After a recession, major markets and economies often experience a pivotal turning point in technology as the economic imperative for change sharpens. We experienced this in 2008, which ushered in the widespread adoption of cloud computing. These dramatic technological shifts then immediately begin impacting how companies and customers operate, as new business models and opportunities disrupt prior ways of working. Look no further than platform-based companies like Uber and Airbnb, which completely altered how we get around and travel.
As executive teams look beyond this pandemic and begin planning for 2021, artificial intelligence is already a key factor in many companies’ long-term strategies. In one Gartner survey of Chief Data Officers, the majority rated machine learning and AI as critical at 76 percent and 67 percent, respectively. Despite this, only 10 percent of companies are able to implement transformative end-to-end machine intelligence systems, and only 1.3 percent of insurers had invested in AI.
These percentages must change. Across all sectors, businesses expecting to succeed in 2021 with a 2019 operating model will quickly fall behind as forward-thinking companies and more innovative strategies gain a momentum swing.
The economic returns are real, as are the costs of inaction
"The pressure for organizations to adopt AI was already mounting before the crisis as the technology delivered returns to early adopters. The COVID-19 crisis has only elevated the technology’s prominence, with many companies using AI to quickly triage the vast challenges they face and set a new course for their employees, customers, and investors in an uncertain, rapidly evolving landscape." - McKinsey
A 2019 report showed a majority of executives in companies implementing AI technologies reported an uptick in business revenue and 44 percent saw reduced costs. As companies develop resilient long-term strategies in a post-COVID world, they will no doubt consider the impact these technologies and strategies have on their workforce.
While COVID-19 has served as a catalyst for significant digital change and investment, global consulting companies have been calling for more meaningful innovation and increased agility from life insurance companies for years. A shift towards streamlined operating models and data-driven transformation is essential to create points of difference, deeper customer understanding, and scalable business models. The call for agility, flexibility, and the implementation of bi-weekly working ‘sprints’ sounds more like the Olympics than competition in the life insurance industry, but it will also soon be a case of gold, silver, bronze, or oblivion.
COVID-19 will likely cost hundreds of millions their jobs, many of which are historically ‘safe’ white collar roles. Over the next few years, life insurance companies will discover which of these roles will be retained by employees, replaced or augmented by automation and machine learning tools, or ultimately deemed non-essential. This is not to say new jobs will not be emerging at an equally rapid pace, because they will. It’s also not to say many jobs can be automated completely, McKinsey estimates only 5 percent can. But the potential impact of AI and machine learning on traditional white collar roles is undeniable and significant.
Data sits at the heart of this change
Monumental annual strategy reviews, repricing, and the roll out of new products and services will become a thing of the past as we move towards proactive business models working in real time. The winners in this new reality will harness data analytics to gain deep understanding of their customers, competitors, and portfolio’s performance in the market. They will have the tools, technologies, and talent required to adapt and personalize products and services for customers, and a digital distribution model making the purchasing and claims processes seamless.
Fundamental to this is transitioning from big data to streaming data; according to Swiss Re, the shift from data lakes to ‘data rivers’ is essential to enable rapid, data-driven decision making. The US military deliberately works to shorten the battlefield decision timeframe by assimilating and assessing a plethora of data from disparate sources and presenting its commanders with options. In an increasingly competitive market, leaders of successful life insurance companies will develop the same mantra, building pipelines designed to enable data flow, analytical capabilities, and collaboration. First, many companies must address issues with data accessibility and maturity; 80 percent currently lack data that's clean and curated enough to help implement and find value from advanced technologies like AI.
Automation, AI, and machine learning are the key tools and technologies modern life insurers require to properly manage and harness this data tsunami. This won’t be easy. Research shows that 70 percent of complex, large-scale change programs don’t reach their stated goals. Regarding AI-based transformation, expert Professor Michael Jordan from UC Berkeley put it, “We should also not pretend that we are talking about something other than an engineering effort of unprecedented scale and scope.”
Data scientists, python programmers, ModelOps, and actuaries will be the essential workers insurers rely on to make sense of this wave of data and incorporate it into long-term decision making. AI projects and proof of concepts that were once ‘nice to have’ need to become operationalized and embedded in order for life insurers to achieve their data potential. Life insurance executives must ruthlessly prioritise these projects and ensure their companies have the cloud infrastructure and digital capabilities necessary to deploy and implement them. This must happen even if it means bypassing traditional IT infrastructure and systems, which is likely.
Harnessing AI technologies carefully and quickly is essential
As AI models learn and develop their ability to assess customer data, and therefore their ability to make recommendations, a better understanding and tighter regulation of these models’ inherent weights and biases is critical. Recent research from Capgemini found around 60 percent of organizations have attracted legal scrutiny and 22 percent have faced a customer backlash in the past two to three years due to decisions reached by AI systems. Data Providence will be a vital reporting tool and life insurers will need to be transparent with regards to the data used to train their models, how they impact decision making, and how they impact policyholders. AI, and life insurance, can no longer be a black box.
Model management platforms are a new industry, and cloud providers like Microsoft, Amazon, and Google are already investing billions into improving the model building, training, and deployment processes. It will soon be a matter of days and weeks, not the months and years reality many life insurers currently operate within. These tools will not replace strategy, decision making, nor the initiatives made by people. Rather, decision science platforms and tools will enable successful business leaders and their teams to access and analyze more information, make better, data-driven decisions, and implement and operationalize these actions faster than competitors.
The life insurance industry has experienced its fair share of disruption; the rise of natively digital platforms, data marketplaces, and automated underwriting are all good examples of how the industry is moving to modernize the use of new technologies. COVID-19 signals a turning point for the industry, and has accelerated the need for AI and cloud-based innovation. The shift from annual to quarterly to real time product and pricing personalization is inevitable, and life insurers must move quickly to take advantage of the many tools and technologies available to them in the market today. The race has begun, and there will only be a few winners.
About the author: Scott Houston is a member of Montoux’s Board of Directors. He was CTO for “The Lord of the Rings” trilogy in New Zealand and founded the New Zealand Supercomputer Center as well as the cloud orchestration company called GreenButton which was acquired by Microsoft. He was a finalist in the EY Entrepreneur of the year awards and named an “Innovation Hero” by the Innovation Council. He now works with AI companies while working on his first novel (about AI).