3 reasons insurers should implement AI into core systems
From underwriting to claims processing, the insurance industry is on the cusp of a seismic shift. Traditional processes which rely on rules-based engines are being replaced by artificial intelligence and deep learning models – condensing weeks of work into a matter of days or even minutes.
For many insurance companies, the AI revolution is already well underway in claims processing and distribution – particularly around fraud detection. Accurately detecting fraud depends on being able to analyze huge quantities of data to spot anomalies. So, replacing multiple databases, pivot tables and spreadsheets with dynamic AI-based applications is an obvious place for insurers to start their AI journey.
But fraud detection isn’t the only area that can benefit from an injection of AI. As digital transformation across all sectors continues, it’s clear that insurers must modernize every part of their back office. This is where AI shines. Not only can AI-based applications process huge quantities of unstructured data in seconds, it’s also much quicker to adapt to new scenarios – like the recent telehealth revolution, or growing insurance losses stemming from the increase in natural disasters.
1. AI costs less and can achieve better business outcomes
In a nutshell, AI can help insurers achieve better outcomes across the entire claims lifecycle by transforming how they handle data. Most insurers collect huge amounts of data from claimants and much of it never gets analyzed to its fullest extent. When data is analyzed in order to process individual claims, it can take days or weeks of employee labor using manual tools. With AI, this takes hours or minutes – vastly speeding up outcomes for customers and easing the tedious workload on your employees.
As well as enhancing how insurance companies deal with individual claims, AI can transform the underwriting process and enhance strategic decision-making across the business. How? By taking all the data collected by insurers and analyzing it at scale to uncover previously hidden patterns and spot trends as they develop in near real time. In PwC’s most recent annual AI business survey, 60% of U.S. business and technology executives said that implementing AI improved their internal decision-making, and 47% said it led to increased productivity and saved them money.
2. It can enhance employee experience and help retain talent
With AI helping to do some of the more tedious ‘legwork’, insurance employees can spend more time on high value cases, leading to increased ROI, enhanced overall job satisfaction, and lower churn rates. Prioritizing the employee experience is more important than ever, given increased levels of burnout, combined with the ‘great resignation’ and ‘great retirement’ in the insurance industry.
As well as helping to retain existing talent, AI can remove some of the burden of hiring new employees. The insurance workforce of tomorrow requires top-notch talent with the right skills. That’s hard to aim for when over half of insurers are looking to hire and almost half a million insurance employees are expected to retire soon. In this diminished talent pool, some insurers are looking to replicate the skills of their best employees by using AI algorithms to build machine learning models that can ‘think’ like their best investigator, claims handler, or underwriter.
However, insurers should remember that AI works best when used in tandem with insurance analysts’ in-depth expertise and human connection. It can help figure out the ‘what’ (such as when fraud is being perpetrated) but often can’t understand the ‘why’ without human interpretation – arguably the more important question at hand. What’s more, AI is only as foolproof as the humans behind it. For instance, when designed correctly, it can help remove underwriters’ bias around immutable characteristics like gender or race in claims fraud and underwriting risk. But, when designed poorly, it can perpetuate these biases.
3. AI is the secret to increasing customer satisfaction
Over half (57%) of consumers report spending more with brands that have worked to gain and retain their loyalty. With this in mind, it’s clear that prioritizing customer experience can pay dividends for insurance companies.
You might not immediately associate AI-based technologies like intelligent decisioning engines with enhanced customer satisfaction. But almost two thirds of respondents to PwC’s AI business survey reported enhanced customer experience as an outcome of their AI implementation. To illustrate this, let’s compare how a car accident claim would typically play out, both with and without intelligent decisioning.
The situation: a claim comes in for a two-car accident with minor damage and no injuries. It’s been flagged as a potential case of fraud. So, over the next two weeks, the claims processing team inputs the case details into one system to detect any patterns of fraud, manually adding handwritten notes, such as the repair estimate. The findings of the claims team are then compared with outsourced information, such as corroborating documents like police reports. Ultimately, it’s correctly determined not to be fraud – but the customer has been left with a damaged car for weeks.
With AI-based intelligent decisioning in place, a system ensures information is correctly submitted and sends out an alarm to a specific claim handler with crucial context like ‘police report is missing timeline.’ Once the claim handler confirms the missing timeline on the police report, it’s uploaded to the system and all relevant information is automatically inputted into the claims document for final review. Within days, the claim handler can confidently determine that there’s been no fraud and the policyholder’s claim is advanced swiftly – a big plus for customer satisfaction.
The bottom line? To thrive in today’s rapidly evolving insurance landscape, insurers will need to re-examine all their back-end systems to transform how they’re handling data. Long-term success hinges on ensuring AI, machine learning, and deep learning models work seamlessly alongside expert teams.
