The journey to an Open Insurance standard – part 4

The Role of Smart Regulation in Innovation

Written by Fouad Husseini, founder at Robosque and The Open Insurance Initiative

Currently, there are hardly any vibrant collaborative insurance ecosystems relying on open interfaces. The entry barriers are high, and the potential gain is low for individual stakeholders to force change. Not surprisingly, for industry insiders, regulation has a big role to play. In the last part in the series we focus on how automating the compliance and regulatory reporting function could introduce more innovation.

A key advantage for an insurance platform would be to allow extended functionality enabling dynamic discovery, orchestration, and negotiation of services to facilitate business. In practice, negotiation of commercial insurance coverage or that of facultative reinsurance agreements has traditionally been carried out in analogue form. This may involve exchanging files of statistics and supporting documents, email correspondence and probably few phone calls and several days of waiting for a response from underwriters and brokers.

Moving most of the technical processes onto digital platforms is not an impossible challenge and great strides have been made. However, meeting regulatory requirements is an issue constantly flagged by emerging platforms due to the difficulty in automating regulatory reporting and compliance. Rules and regulations were not designed to be machine readable and using natural language processing and machine learning has produced limited success.

The cost of regulatory reporting and compliance is substantial as we will see but let us start by briefly describing the reasoning behind Open Insurance (OPIN).

The potential benefits of Open Insurance

It is likely that with shared data and open APIs, the playing field will become significantly larger, an environment in which new innovative services can be created and new service providers can compete.  Designing for scale and broad use case scenarios will allow nonfinancial participants to directly market an array of insurance products.

Consumer data literacy is also important. To build more trust and confidence, insurance stakeholders must play a role in educating consumers on open insurance, their rights, benefits, and the advantages of sharing access to their data across the ecosystem.

Consumers will be big winners

The least consumers should expect are cheaper, wider alternatives, and easier switching opportunities.

Aggregated data will give the customer a holistic view over their financial protection plans. It will mean, personal financial management apps looking across all policies held by an individual or business. Third party providers will make it easier for customers to act on pricing and claims analysis, perform like-for-like product comparisons, and receive quotations based on the product features they are most interested in.

Services monitoring changes in the customer’s lifestyle or circumstances could flag needed changes to the policy, for example, if they are likely to need to supplement the indemnity amount.

Better advice could mean identifying whether a customer is over or under insured come policy renewal or time to switch carrier.

Insurtech enabled platforms will seamlessly provide their customers with bespoke deals based on customer’s lifestyle and financial habits. Technology could be leveraged to serve new demographics with little or no viable financial information to draw upon by helping them demonstrate good risk characteristics.

Insurance incumbents must respond to changing business dynamics

Insurers need to adapt to new economic and competitive realities to future-proof their competitive position. For Open Insurance to deliver on its promise, the range of data shared, its quality and timeliness are critical elements.

Likewise, engagement of consumers will be a factor of how well insurance solutions respond to their needs and challenges. Swiss Re in its Data-driven insurance: ready for the next frontier? states that APIs will have a revolutionary impact on business and will unlock opportunities from sharing the growing streams of data. Data generated from new partnerships and strengthened networks will improve the development of machine learning (ML) to produce instant underwriting and claims processing systems and personalized solutions.

Better innovation capability, continuous learning and scale require digitalization of data. It would allow risk carriers to recommend new products to current and prospective customers.


“We won’t experience 100 years of progress in the 21st century — it will be more like 20,000 years of progress.” Ray Kurzweil


Third party access allows new players to enter the market

It is highly likely that with shared data and open APIs, the playing field will become significantly larger.

Additionally, those that follow the chatter in the venture capital community may have noted many of them proclaim that FinTech enabled marketplaces will unlock the largest commercial opportunities in the future.

It means the next wave of marketplaces will build InsurTech solutions directly into their platforms to solve inefficiencies and complexities in underwriting and claims settlement to capture on their platforms more of the transactions that happen offline or elsewhere online. They will introduce superior experiences developed inhouse or through developer communities.

At a different pace, regulators are also playing their part in trying to redefine how regulated services are monitored and exploring innovations in technology to modernize their processes. This is a large topic and the possibilities are endless, but we have singled out the automation of compliance and regulatory reporting as a key area of transformation.

Can regulation force financial markets to innovate?

Open Finance advocates around the world have marked insurance, pensions, mortgages, and the personal savings markets as targets for reform. Open Banking is not a one-off experiment. We are also heading towards smart data initiatives that allow consumers to share access to a diverse range of data for example, energy and communications consumption data. Policymakers intend to transform the value that consumers receive not only in terms of cost but to also make shopping for alternatives and switching operators easier.

Open data initiatives are fundamentally about empowering consumers over their data rights. The introduction of Open Banking in the UK provided consumers with a secure environment to give third parties access to their account information or make payments on their behalf. Since January 2018, FinTech startups and incumbents, have enjoyed a level playing field and a more forgiving regulatory environment to ensure innovation works in the interests of consumers and small businesses.

But what forms of innovation are policymakers hoping to unearth and is Open Banking on track to achieving this aim?

Open Banking intended to produce different forms of innovation including:

  • Development of banking specific new technologies, for example, ML solutions
  • Introduce new products, services and hyper-personalization
  • Progress the banking market towards the digital economy
  • Reimagine consumers’ relationship with financial services

Although there were many challenges with open banking, some were technical, while others were cultural. To summarize, the takeaways so far are:

Most opinions converge in that it will take several years to see the full extent of market entry and innovation. Therefore, it is hard to conclude at this juncture if regulatory intervention can force innovation. Other elements may have to come together, and time is an important factor for ecosystems to emerge and flourish.

In contrast, one of the primary tenets of OPIN is for the insurance market to adopt Open Insurance on a voluntary basis. This relies on the assumption that regulatory intervention could take time to happen or does not happen at all. The effect is to proactively shorten the path towards innovation and the wider benefits it can produce.

The burden of insurance regulatory frameworks

It is not clear for the time being whether regulators see intervention as necessary. They may play a convening role and provide direct support, or they may simply allow markets to organically evolve into open insurance.

But regulators and policymakers alike are interested in transforming the way consumers and businesses use financial services by widening access to alternatives and improving competition.

Proactively driving smarter regulation to match the speed at which economic activity is advancing could improve the financial health of markets. Regulators, however, would wish to ensure good outcomes for consumers by ensuring data is used ethically, prevent discriminatory or unfair pricing, and penalize exploitation of vulnerable customers.

Regulated entities also have to grapple with regulatory compliance and reporting responsibilities. This includes:

  • identity verification (KYC) in onboarding and detailed assessment of customer needs
  • anti-money laundering and Suspicious Activity Reporting (SARs)
  • customer data privacy and protection
  • financial risk management influenced by Solvency II and IFRS
  • many reporting requirements, frequent updates and ad hoc requests for data
  • dealing with cases where requirements are ambiguous

Additionally, there are financial costs that insurance entities must absorb to ensure compliance. This includes the need for human resources, upgrades to information technology systems and in some cases a need to hire experts to interpret requirements. A Thompson Reuters survey found that 62% of financial services firms in the UK and EU expect their total compliance budget to increase over the next 12 months.

Remarkably, small insurance brokers surveyed in the UK indicated that more than one in every four (26%) full time equivalent staff are employed to attend to regulatory requirements.

For regulators, the situation is equally challenging. Consider, the significant amount of regulation and regulatory reporting introduced in the decade since the financial crisis.

The case for automating regulatory compliance and reporting

A stumbling block in exploiting AI technology in compliance is due to the difficulty in interpreting instructions and compliance tasks. ML and NLP algorithms are not sufficiently capable of interpreting interlinked rules and directives in the way that human compliance officers and legal experts can other than extracting key terms from documents.

The rule books were not written in machine readable format nor conform to common standards. While various RegTech startups have made good progress, they face an uphill struggle in formatting and legally expressing rules to produce ready-to-use compliance solutions.

There are also challenges in determining regulatory obligations, estimating consequences and penalties and potential next actions for breach or misconduct. The causes of misconduct may emanate from biases in AI systems, conflicts of interest, discriminatory pricing or claims settlement.

The introduction of principles-based risk management adds additional complexity to the way risk carriers treat and adjust to the type and manner in which business is handled. Solvency II is a regulatory approach requiring access to granular data from what in many cases are legacy systems. It requires deep and broad analysis of data to identify and mitigate risks that can catastrophically affect a carrier.

Multi-national insurance entities operating across Europe may have to adjust their processes and reporting systems to each of the different EU states. Data sharing restrictions, conflicting insurance law and cross border regulation necessitate a group of experts in handling the resulting complexity.

These are significant and costly hurdles to overcome and justify action by regulators to explore the feasibility of data standardization programs, hopefully across jurisdictions, to aid in the introduction of automated regulatory compliance.

The challenges lie in identifying and managing the various regulatory requirements and converting them into machine executable logic. There was some experimentation.

The Financial Conduct Authority (FCA) has run tech sprints and proof of concepts to demonstrate the feasibility of converting reporting instructions into machine‑executable code.

automated insurance compliance and regulatory reporting
The role of automated insurance compliance and regulatory reporting in innovation

Platforms could stand to benefit from automated compliance and reporting

Building on from the FCA experiments, expressing and recreating stripped down machine-readable version of the structure and flow of regulation handbooks can be achieved. This Domain Specific Language (DSL) can then be turned into machine executable code. Another alternative may be to leverage semantic technologies in combination with rules-writing guidelines to redraft regulations. But this raises usual scalability concerns in necessitating manual intervention to correctly translate legal intent and interlink different regulations.

Consistent interpretation of rule books improves transparency of regulations and streamlines compliance. Standardized data and APIs could allow the regulator to pull accurate and consistent data directly from the regulated entity. Regulators could quickly receive information on new areas of interest satisfying ad hoc requirement.

Consider how late and inconsistent regulatory data can damage regulators’ ability to effectively supervise and monitor financial markets, identify harm, and detect financial crime.

RegTech startups will be able to produce off the shelf low cost solutions to reduce the time spent on communications, query management and SARs.

Platforms rely on technological advances to streamline services and distribution. Therefore,   the potential benefits are numerous, including:

  • reduced reliance on legal advisers or third-party service providers
  • avoid repetition of burdensome reporting across different ecosystems and jurisdictions
  • distributed ledger technologies and smart contracts could become an ideal use case for reinforcing trust and security in transferring data across a “regulatory network”
  • improved accountability and transparency within and across the regulated entity
  • simplified compliance against ambiguous principles-based risk management requirement
  • easier compliance result in more savings which would ultimately benefit customers
  • failure-in-compliance flagging and automated risk identification as it emerges
  • machine learning becomes a cheaper and more viable risk management tool

Regulatory reporting and compliance are expensive and time-consuming requiring resources that can be better deployed in business development. Therefore, more technically led the industry is the more attractive it becomes to innovative entrants in RegTech.

The ability of Insurance-as-a-Software startups would continue to be compromised in absence of affordable solutions and automated workflows. Automating regulations would reinforce their agility and solve problems of insufficient resources and lack of expertise.

Without high upfront costs, subscription-based insurance business models could in theory provide even cheaper insurance solutions while regulators leverage ML algorithms to analyze data in real time for flagging over-zealous practices.

Making compliance and reporting instant removes a significant hurdle. Deeper integration with a regulatory network, helps platforms optimize their processes and gain critical mass of users quicker.

It will likely remove uncertainty about new technology compliance requirement. This is may be the case with platforms that exhibit cross jurisdictional complexity. An issue that arises in the deployment of DLT.

It is also possible that regulatory automation could help in instantly determining the legal and regulatory position of each participant in a marketplace ensuring that they all hold the necessary regulatory authorization in relation to their activities before being able to derive any competitive benefits.

What is next?

I hope this series of articles has provided extended understanding by covering topics that are often off the radar when open insurance is discussed. The collection of topics has completely covered all aspects of our theory for change. It is been quite a challenge to condense and present ideas in 2000 words monthly articles, a target that was always exceeded!

Work on the first stage of the Open Insurance API standard is providing additional learning and research opportunities with a more technical dimension. It is learning by doing.

It has been an interesting and rewarding journey and I hope to share much of this adventure in future articles.


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Read Part 1: Setting the Stage for Open Collaboration

Read Part 2: Understanding the Risks and Challenges of Open Insurance

Read Part 3: Managing Collective Contribution