AI, big data and ‘us’

AI, BIG DATA AND ‘US’ ‘US’

As insurance speeds towards hyper-automation, Aniqah Majid weighs up the benefits and bear traps

Automation has been imperative to the insurance industry for decades. With the inception of ACORD, the sector’s standards-setting organisation, in 1970, and the IBM personal computer, large-scale machine processing used by insurance firms and independent agents was an obvious response to rapid consumer growth and demand.

Aviva was one of the first to bring automation to scale with its pensions division in the early 2010s. It employed a low-code, Cloud-based task management platform provided by Appian that integrated with the data contained in its legacy infrastructure and was used to match employee skills to claims cases. The insurer saw a 40 per cent increase in efficiency, with customer queries resolved in a matter of minutes. Encouraged by the results, and as the insurance landscape became more competitive, Aviva went on to install 40 automated applications, all of them running on top of existing infrastructure in pensions, health and general insurance. The ball was rolling and others followed. Between 2016 and 2021, the amount insurance companies spent on IT increased by around 650 per cent, according to Statista. Now, the arms race is between AI and robotic process automation (RPA).

MOVING UP A GEAR

Used together, AI and RPA herald the new era of hyper-automation.

“It’s about automating non-standard variables,” Leon Fretz, the financial services director of insurance and investment at Microsoft UK, explained during a recent webinar. “(It’s about processing) outside of the normal factors, that require more intelligence and insight than robotic processing automation [alone].”

In real life, that translates in motor insurance, for example, to RPA assessing individual factors around a claim while the likelihood of fraud as part of the risk assessment is predicted through AI.

A report released last year from McKinsey, Insurance 2030 – The Impact Of AI On The Future Of Insurance, presented a controversial image of hyper-automation, claiming that by the end of this decade, virtually all decisions an insurance company makes, from underwriting to claims processing, will be informed by AI. It based that projection on the inexorable desire to prioritise customer experience and maximise profits, all the while cutting costs. Insurers, said McKinsey, will ensure this through the adoption of big data tools and specialised learning models.

Bastiaan De Goei, the insurance industry leader at Instabase, explains: “Unlike the first document-understanding solutions, which were template-based or rules-based, today’s deep learning models understand a document’s context and content in its raw form and can process highly variable and complex documents without human intervention. They become smarter over time, generalising their learnings across diverse document types and evolving, using human-in-the-loop processes.”

Instabase allows insurers to develop fully-automated business processing applications with the use of unstructured data. The platform extracts and digitises crucial customer documents, which insurers can use to automate any paper-based process, such as claims.

“The paper-intensive and process-driven work culture in the insurance industry make it the right candidate for automation,” adds Shashi Bhargava, EVP head of the product and solutions group at Datamatics.

“Policy issuance, document verification, premium calculation, customer onboarding, claims processing and claims validation are some of the popular automation use cases for insurance companies. Automation will ensure faster turnaround and better compliance along with tighter cost control, and help insurance companies stay competitive.”

Datamatics, which uses AI and robotics to enhance its business management services, recently partnered with Parascript to offer accessible and accurate document recognition software.

The area said to benefit most from intelligent automation is claims processing – by far the largest and most complex job of the insurer, from handling claim requests to assessing the loss and making settlements, it straddles a number of traditionally siloed departments and their data. Historically, this point-to-point process would take anywhere between a couple of weeks and a couple of months to complete. Hyper-automation looks to streamline it, utilising AI to gather and verify information, and integrated RPA to process it in a matter of days or hours.

The absence of human intervention in the back office – from the first notice of loss (FNOL) to the claim settlement, otherwise known as touchless claims processing – has been led by companies including Quadient and Genpact. A further step, taken by Guidewire, among others, is to offer virtual claims assessments to policyholders and loss adjusters. With the AI-based software company Plnar, Guidewire can assess and manage claims using 3D models of rooms and damage, which it develops from photographs taken by an insured’s or loss adjuster’s tablet or mobile phone, that is combined with policyholder data.

Automated claims processing has been shown to dramatically speed up settlement time while achieving consistent accuracy and eliminating a huge number of manual interventions. In early 2021, a trial by Zurich in partnership with Sprout.ai, cut property claims settlement to under 24 hours by introducing automated policy checking, using natural language processing (NLP) and knowledge graphs.

According to McKinsey, by employing advanced automation, insurers can cut the overall cost of a claims journey by 30 per cent.

“Modern automation platforms like Instabase allow insurers to develop and implement such solutions quickly,” De Goei explains. “The platform has more than 140 ready-to-use building blocks such as digitisation, splitting, classification or extracting. These building blocks can be stitched together and easily create a customised workflow. In its own right, this is very useful and effectively allows an organisation to tackle nearly any complex workflow that today relies on manual document review.”

This level of efficiency has already infused big insurtechs like Shift and Lemonade, the latter of which has taken another important step in using AI to mitigate predictive discrimination.

A human workforce is still crucial to the industry. Survey after survey indicates that customers of all ages value the interaction

Skeptics have sounded the alarm on the customer-facing aspect of the automation process, warning that the algorithmic nature of AI processing can cultivate a specific bias towards a narrow demographic (archetypal white male). The concern over AI bias is justified to some degree; the tool has repeatedly teetered towards the red zone in job recruitment and social media advertising, which unfairly screens out some groups.

The main issue here, and one which continues to contaminate the use of AI in insurance, is the lack of comprehensive, unbiased data. A change in the type of data insurers use for processing will strongly benefit their automation efforts, and re-win the trust of customers.

But what about the trust of staff? The industry has already seen job numbers impacted by hyper-automation, and yet a 2019 PwC report, Financial Services: Preparing For Tomorrow’s Workforce Today, found that the biggest barrier to digital innovation was not technology, but the lack of skilled teams and a shortage of talent. A human workforce is still crucial to the industry in other ways. Survey after survey indicates that customers of all ages value the interaction. Perhaps the bigger dilemma is not the loss of staff but the training needed to prepare them for an increasingly human/AI blended workspace.

De Goei describes how automation can act as a subordinate tool for professionals: “The automation of rote tasks such as finding data points, manually transcribing data into a downstream system, asking for missing information or checking for duplicates, allows highly qualified adjusters and underwriters to focus on their core tasks of adjusting and underwriting.

“We found this leads to higher employee satisfaction and retention. It also allows these critical employees to focus more on their customers, whether they face off to brokers or policyholders. Along with higher accuracy and faster processes, this additional client focus leads to significantly higher customer satisfaction.”

Given the rate at which technology is developing, reskilling is as much an imperative to insurance as automation is.

There is some cause for optimism. Mercer’s 2021 Global Talent Trends –Insurance Industry Outlook report, found that this industry is one-and-a-half times more likely than others to focus on developing skills related to automation. The report shared that the majority of insurers did intend to implement some form of upskilling: 63 per cent had identified, or planned to identify, new skills and capabilities for post-COVID operations.

The goal of automation was never to outpace the expertise of the agent, but to facilitate them. The advent of hyper-automation does not change this. Be it customer preference or speed of facilitation, both man and machine are needed in the insurance space; together they create equilibrium.