Author Archives: Jinal Shah

About Jinal Shah

Director, Capital Markets, RMS
With more than 10 years of experience in the Insurance Linked Securities (ILS) market, Jin is responsible for managing investor relationships and new ILS product development at RMS. During his time at RMS, Jin has led analytical projects for catastrophe bond placements , and has designed new parametric indices to facilitate trading of index-based deals in peak zones, as well as introduced new pricing initiatives to the ILS market. Jin currently focuses on pricing deals and managing portfolios with RMS ILS investor clients, and leads the development of Miu, the RMS ILS portfolio management platform. Jin holds a bachelor's in Mathematics from The University of Manchester Institute of Science and Technology, and a master’s in Operational Research from Aston Business School and is a CFA charter holder.

The ILS Community Is Calling Out for Greater Pricing Transparency

I often hear reinsurance underwriters comment on how difficult it is to capture and represent all of the risks underlying a single transaction. Their data comes in many different formats, sometimes from broker or cedants’ own models, which can result in significant differences in modelling assumptions from one transaction to the next. Alongside this, deals almost always include some unmodeled risks like terrorism, aviation or marine. Consolidating all the risks in a transaction into a single view can be frustratingly complex.

This was a tolerable situation in the world of traditional reinsurance, when an underwriter’s autonomy and experience carried greater weight, and capital providers—usually shareholders—were less interested in the finer details of the risks. But the world has changed. Today, as collateralized reinsurance and sidecars financed by highly technical investors become increasingly widespread, especially in retrocession markets, better quality data is more important than ever, and often essential to getting the deal done.

Furthermore, the Insurance Linked Securities (ILS) market demands valuation of its on-risk investments, as fund managers face increasing pressure from stakeholders (internal compliance, regulators, and especially investors) to have deals marked independently.

The challenges

The challenge is compounded by capital markets investors’ broadening appetite for reinsurance risk. Both excess of loss layers and quota share deals are in the frame, with the former often covering tail risk with a low probability of attachment, and the latter the full distribution of risks with a high frequency of loss that’s attritional in nature. Deal pricing is fundamentally dependent on the transaction structure. Attachment and exhaustion probabilities determine the likelihood that event losses will trigger and exhaust a layer, and ultimately how losses within a layer will develop over a risk period. Because of this, a time-dependent view of loss development and ‘incurred but not reported’ claims should influence investment valuations. Historically, this has proved difficult to achieve, given the inconsistent data and unmodeled risks typically supplied in a deal submission. Current market solutions employed by fund managers are mainly based on actuarial methods of valuation which do not capture the full risk profile.

Cash flow is also critical. Net earned premium should be risk weighted to ensure that future premium cash flow is not accrued before the risk has passed. Set-up costs including brokerage fees, taxes, and others should also be considered. Lastly, pricing models must be dynamic, such that the technical price is updated to reflect actual reported losses, and cash flow forecasts are recalibrated accordingly.

Such a view of risk and return – one which is both time and structure-dependent – is fundamental to arriving at the proper valuation of a reinsurance deal in isolation and, also critically, for a portfolio. A uniform procedure for transaction and outstanding deal pricing is therefore crucial to satisfying investors and their stakeholders.

We can now achieve all of that easily, regardless of the state of the risk information in hand.

RMS’ Miu platform offers a single environment in which to analyse all risks within a transaction, with a new multi-model risk aggregation feature complimented by a pricing service. The simulation-based tool delivers a single, holistic view of the risk in a proposed or live transaction and provides complete portfolio roll-up capabilities. In addition, the RMS mark-to-model pricing service provides weekly marks to support net present value calculations for deals, portfolios, and fund of fund strategies.

The solution

By using the RMS Miu platform, investors and reinsurers can import loss data in multiple formats, including exceedance probability (EP) curves, results data modules (RDMs), and event loss tables (ELTs), and fold them into a single, comprehensive view of risk. It’s an easy process which can be done while maintaining correlations between peril regions, whether the risk is modeled by RMS or not, capturing correlation of non-modeled risks across deals by defining a baseline view of risk for specific peril regions.

The Miu applications enable investors and reinsurers to unify their universe of risk into one place. More broadly, the platform facilitates their ability to model and share ILS reinsurance transactions by providing a single view of risk, in so doing delivering the market transparency that leads to improved pricing certainty, and consequently more capital for the sector.

This post was co-authored by Anaïs Katz and Jinal Shah. 

Anaïs Katz

Analyst, Capital Market Solutions, RMS
As a member of the advisory team within capital market solutions, Anaïs works on producing capital markets’ deal commentary and expert risk analysis. Based in Hoboken, she provides transaction characterizations to clients for bonds across the market and supports the deal team in modeling transactions. She has woked on notable deals for clients such as Tradewynd Re and Golden State Re. Anaïs has also helped to model and develop her group’s internal collateralized insurance pricing model that provides mark to market prices for private transactions. Anaïs holds a BA in physics from New York University and an MSc in Theoretical Systems Biology and Bioinformatics from Imperial College London.