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RMS Analytics: Network effects

The most successful companies in vertical markets pair product innovation with go-to-market disruption. In this series we explore that marriage, how companies many have never heard of came to dominate their markets, and how they scaled with distinct tactics. These history lessons inform what we look for when investing in vertical markets at Scale Venture Partners, and hopefully provide blueprints for the next generation of vertical market winners. 

Company name: Risk Management Solutions (RMS) now Moody’s Insurance Solutions

Founder: Hemant Shah, Weimin Dong

Year founded: 1989

Company vertical: Insurance (Property & Casualty)

Market dominance: Catastrophe modeling and risk management

What do they actually do: With over 400 risk models covering 120 countries, RMS is the world’s leading provider of catastrophe risk modeling solutions. Their core value proposition is helping insurance, reinsurance, and financial organizations assess, quantify, price, and manage risks from natural and man-made disasters. They provide models, data, and software to estimate the likelihood and potential impact from events like earthquakes, hurricanes, floods, terrorism, and cyber attacks, which then inform decisions about underwriting, pricing, capital management, and risk transfer. Ultimately, this helps companies better prepare for and mitigate the financial effects of catastrophic events. For context, in 2005, Hurricane Katrina took the lives of 1,200+ people, caused $150B+ in economic loss, caused $41B+ in insured loss, and led to 1.7M insurance claims being filed across 6 states.

Product innovation: RMS led the productization and commercialization of previously academic methods to predict the likelihood and financial cost of potential future catastrophes so that insurance and related industries can manage and transfer risk. Insurance companies are well equipped to forecast future losses from frequent events like car accidents by using traditional statistical and actuarial approaches on 5-10 years of historical claims data. These approaches do not work for infrequent, catastrophic events, like earthquakes, because (1) catastrophic events do not happen statistically frequently enough to build robust historical claims-based models and (2) our natural and built environments are changing rapidly enough that historical data is not useful.  Factors like climate change and increased building development in at-risk areas compound so that future events are likely to be more damaging than their predecessors. As they say in the investment world, “past performance is not indicative of future results.”

RMS builds forward-looking models by hiring scientists with deep understanding in structural engineering, geology, seismology, geophysics, hydrology, meteorology, and geospatial science to characterize and quantify key elements of these events into four modules:

  • Stochastic event module: Simulates hundreds of thousands of possible events that could happen in the future, each with varying characteristics like location, frequency, and strength, to represent the full range of possible scenarios
  • Hazard module: Determines the strength, geographic distribution of the event (e.g. ground shaking), and contributing factors (e.g. soil type) for each stochastic event
  • Vulnerability module: Transforms the hazard intensity (e.g. ground shaking) at a location into physical damage to a specific asset (e.g. a four storey masonry building)
  • Financial analysis module: Translates the physical damage into monetary values, taking into account the repair cost and insurance policy terms like deductibles and limits, and then sums up individual losses to represent the overall portfolio loss

The outputs of these models include monetary estimates to questions like, “in any given year, how likely is it that a group of insured buildings in California experiences a portfolio loss of $100M or more” and “in any given year, on average, how much is the portfolio expected to lose?”

Scaling magic moment: More than any other decision, what unlocked breakout growth and positioned RMS to be crowned the dominant catastrophe risk modeler is that it pursued an open ecosystem approach from the start. This began with their use of open dBASE (.dbf) files to store location-specific exposures in the RMS Exposure Data Module (EDM) and analysis results in the Results Data Module (RDM). Because insurers could not easily access information from their locked down “core” systems of record, RMS’s openness led to a rapid proliferation of internal tooling built by insurers, with the RMS EDM & RDM as the foundation of their infrastructure.

Insurers’ preference for the open RMS system then spread to brokers and reinsurers who also benefited in similar ways. The combined effect was that the RMS EDM & RDM became the ubiquitous data exchange format, effectively a currency, to such an extent that, even if an insurer used a competitor to RMS, the competitor had to offer a transformation tool to convert their format into the RMS format to exchange the data throughout the market.

Where are they now: RMS continues to be the leader in catastrophe risk modeling and was acquired from the Daily Mail and General Trust (LON: DMGT) by Moody’s (NYS: MCO) for $2B on September 15, 2021. RMS had 1,300 employees at the time of acquisition and $320M in revenue. Moody’s acquired RMS for two reasons. First, this acquisition enables Moody’s to incorporate, in its financial ratings, the effects of catastrophes on the creditworthiness and financial stability of a company or financial instrument. Second, RMS expands Moody’s ability to assess, manage, and transfer risk for customers in insurance, real estate, supply chain, and related industries, incorporating natural perils, climate change, and man-made risks like cyber.

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