How to shut out the noise and make AI work for you: an insurance deep dive

八月 29, 2024

How to shut out the noise and make AI work for you: an insurance deep dive

Arrtificial intelligence (AI) is in the process of disrupting the entire financial services value chain. Given the hype surrounding this shift, it’s key that organizations stay focused on value. In this article, we approach this topic through the lens of the insurance sub-vertical.

It’s no secret that the insurance industry is suffering from many of the same challenges faced in financial services more broadly, if not in every other major sector. Key macro-level issues include:

• Financial pressures as a result of previous and anticipated economic downturn: Predictions of future GDP contractions of 1-3% alongside more stringent regulation and changes to accounting standards have insurance leaders and chief risk officers on edge. Add in both increasing losses from more frequent and extreme catastrophes, and new emerging exposures, and insurers are seriously rethinking their ideal capital and balance sheet composition.

• Personalization means that customers can demand more from providers: Customers have more information than ever before, making it easy to shop for the personalized, tailored products they desire.

• Increasingly fierce competitors: InsurTech disruptors and technology giants are finding their niche in the value chain and playing to win. By using emerging technology to provide optimized services at lower cost bases, these organizations are winning out against traditional players.

• Workforce issues, namely staff turnover and digital talent retention: staff turnover in insurance is high, with employees frustrated at the constant battle with manual and repetitive work. There’s also a larger problem surrounding attracting and retaining top talent in roles linked to data science, artificial intelligence and digital transformation.

For budget holders facing these problems, technology can feel like a silver bullet. Ever since ChatGPT was released to the public, we’re seeing no end to the supposed business applications of cutting-edge gen AI and how it can impact the top and bottom lines. Digital winners that moved quickly in 2022 are now moving away from applications that have immaterial benefits, towards use cases that genuinely impact revenue and profit. This has culminated in a pervasive feeling amongst the slower movers of being left behind — executives are scrambling to find opportunities to invest a small amount in a novel AI solution now and save countless millions in the future. What could go wrong?

However, through our extensive work as a strategic partner to leading insurance firms, we have seen first-hand that executives in the insurance sphere are concerned their organizations are still struggling with foundational-level digital transformation issues, and definitely aren’t yet ready to realize the benefits offered by AI and its derivatives.

Within insurance, we have found the most pervasive issues when it comes to digital transformation and AI implementation to be:

Dominance of manual processes in the sector

• The insurance value chain is still propped up by laborious back-end processes which, for the most part, need to be carried out by people

• Tasks such as account and contract management, risk analysis, and claims are just a few of the manual functions that create inefficiencies and frustrate staff and customers

• Insurers often won’t have a holistic view of their processes, causing a lack of transparency and visibility and further exacerbating problems such as duplicated effort

Automation in claims and underwriting can reduce manual document handling by at least 70%

The majority of insurance firms still run on outdated systems

• Many companies are still using legacy systems rather than fit for purpose, SaaS software tools. These often aren’t set up for, or don’t integrate with, overlaying technology, meaning they aren’t compatible with advanced data analysis or AI systems

• Similarly, on-premise systems are still prevalent, with firms putting off the migration of data to the cloud. Delays to this shift are resulting in industry-wide security risks, data silos, lack of effectiveness and eventually obsolescence

Around 70-80% of an insurer’s IT budget is spent on maintaining legacy systems, which are often reliant on manual processes

Data and information continues to be a problem

• Insurance companies are collecting more information than ever

• However, firms consistently struggle with the storage, cleansing and structuring of data

• Lacking data quality prevents advanced analysis and business intelligence — resulting in a large amount of insurance firms being data-rich, but insight-poor

More than one third of insurance carriers struggle to maintain a single source of truth for data

There is a lack of change readiness in the organizational culture, particularly around data and AI

• Varying levels of understanding around the maturity and readiness of the organization, from the C-Suite down

• Difficulty attracting top talent, particularly in data science, AI and digital innovation

• Legacy practices prevent or slow the adoption of data initiatives where they are put into place, stalling an organization’s overall digital and data transformation

88% of companies with excellent change management programmes exceeded expectations, compared to only 13% of those without

Despite these challenges, there is light at the end of the tunnel. There are leading digital pioneers that are cutting through the noise, moving from hype cycle to value-driven tangible outputs via AI and machine learning.

Use case #1: Automated claims processing

InsurTech firm Lemonade has developed an innovative, machine learning-enabled system that streamlines the claims process. The technology reviews claims, cross-references them against policy details and settles them automatically.

Lemonade now handles 50% of claims using this technology and advertises that they can resolve claims in as little as 2 seconds.

Use case #2: Personalized policies

Car insurance specialist Metromile has leveraged AI, as well as telematics data from vehicles, to offer personalized and cheaper insurance policies to their customers. Metromile bases their policies on real-time usership data in addition to traditional metrics such as age, location and credit score.

Metromile claims their AI-powered products save the customer 47% on average, thus delighting customers and giving them a reason to renew. The speed of pricing data analysis also results in decreased underwriting time — again, affording employees more time to focus on non-automatable tasks.

Use case #3: Automated compliance

Aveni’s Gen AI and NLP-based technology platform will listen to and analyse customer calls to detect client vulnerabilities and highlight ways in which the service can be enhanced. Aveni Detect works as a ‘compliance checklist’, leveraging NLP to produce evidence of compliance, and Aveni Invest will auto-populate CRM systems, brief admin assistants, and generate tailored customer emails. The system will also enable targeted skills development for insurance advisors

Aveni Detect can save 50% of advisors’ time spent on new client meetings and a further 75% on ongoing advice throughout the customer relationship.

Given the exciting potential benefits of AI, what can insurance companies do to get ahead and ensure they are set up for success?

Budget holders and C-Suite level stakeholders need to assess their own progress on their digital transformation journey and ask themselves: are they really ready for AI implementation, or are they fixating without rationale on the ‘shiny new thing’?

 

They need to think about:

Moving away from… Copying what they’ve seen competitors do Running before they can walk Defaulting to AI as a silver bullet

Towards… Thinking about what makes sense for their business, based on their customer needs and business strategy / priorities Getting the right foundations in place, identifying the biggest issues, and setting out plans to address these Identifying specific use cases where AI can have the biggest impact and where the underlying infrastructure is ready

 

What to do if you don’t know where to start: Operating model assessment

Through our operating model assessment, we work collaboratively with our clients to assess their organization holistically and understand where the root problems lie. Our teams work with leadership and employees across all levels, as well as perform tech and data audits to cover the following four pillars:

• Processes and communication

• Technology

• Information, data, and insight

• Organization and people

We then evaluate the business on its to-be and as-is state, as it pertains to digital transformation. Working together with our clients, we’ll outline a vision and priority initiatives, and develop a roadmap with tangible steps to achieving these in the near, medium and long term.

What to do if you’re aware of your digital transformation progress, and want to get started: AI assessment and use case prioritisation

Previously, we laid out a framework for how firms can assess the threat posed by AI, separating these out into existential, functional, and foundational. The second part of the framework went on to explain how firms could assess themselves on a scale from laggards to winners. Going through this exercise will help you understand the AI opportunity for your organization and will help you focus your effort and investment in places that make sense rather than spending time, money and effort on listening to the noise.

We also offer a proprietary AI use case prioritisation framework, which we’ve developed through our work with clients in insurance as well as other sectors. This can help organizations who are ready to make the jump with AI, but don’t know where to focus their effort, get started.

 

Are you ready to take your business to the next level? Get in touch with our experts today.