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Most (re)insurers rely on an assortment of tools from a variety of vendors to support various business functions and subsequent workflows. From risk selection, underwriting, and pricing through to event response, portfolio, and capital management – all have different data and analytics needs.

However, one constant across all these functions is the need to capture and account for details within legal contracts. The risk ultimately held and managed by an insurer lies within the stacks of primary policies, facultative cessions, traditional reinsurance treaties, and other alternative risk transfer agreements. All these contract terms and conditions must be accounted for within a financial model.

What Is Financial Modeling?

Financial modeling translates the science and engineering results embedded in catastrophe models into bottom-line dollars and cents. It quantifies theoretical damages caused by catastrophic events and converts them into financial losses or obligations for the various risk-bearing entities.

To achieve this, financial models must apply the terms and conditions from all relevant legal contracts that specify how risk has been transferred between the different parties. This sounds simple enough. Surely if one can write a contract out in words, then representing it mathematically and computing the losses that it covers should be straightforward. But as anyone who’s worked in this space knows, the devil is in the details.

Risks of Inconsistency

Inconsistent financial modeling can lead to all sorts of detrimental outcomes. Let’s explore a few common situations.

Scenario 1: Inconsistent Modeling of Reinsurance Benefit Leads to Contradictory Growth/Contraction Plans

Even relatively small (re)insurers often have multiple teams working simultaneously on many projects and initiatives – and all using different tools. Often, two corners of the same company can seem impressively disconnected.

For example, an exposure manager could be running accumulations to analyze the company’s net position using the financial model within their cat modeling software. Yet, the “Ceded Re” department at the same company is using a broker-provided solution to design a complex outward reinsurance program.

This oversight could result in a poor business decision such as the non-renewal of profitable renewal business leading to the construction of a less-than-optimal portfolio. In this example, the benefit of reinsurance is modeled differently across the two teams. As a result, the exposure manager’s analysis may incorrectly identify an accumulation hot spot where the company is overexposed with respect to its risk tolerance.

These results might then be used to justify a reduction in exposure near this hotspot by non-renewing some large policies. From the Ceded Re department’s perspective though, this hot spot may have already been identified and appropriately mitigated via the purchased reinsurance program. With growth/contraction plans based on a different set of assumptions than the risk transfer plans, the organization is out of sync and has ultimately eroded firm value as a result.

Scenario 2: Inconsistent View of Reinsurance Benefit Leads to Inefficient Levels of Capital

A risk analyst runs a wildfire model using financial model (FM) 1, which can accurately account for hours and spatial clauses within the reinsurance program, to split long events into multiple occurrences. This modeling allows the underwriting department to grow its fire-exposed book with the confidence that the net exposure is acceptable per the company’s risk tolerance statements.

At the group level, a different wildfire model is used with FM 2. This FM cannot assess the impact of the hours and spatial clauses, with the group holding higher levels of risk capital to support the business. As the fire-exposed portion of the book grows, so does the degree to which capital is being held inefficiently. If the company is overcapitalized compared to the net risk it has taken on, then this leads to lower-than-desired returns. The FM inconsistencies have thus led to suboptimal shareholder value for the company.

Scenario 3: Inconsistent Treatment of Terms Leads to Mispricing and Adverse Selection

A capital modeler sets out to compute a nominal amount of capital on which a line of business must earn its required hurdle rate. As part of this analysis, a hurricane model is run using FM 1, which cannot accurately account for the increased cost of construction materials following a large event. FM 1 makes a simplifying assumption that all events are affected by the same high level of inflation and therefore overstates the necessary supporting capital. This produces a resulting overstated profit load to be used for pricing.

Meanwhile, a portfolio manager uses FM 2 which more accurately models the complexities of the event-induced inflation. The analysis arrives at a lower-than-expected loss for each exposure, making it appear to be a desirable section of the book of business.

Marketing and sales target the area, but the potentially excessive rates leave the company struggling to gain market share and simultaneously exposes them to possible adverse selection. The FM inconsistencies have led to mispricing that could hinder the company’s ability to deliver on financial plans and projections.

Barriers to Building a Common Financial Approach

Inconsistent financial modeling can arise for many reasons. The fact is that even the most collaborative organizations can come up short of building a unified view of risk when they are not using the right analytical tools. A few drivers of inconsistent financial issues include:

  • Incompatible views of risk from different model and software vendors:  Different modeling frameworks can take different approaches to calculate financial risk. In addition, some applications may not need to take as robust of an approach to quantifying risk.
  • Lack of a single source of truth: Catastrophe model output is used for a variety of workflows such as underwriting, pricing, exposure management, portfolio steering, and capital modeling. Most (re)insurers want to foster collaboration and use the same analytics across these tasks. However, when applications and copies of exposure data are hosted in different data centers, it’s difficult to apply a unified and flexible financial engine that meets the collective needs of the organization. The result is that teams of coworkers run their everyday workflows on siloed views of risk, making it difficult to achieve collective corporate goals.
  • Change management is both time-consuming and challenging: On-premises catastrophe modeling software packaging frequently bundles new and updated models in a single release. Not everyone within the risk organization may be using the same version of the same model or the same exposure datasets due to entrenched, fragile workflows that cannot be easily adapted. Where inconsistencies exist today, bringing them into alignment with caution involves the dissection of financial calculations to understand the impacts of each assumption change.

Rewards of Effective Financial Modeling

Getting this right means achieving consistency in your financial model. It needs to offer flexibility, transparency, and modularity so the financial model can be used across the business.

Our clients are making major breakthroughs in terms of financial modeling consistency, benefiting from applications that use the cloud-native RMS® Intelligent Risk Platform™ such as Risk Modeler™, ExposureIQ™, and TreatyIQ™ – all of which share the same data across the business, use a common approach to financial model methodology, and can accommodate the complexities of your business, such as the inclusion of complex reinsurance structures.  

This consistency gives your business a distinct market advantage and puts you one step closer to that utopian corporate scene where the firm’s engine is firing on all cylinders – your cloud-based, software-as-a-service solution is opening the doors.

Teams are making decisions that are aligned based on a single set of assumptions and implementations. Tools are centralized and built on shared engines allowing for rapid development, enhancement, and maintenance.  

Consistent views are held so that each area of the company knows what the other is doing and why they’re doing it. Silos are removed, goals and actions are aligned – and your company can focus on the many other challenges in the (re)insurance industry.

Find out more information about the RMS Intelligent Risk Platform.

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Jesse Nickerson
Jesse Nickerson
Senior Director, Pricing Actuary, RMS

Jesse is a Senior Director, Pricing Actuary at RMS, and an Associate of the Casualty Actuarial Society. He earned a bachelor’s degree in mathematics in 2005 from Lafayette College and a master’s degree in mathematics in 2009 from Western Washington University. He has spoken recently at industry events on the topics of spectral risk measures and Bayesian inference.

His actuarial work has centered on the development of simulation-based software products and actuarial pricing tools, which is his current focus at RMS.

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