Len Llaguno

Len Llaguno

Greater Chicago Area
4K followers 500+ connections

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Do you have concerns about the accuracy of your loyalty program liability?
Or do you…

Articles by Len

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Publications

  • Reserving with Machine Learning: Applications for Loyalty Programs and Individual Insurance Claims

    Casualty Actuarial Society E-Forum

    Reserving is typically performed on aggregate claim data using familiar reserving techniques such as the chain ladder method. Rich data about individual claims is often available but is not systematically used to estimate ultimate losses. Machine learning techniques are readily available to unlock the benefits of this information, potentially resulting in more accurate reserve estimates.

    In this paper we introduce a reserving framework that leverages machine learning to incorporate…

    Reserving is typically performed on aggregate claim data using familiar reserving techniques such as the chain ladder method. Rich data about individual claims is often available but is not systematically used to estimate ultimate losses. Machine learning techniques are readily available to unlock the benefits of this information, potentially resulting in more accurate reserve estimates.

    In this paper we introduce a reserving framework that leverages machine learning to incorporate rich
    granular information that is not captured when data is analyzed at the aggregate level. The framework relies on the snapshot date triangle as the format for organizing the data, which enables us to incorporate all available information in the prediction of ultimate values. A mix of machine learning algorithms is applied to the snapshot date triangle to create segments of claims with homogeneous development patterns. Standard triangular methods can then be applied on each segment to estimate the ultimate values.

    This method was developed in the context of reserving for loyalty programs. Within the loyalty context, reserving refers to estimating future redemption patterns for points issued to-date, producing an estimate of the loyalty program liability. We show how this framework can be used to create segments of members with homogeneous redemption behaviors, which facilitates the reserving exercise.

    We see a clear analogy between a loyalty program member’s redemption pattern and a claim payment pattern. Consequently, the applicability of this framework for loyalty program reserving suggests there may be an opportunity to apply this framework for insurance claim reserving.

    See publication
  • Loyalty Rewards and Gift Card Programs: Basic Actuarial Estimation Techniques

    CAS E-Forms

    In this paper we establish an actuarial framework for loyalty rewards and gift card programs. Specifically, we present models to estimate redemption and breakage rates as well as to estimate cost and value for use in both accrued cost and deferred revenue accounting methodologies. In addition, we provide guidance on various issues and considerations that may be required of an analyst when working with loyalty rewards and gift card programs.

    Other authors
    • Timothy A. Gault
    • Martin Menard
    See publication
  • A Structural Simulation Model for Measuring General Insurance Risk

    CAS E-Forum

    The paper describes a stochastic simulation model developed by the authors that has some attractive advantages over other published approaches to risk measurement. A particular issue is the need to measure reserving and pricing risk over a one-year time horizon; the model does both one-year and run-off risk measurement.

    Other authors
    • Stephen Lowe
    See publication

Honors & Awards

  • Towers Watson Chairman's Award

    Willis Towers Watson

    The Chairman’s Award recognizes associates who have made outstanding contributions to Towers Watson by consistently performing at an exceptional level, significantly exceeding the requirements of their roles and exemplifying our core values.

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