In October 2023, Doubell Chamberlain spoke to Rishi Raithatha about the impact that innovation and technology has had on the microinsurance market over the last 10 years. This is a short excerpt from the full interview. You can read more of the interview on the MicroInsurance Network site.

Q: What are some instances of innovation in the microinsurance or inclusive insurance space that you have witnessed?

There have been numerous instances of innovation and a few that we have found particularly interesting include:

Risk pooling: Technology and data-driven insurance models, such as TongJuBao, seemed able to overcome the traditional boundaries of insurance by leveraging the peer-to-peer mechanism. This enabled them to underwrite risks not typically considered underwriteable, for example divorce insurance.

This model included innovative approaches to client communication, such as gradually providing insurance contract information to the client as they needed it. Even at that time, most of the peer-to-peer plays were not breaking boundaries on insurance but rather competing for traditional insurance business by focusing on areas such as vehicle and pet insurance. However, we have not yet seen the peer-to-peer model reach much application or scale in the microinsurance context.

Distribution: The past decade saw the rise and fall of Mobile Network Operator (MNO) distribution, succeeded by digital platforms such as Grab, Gojek, and Alibaba. Although these platforms generally represent a small segment, they exhibit an interesting alignment of incentives. Additionally, there are rare instances of supply-chain operators offering insurance, which, while not new, remains sufficiently uncommon to be considered innovative.

Data and digital platform-driven risk management and transfer: Examples include returns insurance for African online retailer Jumia and targeted accident insurance for a moto company in Rwanda. These innovations leverage data and digital processes to incorporate risk transfer into specific transaction components, enhancing business viability and facilitating risk management.

Communication: A notable change that holds great potential is the increased use of WhatsApp for business in Africa, exemplified by Kenyan insurer Britam. Although chatbots have seen limited success and are often costly, the innovation lies in data-driven automated communications that provide timely information, such as informing the client of withdrawal costs from different banks’ ATMs.

Claims: There are some interesting, if isolated, examples of claims innovation. Inclusivity Solutions developed an application that identifies the closest claims assessor to an incident by using an Uber-type algorithm. The use of Artificial Intelligence (AI) to assess claims is also very interesting. Seguros Bolivar uses AI to assess the likelihood that a claimant is lying when they report a claim via phone. If the AI trusts the claim, they pay immediately; if not, they do an assessment. Although some false claims slip through, the cost is greatly offset by the reduced administration cost. Similarly, companies like Pineapple and Lemonade have applied AI to assess claims based on photos of goods.

Verification: One of the more consistent albeit slow-moving trends is the industry being able to access government datasets to verify client information. The most obvious is identity (for example, NIDA in Rwanda is accessed directly via API by all insurers) but also a motor vehicle database in Ghana as well as the births and deaths registry.

Technology-based risk management: Products like Bima/Semadoc, which offer health coverage and risk mitigation through telemedicine, have seen voluntary uptake due to their added value in risk management rather than relying solely on traditional insurance. Challenges arise when such innovative solutions are not prioritised by parent companies, such as in the case of Semadoc. Again, an example of those who are interested and able to innovate but not given the space to do so.

IoT-based risk management and mitigation: Here, technology is used both in mitigating the risk as well as adding tangibility and value to a bundled solution. Examples include Lumkani, Parsyl (the goods in transit tracker), anti-theft security tags, and even cow sensors. We are also interested in platforms that facilitate peer learning about crop pests and diseases. While this phenomenon is growing, there are myriad of these technology providers and very few insurance partnerships.

Q: How have these instances of innovation contributed to growth within the microinsurance market?

While these are all interesting case studies, I am not aware that these initiatives have unlocked major growth in the microinsurance sector or that these business-model innovations have been broadly adopted beyond these specific instances.

This limited uptake, I believe, is largely due to the challenges in overcoming the “gravitational forces” of underdeveloped insurance markets and infrastructure, which are not conducive to widespread innovation. In addition, resource-constrained insurers tend to prioritise the most profitable opportunities, which do not always include these microinsurance offerings.

The regulatory environments also fail to offer innovators a fair playing field or even a balanced negotiating stance with incumbent insurance entities, rendering innovators subject to the inertia of the established industry. As an example, the insurance sector has been notably slow in digitising its operations. A study on the impact of Covid-19 on the insurance industry revealed that in one country, only one insurer was capable of processing claims during the lockdown, as the rest depended on physical presence in the office. On a positive note, the shock induced by Covid-19 has seemingly accelerated digitisation efforts within insurance companies.

Q: So how has innovation around microinsurance changed?

Overall, the level of microinsurance innovation seems low given the level of technological change over the last 10-15 years. The pandemic and its economic consequences have devastated business development capacity in the insurance industry but also triggered some digitalisation of processes for insurers. This digital shift may, hopefully, extend benefits to microinsurance, albeit indirectly.

As result of the pandemic’s impact, we have also seen the decline in the amount of grant funding available to fix the underlying insurance industry and deliver developmental outcomes. Moreover, the limited funding that is still available has shifted to the impact investment space but with too small a scope and mandate to solve some of the larger ecosystem problems and ensure success for their investees.

Consequently, innovation has pivoted from being largely donor-driven to being spearheaded by insurtechs, fintechs, and digital platform (as opposed to the traditional insurance sector). Such innovations tend to focus on immediate market and business model challenges, frequently overlooking microinsurance. Tech-driven innovation also often focuses on solving particular challenges rather than challenging any existing insurance model. The expectation of large-scale industry disruption is, therefore, often disappointed.

Data, including that from sensors, is emerging as a significant driver of innovation, although its application to microinsurance remains limited. Leveraging data, AI and machine learning are poised to facilitate further innovation and efficiency improvements.

Utilising real-time data and communication capabilities for client communication not only in claims processing but crucially in risk management, holds substantial potential to deliver value to consumers and unveil new prospects for insurers.

Rather than advocating for dedicated microinsurance regulatory frameworks, we see greater value in fostering a generally proportional and adaptive regulatory environment in developing countries. This could include advanced approaches such as sandboxes and cell captive regimes, as well as straightforward approaches to industry communication, aligning with the specific risks and needs of these markets without constraining business models.