maintaining accuracy. In another example, Scor and HealthyHealth have entered into a partnership that captures ‘user well-being’ information from smartphones and wearables from HealthyHealth. This is then leveraged by the insurer to evaluate critical medical circumstances.
- Commercial Insurance
As a market that generated over US $690 billion in 2020, commercial insurance has become very competitive (1). In the wake of the pandemic, commercial insurers need to reimagine their business models and accelerate customer onboarding processes. They should also identify the right sources of data and understand how to leverage these efficiently. AI can play a crucial role in automating processes, reducing costs, and saving time. Data can be used to help predict customer behavior, understand preferences, and optimize price and product offerings. It will also provide a comprehensive view of relevant and accurate information throughout the new business and underwriting lifecycle for faster decision-making. Various partnerships in this space have put customer experience at the core. For example, Lloyd’s agreement with geospatial InsurTech firm McKenzie Intelligence Services enables faster payments and claim settlements for an enhanced customer experience. It has helped Lloyd build an advanced digital and technology-led insurance marketplace.
With the right strategy and execution, AI has the potential to help the insurance industry transform rapidly by leveraging capabilities like RPA, IoT and data analytics. Infosys has enabled several global insurance majors to accelerate their digitalization journeys through its platforms, solutions, and services. For instance, Infosys Nia leverages AI, ML and automation to improve insurer-client engagement and ensure return on investment at reduced risk. Infosys also provides tailored AI-powered products for commercial insurance that help insurance carriers improve underwriting productivity, efficiency, and speed. It also facilitates accurate policy quotes based on the carrier’s risk appetite for higher profitability. Coupled with the speed-accuracy-trust framework, AI is set to help the insurance industry evolve and thrive in the post-pandemic world.