Mike Tyson famously said that “Everyone has a plan until they get punched in the mouth”. Every company had a strategic plan coming into 2020. Then, Covid-19 walked into the ring.
Insurance has been hit hard by Covid-19 and economic hardship. With many insurers focused on cash conservation, **leading insurers can emerge from the crisis even stronger if they make smart investments in AI. **Insurers’ massive customer datasets and their famously manual processes create some ‘quick win’ AI opportunities.
Insurers must proactively adopt AI because the business outlook isn’t great. Lloyd’s of London estimates that 2020 underwriting losses will hit $107 billion. (Re)insurance firms are looking at billions in claims for business interruption and trade credit insurance losses (insurance for when buyers can’t pay sellers).
Insurance policy sales will be affected by falling economic activity. Production and movement of goods and commodities stalled. That means fewer companies need insurance for cargo, energy, commodities, shipping, etc.
Many insurers will emerge shaken from the Coronavirus era. They will then walk right into the worst recession since the Great Depression according to the IMF, which will reduce demand for some types of personal and commercial insurance
Why AI Now?
At first glance, a post-pandemic economy might not seem the best time to invest in artificial intelligence. After all, shouldn’t firms preserve as much capital as possible?
In fact, this is an opportune time to develop AI capabilities. AI is good at automating logical, repetitive processes and generating insights from data. This allows insurers cut costs and discover new revenue streams. The maturity of AI software vendors means that AI tools need not be built from scratch. If insurers understand their business needs and develop an AI strategy to meet those needs, small investments in AI can achieve high ROI.
Large insurers have been investing in digital transformation for years. Converting data into digital format, embracing mobile & web-based customer interaction, and upgrading tech stacks has given these firms the infrastructure to adopt AI quickly. Digital transformation has laid the groundwork for AI transformation.
Insurance Business Priorities
Insurers should focus on three priorities in a post-pandemic economy: cost reduction, risk reduction and customer insights.
Covid-19 and the economic downturn will affect insurers in the revenue (underwriting income) and expense (claims) columns in the short term. The immediate business priorities for insurance companies are cost and risk reduction until the market picks up.
For cost reduction, insurers should prioritize process and claims automation. They will streamline workflows so that the same work can be done with fewer people. AI tools such as intelligent Robotic Process Automation (RPA) will be relevant here.
For risk reduction, insurers will invest in better fraud detection tools since fraud is expected to increase during hard economic times. According to the Association of British Insurers (ABI), the 2008 recession saw a 17% increase in fraudulent insurance claims compared to 2007.
Insurers will also reduce risks by improving underwriting standards. This means understanding risks better and insuring higher quality risks to prevent large unanticipated claims. Machine learning and Natural Language Processing (NLP) tools can search through past insurance policies and understand how to price new policies.
Insurers can then focus on preserving revenue streams and discovering new revenue sources. Investing in AI for customer insights will yield significant ROI because insurers have large amounts of customer data. Enterprise search software powered by** machine vision **can quickly search through internal databases and document repositories to give agents and customer service staff a 360-degree view of customers.
AI for Cost Reduction: Claims Automation with Intelligent RPA
**Insurers can use intelligent RPA to automate claims processing to cut costs and pay claims more quickly. **Claims handling is rife with challenges that intelligent RPA can solve, such as manual data input, multiple data sources (documents, emails, images, mobile apps) and time-intensive decision making.
TraditionalRPA software automates manual and repetitive tasks without using AI. It simply records and replicates employee actions and mouse clicks to generate an invoice or report, for example. This only works if the process never changes. Most traditional RPA tools must be updated when invoice layouts or reporting requirements change, for instance.
Intelligent RPA systems from vendors such as UiPath and Automation Anywhere addmachine learning, NLP and machine vision to RPA tools. Instead of just replicating human action, intelligent RPA finds the most efficient way to automate tasks while dealing with new data and changing requirements.
AI-enhanced RPA can reduce time and cost across three phases in the claims life cycle: data input, validation, and adjudication.
#insurance #machine-learning #covid19 #coronavirus #artificial-intelligence #deep learning
