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Data puts active portfolio management on a firm footing

Insurance Consulting and Technology
Insurer Solutions

December 16, 2020

Continuing our series of articles on portfolio management in the Lloyd’s and London Market, we look at the symbiosis between active portfolio management and granular data.

Introduction – data from the ground up

The breadth, reliability and granularity of data sources used in portfolio management determine the parameters for success. Or rather, accepting limits on the breadth, granularity and reliability of the data may limit the extent to which success can be achieved.

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About this series

Earlier this year, we announced that Lloyd’s would launch a series of reports, presentations and workshops throughout 2020 to focus on the latest portfolio management trends in the insurance industry, including analysis of behaviour in underwriting, pricing, and portfolio and data management.

We believe that this report, together with all the reports and activities that we have been conducting throughout the year, will benefit Lloyd’s market participants in by describing more fully the elements of strong portfolio management and its relationship with first-class underwriting performance.

Good quality and quantity data are essential foundations for successful portfolio management capabilities, as we established in our article “Portfolio Management in the London Market: What separates the best from the rest?” published in February 2020.

In “Learning from the best: Behaviours that drive top underwriting performance” we also saw that top performers exhibited a better understanding of their portfolios, with faster reaction times, and demonstrated a “growth mindset” which included a belief that technology is liberating.

Likewise, in our benchmarking research of 2019, we established a framework for portfolio management consisting of 72 attributes, which underpin three broader dimensions; granularity, agility and coherence. Of these, Granularity had the largest gap in capability between outperformers and emerging performers. Within this dimension, the key drivers of the capability gap were not just the granularity of the data, but also the organisation’s ability to use data and technology to help better manage its portfolio. Our research demonstrates that the case for data as a foundation for portfolio management is strong and recognised.

Grouping the attributes of portfolio management:


Granularity
  • Granularity — Segmentation by class, peril, channel, etc. Full range of mix indices. Well defined, appropriate target business mix.
  • Data and technology — Access to tools at point of sale/renewal. Single source underwriting data. Data science solutions for unstructured data.
  • Spreadsheets — An over reliance on spreadsheets can hinder effective pricing, planning and monitoring.
  • Pricing models — Used to steer portfolio strategy, support underwriting decisions, and enable operational efficiency, predictive pricing and MI reporting.
Agility
  • Speed — Fast identification of portfolio issues, analysis of issues/deviations, and implementation and visible impact of plans.
  • People skills — Skills to set/analyse portfolio strategy, and promote balance between data and judgment. These are at the point of sale.
  • Monitoring business mix — Monitor target business mix adherence and appoint an individual responsible to regularly track mix.
  • Discipline — Good grip on price adequacy and portfolio. Balanced rates and retention, a robust set of indicators, and appropriate rate change requirements.
Coherence
  • Plan testing — Plans tested against market realities, set for macroeconomic factors, and tested for contradictions/assumptions. Good understanding in setting expenses and acquisition for new business.
  • Alignment — Coherence between plan and models, alignment between portfolio strategy and risk alignment, well cascaded target business mix, effective planning process, aligned product development, and effectively allocated RI.
  • Consistency — Accurate outcome forecasting, consistent ULR use across portfolio, and consistent approach on attritional, large and cat claims.
  • Importance — Confidence in portfolio strategy and investment in portfolio management. Recognition that portfolio management is important for competitiveness.

Of course, data alone is not enough: how an insurer integrates these insights into its decision-making processes is also fundamental and we will explore these themes in more depth later in our series of articles on portfolio management.

As data forms the foundation of portfolio management, so in turn portfolio management is proving to be a gateway to the development of new solutions and products. Perhaps the best example is how portfolio analytics is driving the parallel development of different underwriting models: case-level augmented underwriting and portfolio-level algorithmic underwriting. These models can develop in tandem as the same development in portfolio analytics can manifest either as a significantly more sophisticated underwriting dashboard to better inform case-level underwriting, or in the development of an algorithm that can be deployed at portfolio level.

So where to start?

We recommend that any organisation should adhere to the four ground rules below. This will set them on a path to create a data asset that puts a portfolio management approach on to a firm footing, and enables outperformance potential:

  1. 01

    Ensure an approach that is equally useful for all the functions that make up your business.

  2. 02

    Pull in all the elements of exposure that describe the risk with a componentised approach to pricing, rather than the traditional risk proxies.

  3. 03

    Collect claims, pricing, exposure management, reserving and quote data.

  4. 04

    Track the decision making of any given underwriting decision, and how the price (making clear distinction between risk adjustments and commercial adjustments) and terms are altered at any point.

The future of data

The good news is that the collection of data, and the analysis of that data, is becoming easier, quicker, and available at a lower cost. It is also becoming more valuable as our understanding of the data-rich world, and the tools available to infiltrate and unlock it, becomes more advanced.

Moreover, new metrics can be developed by looking at existing data from a new angle (which we refer to as novel data). For example, insurers could use internal operational data and metrics, such as a count on number of emails to broker A vs broker B in a typical renewal process, that could help followers streamline the process. Indeed, the adoption of novel data is a feature of many market outperformers – for example, using a scoring system to assess the ‘friction’ in conversations where high levels of rate might be required, or to create a lead indicator on rate and retention outcomes, particularly in evaluating the initial impact of remediation activity.

Novel or otherwise, an enlightened view on data will be the bedrock for insurers in achieving actionable and active portfolio management.

Whilst our focus in this article is mastering data for portfolio management, there is of course also a symbiosis between portfolio management and pricing, where the granularity of how each operates will drive potential in the other. Furthermore, achieving granularity at the pricing stage and translating that through to other business processes is often the key enabler.

Download the pdf to read the full article.


This report has been co-produced by Lloyd’s and Willis Towers Watson for general information purposes only. While care has been taken in gathering the data and preparing the report, Lloyd’s does not make any representations or warranties as to its accuracy or completeness and expressly excludes to the maximum extent permitted by law all those that might otherwise be implied.

Lloyd’s accepts no responsibility or liability for any loss or damage of any nature occasioned to any person as a result of acting or refraining from acting as a result of, or in reliance on, any statement, fact, figure or expression of opinion or belief contained in this report. This report does not constitute advice of any kind.

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