The past decade has seen disruption become a key catchphrase in business, politics and public thought, as the impact of disruption spreads across economies. While this disruption may manifest as new business models, and the upending of old ones, it is technology that is enabling all of this change.
The insurance sector is being particularly affected by disruption. Smart phones have created a new means of distributing insurance products and lodging claims. The vast amounts of data generated and collected each day create opportunities for better underwriting and new products. Furthermore, the importance of digital assets has created opportunities for new insurance products, such as cyber-insurance.
With the speed and volume of change and disruption ever increasing, we could write a book on the trends and likely impacts of technology and disruption in the insurance sector. This article just focuses on three areas that continue to affect the insurance sector in 2019 and beyond:
- the ongoing impact of digital transformation on business models and operations;
- the use of artificial intelligence; and
- the partnering of incumbent players with insurtech startups.
The impact of digital transformation on insurance operations
All market indicators suggest we will see even more emphasis on using digital technology to transform insurance operations in 2019 and beyond. There are two key factors to consider in any digital transformation project:
- the customer journey and how the technology will affect overall customer experience; and
- how the regulatory environment interacts with "the possible."
The range of insurance policies available in the market, along with the impact of aggregators, means that more and more policyholders are regularly reviewing their insurance requirements and switching between insurers. However, the market suggests that it is no longer simply a race to the cheapest policy; rather, policyholders see technology-based offerings as a differentiator in, for example, underwriting (where the technology is used to drive more tailored underwriting decisions, reflecting a person’s individual characteristics) and claims processing (where a technology-based approach drives efficient resolution of claims, reducing the need for policyholder interaction and overall end-to-end cycle time). In each of the above cases, both customer satisfaction and net promoter score levels are increased.
Efforts to enhance the customer experience are a major factor in the continuous jockeying for position between financial institutions and technology companies. For any insurance-focused, digital transformation initiative, it is key to place the customer journey at the heart of any development. We are seeing more and more insurers take an agile approach to life-cycle design, identifying pain points and seeking to use technology to remove these. Consumers are now demanding real-time engagement and online access to services managed uniformly from their mobile and personal devices. This means insurers need to embrace the move from the old world of paper processes to a new, truly cohesive, digital ecosystem, minimizing the need for manual intervention to a limited number of exceptions.
So how does one ensure successful delivery of these projects?
In complex digital programs, it is unlikely to be a clear-cut case of one party or the other having culpability for delays. When projects fail, it is often due to a lack of collaboration, so it is important to deal with this upfront, making sure both the insurer and the supplier understand, in advance, the scale of what is required. Having clarity of expectation up front is critical, and jointly producing and adhering to a shared project plan, with clear timelines, outcomes and processes for dealing with bumps in the road will invariably increase the likelihood of success. During delivery, suppliers need to keep their customers honest too, and having a clear contractual process allowing them to flag up delays that will impact delivery is crucial. A collaborative approach allows for change and we see more iterative development processes focusing on ensuring that technology delivers what the customer actually wants.
All that said, regulation still determines what is possible - the parameters within which insurers can innovate - and it is important to bear regulatory requirements in mind when considering any form of digital transformation. Whether regulation seeks to control or encourage innovation depends on many factors, and it goes to the very heart of the jurisdiction’s regulatory culture. While some countries prioritize innovation as a means of delivering better customer outcomes, others prioritize standardized requirements that can be regulated appropriately in the interests of consumers.
In some regions, such as the UK, we are seeing regulators looking to encourage innovation in general insurance firms to provide greater transparency and fairness in pricing decisions. The formal launch of the Global Financial Innovation Network (GFIN) in January 2019, by an international group of financial regulators and related organizations, should help to facilitate this, by including in its remit the creation of a global regtech sandbox, providing firms with an environment in which to test cross-border solutions.
The topic of regulation needs to be front and central to any innovation. Those assessing the viability and return on investment for any technology-driven initiative planning should ask themselves not just can we? but also should we?
Experts around the world are warning against the widespread adoption of artificial intelligence (AI) without greater regulation. Many businesses in the insurance industry are already using – or intend to start using in the near future – AI as part of their business operations.
It's easy to see why. AI can be used to assist with distribution of insurance policies (such as using chatbots to sell insurance policies directly to customers or to identify gaps in coverage), underwriting (such as automated data analysis and pricing) and claims assessment (such as reviewing policy wordings and automated claims assessment), among other things.
However, the increasing adoption of AI also brings with it a number of novel issues and risks:
- Intellectual property rights and ownership of data: One of the main benefits of AI is that it allows organizations to derive hugely valuable data from large raw data sets in order to improve decision-making, personalization and customer experience. The value of this data is significantly increased when it is resold, so there is a tension between providers who want to retain customer data and customers who want their data returned to them without being widely shared. It is also essential to ensure that contractual arrangements deal not just with the data set itself but also the use of derivative data.
Similarly, the system “learnings” (i.e. what the company's data “teaches” the provider's algorithm) may have more market value than traditional customer modifications, because they improve the provider's platform, making it more marketable to future customers. Even if the derived data itself is not resold by the platform provider, the speed (and therefore cost) at which similar raw data sets could be used to create derived data would be significantly reduced. Learnings are therefore of greater interest and use to the provider and to competitors. Any agreement must contain provisions examining all the different output types, considering exclusivity of use (if technologically feasible with that platform) or, in the absence of that, commercial benefits that accrue to the customer.
- Bias and errors in data: There is a risk of AI systems (especially those that learn from human behavior and historical data) making decisions that are discriminatory or biased and that breach anti-discrimination legislation. Where has the data come from? Is there a risk of polluted data and bias through previous use; if so, how should the company's data set and the provider's platform be combined to ensure this is addressed? Data set biases and inaccuracies will be reflected in outputs from a trained system, so it is in all users' interests to ensure the quality of the data fed into the system. Working together with a platform provider to understand the source of previously used data, but also conducting a forensic assessment of the quality of your own data, is vital. As decision-making becomes increasingly opaque, retailers should also consider strong audit and transparency provisions to ensure clear traceability and accountability of data use and learnings, alongside a mechanism for recourse.
- Impact and treatment of service failures: The deployment of AI and robotic process automation (RPA) solutions also raises a number of novel issues around the impacts of service failures, due to higher volume of tasks undertaken. Any incidents or failures that arise are likely to not just perpetuate but accelerate, becoming catastrophic, with a significant impact on an organization's business. The risk of perpetuation of mistakes means that human supervision of the technology outputs will still be beneficial to mitigate the risks of perpetuation of errors. Similarly, the impact the system being unavailable is likely to be compounded by the absence of a manual workaround. Organizations should consider their contracting risk profiles carefully, assessing how the contract would respond to a significant outage impacting trading or a significant loss of derived data and learnings and consider liability provisions through this lens.
Across the world, the impact of AI is causing regulators to consider laws for the regulation of AI. For example, in late 2017, a group of US senators and representatives developed the Fundamentally Understanding the Usability and Realistic Evolution of Artificial Intelligence Act of 2017 (dubbed the FUTURE Act) to establish a federal committee to advise on topics relating to the development of AI, study aspects of AI and report to the Secretary of Commerce on legislative recommendations. In December 2018, the European Commission's High-Level Expert Group on AI released draft Ethics Guidelines for Trustworthy AI as a working document for stakeholders' consultation. A final version is due this spring. All this suggests we can expect legislation to come into force, probably within the next one to two years, leading to a new regulatory environment that businesses will need to navigate.
Insurtechs: Collaboration and investment
Technology has driven the rise of insurtechs (startups focused on technology in the insurance sector), which are turning insurance on its head; creating new ways of pricing, producing, distributing and servicing insurance policies. As insurtechs proliferate, so too will collaboration and investment opportunities for incumbents who are looking for strategies to respond to change.
Today, incumbent organizations understand there is an innovation gap: the pace of change in technology is outstripping their ability to innovate and keep up with changing consumer behaviors and market demands.
Organizations are acknowledging that they cannot – and from a financial and operational risk perspective should not – try to do everything themselves. In some areas, partnering is the better option. Innovating and developing new solutions takes time, money and management focus, all of which are finite resources, so being able to partner with insurtechs is a viable approach to addressing and closing the innovation gap.
Our work with financial services organizations suggests that the proportion of organizations with a high level of engagement with fintechs and insurtechs is set to almost double (from 30% to 55% of FS organizations) over the next two years, indicating that we can expect to see a new age of collaboration, adoption and investment.
The term partnering encompasses a range of models for incumbent organizations to work with insurtechs.
Among these models:
- Investment in startups – either directly or through a specific corporate venture capital (CVC) arm. Many large corporates have established specific CVC funds that sit outside their primary corporate structures, in a manner similar to the funds established by VC or private equity investors. Some of these funds have investment strategies focused around growth, while others are focused more on strategic investments in adjacent areas. For example, Ping An Insurance, the largest insurer in China by market value and the second-largest life insurer in terms of premiums, invested USD1 billion in its Ping An Global Voyager Fund, which focuses on fintech and healthcare-related technologies, which has obvious synergies with its life insurance platform.
- Partnerships or joint ventures – these types of models can vary greatly from deal to deal, but they typically involve a combination of investment by both the incumbent and the insurtech, together with service arrangements.
- Collaborations – this term often denotes a more traditional supplier-customer relationship, although given that insurtechs are often still in the development and growth phase when contracting with incumbents, there is often an element of the incumbent (as the customer) contributing something to the insurtech, be it expertise, data or a target customer base. In the past, these collaborative arrangements were usually negotiated and agreed at the same time as the investment, but this is becoming less frequent; the impacts of increased regulatory scrutiny are making the decoupling of investment and collaboration arrangements a preferred approach to avoid derailing investment rounds and opportunities.
Through its analysis, Deloitte identified almost USD900 million in investments in insurtechs in the first half of 2018 . With so much capital available and looking to be deployed into value assets, there is a need for organizations in the insurance sector that are looking to invest to ensure that they can move efficiently through due diligence and agreement processes. Investment rounds move quickly; there are often more investors looking to invest than there is room in the cap table, and serial entrepreneurs and their advisors are conscious of not overly diluting founders’ interests. We expect that the sophistication of high-quality insurtechs will only continue to rise. Therefore, investors will need to be as nimble as possible and clearly understand their investment mandates and parameters.
In considering collaboration opportunities with insurtechs, there are a number of emerging issues organizations in the insurance sector should take into account, but two challenges are:
- Data security: Data security is, and will increasingly be, a focus for organizations, particularly given the increasing prevalence of mandatory data breach notification regimes around the world. Keeping data secure is a difficult and time-consuming task, even when the data is within the confines of the organization's environment. Partnership with a series of smaller insurtech providers is likely to result in an increase in the volume and variety of data flows, rendering that data more susceptible to unwitting manipulation, use or disclosure. Insurtech providers may have difficulty meeting the robust data security requirements expected by larger, more established organizations. Whatever the specific detail of the delivery model, regulated entities and larger organizations may need to be prepared to focus on the must-haves when it comes to data security, with an emphasis on compliance with core security standards and prompt notification and remediation of breaches. This must be coupled with an understanding that a greater responsibility will likely sit within the financial institution itself to ensure compliance via its own processes and activities.
- Concentration and over-reliance risk: In a traditional outsourcing with a major vendor, a large organization is more likely to be one of several large customers outsourcing a similar type of services to the vendor. There are also likely to be other service providers that can provide a similar service, if required. While the size and agility of insurtechs is a factor in their rise, their size and the size or makeup of their client base could present an increased concentration and over-reliance risk. This risk could manifest in two ways: (i) if it results in the insurtech’s business being overly dependent upon a single or small number of major clients, or (ii) if the insurtech service or product is particularly niche and the client is dependent upon it to run its business. In both scenarios, adverse impacts on the insurtech’s viability could present a real risk of material disruption, and one that could take place suddenly and swiftly. If the service was suddenly withdrawn, the customer would be faced with a material disruption as well as an unexpected and potentially significant cost increase in sourcing an alternative solution. The prudent incumbent will monitor closely its reliance on a single insurtech and plan any action it would take in the event the insurtech fails, to mitigate any business impact of any such failure.
So what’s next?
In the insurance sector in 2019 and beyond, disruption seems inevitable. Technology-based developments across the sector, whether generated by insurers in-house, in partnership with the big-player technology suppliers or through partnering with insurtech, are becoming increasingly prevalent and driving change – both in terms of how insurers operate, and what customers expect. Placing the customer and the regulatory framework at the heart of any investment decision is key to ensuring investments are ultimately focused in the right areas.
Simultaneously, it is important to be alert to the hazards, paying close attention to how regulatory and contractual frameworks respond to the different risks posed by these technologies, while at the same time innovating and transforming, to ensure that insurers continue to remain competitive in the face of disruption.