The insurance sector is very unique across many dimensions. It is an industry bound by strict regulations, rigid data privacy and a stable, ‘if it ain’t broke, don’t fix it’ perspective, that calls for risk management and overall caution first. Compared to more unrestrained sectors, the insurance industry requires a well thought out approach to digital transformation with proven, established methodologies.
Yet, almost overnight, the world has changed. First the pandemic, and now the conflict in Ukraine have created global uncertainty and upheaval. Many countries are staring into the abyss of recession, with interest rates and the cost of living rapidly increasing. While high interest rates present opportunities for insurers to maximize returns and maintain shareholder value, new market entrants, committed to finding innovative ways to deliver customer-friendly insurance policies, are forcing a reappraisal of an industry that has always advocated for a cautious approach to change.
Customers are also demanding innovation. How, when and why they buy their insurance products, and an unprecedented amount of choice, is driving a need for fundamental change to remain competitive. This doesn’t mean sacrificing short term profit for long term growth.
It’s about creating value – something that established insurance players have been somewhat remiss at doing. Global management consultants McKinsey’s research suggests that as many as 1/5 of global insurers ‘destroy significant value,’ while only 20% generate positive returns for their shareholders.
The choice to revolutionize how an insurance company makes its decisions may feel like an expensive, resource-intensive minefield. Yet the transformative nature of using tools such as transparent AI and machine learning can empower actuaries and insurance organizations to deliver dynamic, competitive services and pricing, and maximize the power of leveraged data to make better business decisions and offer fairer, more varied and competitive services to their customers. Sophisticated pricing teams will empower insurers to quickly react and adapt to changes and make the most of them.
New Players, New Market Threats
Early adopters of AI and big data technologies have already had a significant impact on the insurance industry. Big Tech (think Google, Facebook, Apple and Amazon) have been making data-led decisions for many years, and have turned their focus to the insurance space, with a ready-made, global customer base and a wealth of user data to inform their decision making.
In a study published in CapGemini’s World Insurance Report 2020, 36% of customers say that they are willing to purchase insurance from Big Tech firms, vs. just 16% in 2016. This is putting tremendous pressure on historical insurance carriers.
Adding to this, insurtechs are creating entirely new ways of interacting with customers, raising policy holders’ standards and accelerating customer expectations. New market players provide customer-friendly, socially responsible and equitable insurance which fits with a modern consumer’s values and ethics, using AI to transform their customers’ journeys and remove long-held barriers to participation. The entire customer journey is based on real-time data, all of which makes buying insurance both engaging and deeply personal in ways that are currently unmatched by established, stalwart insurers.
In fact, according to Dan Schreiber, CEO of Lemonade, (a company which describes itself not only as an insurer, but as a next-generation AI company), his company collects around 100 times more data than a traditional insurer. This data is used to better target and serve customers, upending historical industry standards to deliver fast, seamless and personalized insurance products and services.
Compare this to the old ways of doing business – in CapGemini’s World Insurance Report 2020, less than 30% of insurers said that they felt that websites and mobile apps were worthwhile sales drivers or that their websites were useful for sharing policy information with customers. Fifty percent of those same respondents said that product fit was not necessarily critical to experience-led engagement. CapGemini made a very real point: “That these responses signal a gap in insurers’ understanding of their customers, and a failure to realize the power of online platforms for sharing information.”
Personalization is also exploding in usage-based insurance, which is estimated to be worth $125 billion by 2027. Over half of respondents in the same CapGemini World Insurance Report 2020 wanted usage-based insurance, in line with the rise of telematics, health tracking data and more.
Ultimately, the changing landscape of technology gives customers greater choice than ever before. At the touch of a button, they can choose to do business with organizations that reflect their values and give a level of service that traditional insurers who don’t acknowledge this shift will fail to keep pace with.
Leveraging data to deliver this service is no longer a ‘nice to have’ – it’s essential to remain competitive. Trust, personalization and engagement are key to the future of insurance.
Data is great… if you know what to do with it
It feels like stating the obvious, but more data = better decisions.
While the whole world seemingly embraces data as a tool to transform how we do business, the reality is, that unless that data is used correctly, it remains either worthless, or missing its full potential. Incumbent carriers need to accelerate deploying AI and machine learning to stay in the game. The insurance industry has been somewhat slow in unlocking the ways in which data can transform how business is done, and has lagged behind others when adopting AI at scale. Some of the challenges the insurance industry has experience include:
- Legacy IT systems
- Identifying the data to capture
- Determining how to securely store and manage the data
- Understanding how to use the data in a way that fulfills the many obligations of regulatory restrictions
- Knowing how to use AI and machine learning to process this data to generate fair, unbiased results
Yet, regardless of these challenges, leveraging AI and data is a land of opportunity for the insurance industry.
Opportunity to provide almost instant quotes. To offer more targeted (and thus more attractive), personalized pricing. To unearth exciting new revenue streams that were previously inaccessible. To forge new and diverse customer relationships that are not based on a long meeting in a broker’s office, but in an online environment. To identify claims risks more accurately before they happen, and price for them accordingly.
Interestingly enough, 87% of insurance carriers are investing more than $5m in AI-related technologies per year, illustrating their willingness to accelerate this adoption.
IDC Research believes that: “… the data created over the next three years will exceed that created in the last 30.” But what do you do with it? Even storing it becomes complex, let alone implementing it to empower decision making.
AI gives insurers the ability to make faster, more accurate decisions based on current situations. To do this successfully, the skills and knowledge of the actuary are fundamental to ensuring the right data points are used to make the right decisions. AI learns from its inputs, so building models that operate both fairly and transparently is crucial.
The cornerstones of privacy, fairness, equity, explainability and impact assessments need to be met to ensure that transparent AI for insurance is fit for purpose.
Using technology for data and AI needs also comes with significant cost savings. Sleeker, more efficient and time-saving benefits can be realized through technology. Lingering challenges like allaying high-risk applications, which can be more rigorously screened for and treated fairly, but in a way that mitigates the risk for insurers.
In fact, analysis shows that AI leaders generate five times the amount of ROI than firms that are further behind on their AI journey, and 22% of firms state that at least 5% of their earnings in 2019 were attributable to AI. Organizations that adopt AI now will survive and thrive, while those that lag will fall behind.
Key technology enablers are driving at-scale transformation:
- The ability to harness data: this is increasingly powerful as more data sources emerge, such as telematics for cars, the rise of wearable devices for health or behavioral data, and connected CCTV and security
- The use of cloud-based solutions: these allow for unlimited computing power, substantial flexibility and guaranteed Many leading global providers of integrated insurance software like Duck Creek and Guidewire are gradually moving away from the on-premises world to focus on the cloud.
- The use of AI and machine learning technologies in production.
AI and machine learning technologies can unlock huge potential along the insurance value chain. Many disruptive players are tapping into these opportunities through:
- New product offerings: by leveraging data not traditionally used by insurers, such as satellite data, insurers can expand their offering in areas like parametric
- More accurate and targeted pricing: Akur8 leverages proprietary machine learning to automate the insurance pricing process while guaranteeing the transparency of the models, thanks to their unique transparent AI, accelerating the speed to market by 10x and improving the predictive power of the models by 10%.
- More efficient claims handling: leveraging AI through chatbots increases the efficiency of claims
- Quicker claims management through automation: the use of AI for image recognition on a video or pictures of a claim allows for the automation of damage
- Fight against fraud: AI has transformed insurers’ fight against fraud, by enabling them to identify systematic patterns and facilitating fraud detection, while making it more
The challenge of using AI and machine learning is not about the maturity of the technology anymore. That is a given. It is about the capacity to use it in production, at the core of insurers’ processes.
Machine learning models have become easy to build; understanding them from a business perspective, putting them into production, at the heart of processes as core as pricing and maintaining them, is where the differentiation opportunity lies.
This is where expert, best-in-class B2B SaaS providers come into play: they provide incumbents with cutting-edge technologies that can be used in production at the core of their processes.
Regulatory pressures… good and bad
The abundance of data, and the opportunities to leverage it, come with regulatory requirements to ensure clarity and transparency to meet government and industry guidelines and to ensure ethical use that customers can have confidence in. In fact, 56% of organizations struggle with IT governance, compliance, and auditability.
These regulatory pressures are set to grow alongside the proliferation of AI and machine learning technology, and an added challenge of differing regulations in different countries and economic areas can be a minefield for global insurance brands to navigate.
It is not just the technologies themselves that are being regulated, but how they are applied. The United States House of Representatives Resolution 2231 seeks to establish a requirement of algorithmic accountability, addressing bias and discrimination, a risk/benefit analysis and impact assessment, and issues of security and privacy. This is driving a need for greater transparency and the rise of ethical AI, which considers how the technology is used and the impact of its use.
Regulatory changes to an already-strict insurance world mean that there is added pressure on profitability. Measures like Solvency 2 demand a certain amount of capital reserves to be maintained by insurers and new regulatory frameworks and measures mean often expensive changes to processes, while increasing data protection measures – all of which require profitability to survive.
But there is an upside – the speed of AI and new ways of processing data can accelerate the way that data is managed and automate processes in line with regulatory restrictions, to rapidly keep pace with legislation.
Now is the time for change
In its 2020 report; ‘The Growing Importance of Pricing in the Insurance Industry,’ Boston Consulting Group commented: “… those insurers who continue to rely solely on a traditional actuarial model with a cost-based perspective and a limited set of risk differentiators will eventually end up with a larger pool of relatively riskier and less profitable clients. This will negatively impact profitability and, ultimately, market share.”
Yet pricing analytics has been one of the last bastions of powerful data use in the insurance industry.
Their report also found that digital leaders achieve earnings growth that is 1.8x higher than digital laggards and enjoy more than 2x growth in enterprise value. So, the financial argument for adopting these new technologies is already there. Meanwhile, the role of the actuary is changing forever.
While their key skills and competencies still have a vital part to play in mitigating risk and making policy decisions, the tools at their disposal can empower them to make better, more responsive decisions to keep pace with the new market players.
The early groundwork has taken place and we now have a world where transparent AI can ensure clear, fair and ethical decision making, which empowers insurers to make faster, smarter decisions and gives them the flexibility to ride the waves of a changing economic climate with ease.
The time for digital transformation in the insurance industry is now. While tech giants are looming in on the insurance pie, insurtechs are building successes on the foundations of poorly satisfied customer expectations. Incumbents can learn from the secret ingredients of new players: harnessing data and leaning on AI.
When data is transparent (and not just explainable), AI can be safely applied to processes as core as pricing to generate substantial differentiation, while complying with regulations and avoiding adverse selection risks.
Ultimately, no insurer would dispute the core importance of pricing within their strategy. Yet how pricing is determined and used is in a state of change and transformation, thanks to tools like machine learning and AI. This change is parallel to the slow nature of change with actuaries, whose precision and modeling remain of fundamental importance, but whose existing roles will evolve from manual rate plan building into validating, checking, confirming, and approving automated machine learning outputs.
Old tools and doing things the way they have always been done will become a thing of the past, replaced by new approaches that can generate faster, more nuanced, and responsive policies and decisions, tempered by a need to address new regulatory constraints and challenges.
In the current economic climate, insurers choosing to stick with what they know will find themselves at competitive risk, as other players, who understand how to leverage data, will bring new and innovative offerings to the market.
Akur8 is revolutionizing insurance pricing with Transparent Machine Learning, boosting insurers’ pricing capabilities with unprecedented speed and accuracy across the pricing process without compromising on auditability or control.
Our modular pricing platform automates technical and commercial premium modeling. It empowers insurers to compute adjusted and accurate rates in line with their business strategy while materially impacting their business and maintaining absolute control of the models created, as required by state regulators. With Akur8, time spent modeling is reduced by 10x, the models’ predictive power is increased by 10% and loss ratio improvement potential is boosted by 2-4%.
Akur8 already serves 50+ customers across 20+ countries, including AXA, Generali, Munich Re, Tokio Marine North America Services (TMNAS); specialty insurer Canopius and MGA Bass Underwriters; consulting partners Xceedance and Perr & Knight; and insurtechs Manypets and wefox. Over 700 actuaries use Akur8 daily to build their pricing models across all lines of business. Akur8’s strategic partnerships include Milliman, Duck Creek, Guidewire and Sapiens.