Tag: casualty practice

Managing Interest Rate Risk: ALM, Franchise Value, and Strategy

Mary Pat Campbell

Link: https://www.casact.org/sites/default/files/2021-03/9_Panning.pdf

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Excerpt:

Fortunately, there is a solution to the dilemma just posed. It
consists in adopting a pricing strategy that substantially alters the
sensitivity of a firm’s total economic value to changes in interest
rates. In the example give earlier, where
a = 15% and
b = 0, the
duration of the firm’s franchise value and total economic value are
17.62 and 6.70, respectively. But suppose we alter the firm’s
pricing policy by changing these parameters to
a = 10% and
b = 1.
In this case the target return on surplus remains at 15% (given that
the risk-free yield remains at 5%), but the durations change from
17.62 to 7.62 for franchise value, and from 6.70 to 3.27 for total
economic value. The key insight here is that a firm’s pricing
strategy can significantly affect the duration of its franchise
value and, consequently, the duration of its total economic
value.

This insight suggests a more systematic approach to managing the
duration of total economic value: find a combination of the
strategy parameters
a and
b such that the return on surplus and the
duration of total economic value are both acceptable. This can be done either by systematic numerical search or by constrained optimization procedures. For example, if the firm in our example
wanted a target return on equity of 15% but a total economic value with a duration of zero, it should implement a pricing strategy with the parameters
a = 6.2% and
b = 1.763 to achieve
those objectives. The consequences of this and the two previously
mentioned pricing strategies are shown in Figure 3 for the three
different pricing strategies just described.

Author(s): William H. Panning

Publication Date: 2006

Publication Site: Casualty Actuarial Society (for exams)

DEFINING DISCRIMINATION IN INSURANCE

Mary Pat Campbell

Link: https://www.casact.org/sites/default/files/2022-03/Research-Paper_Defining_Discrimination_In_Insurance.pdf?utm_source=III&utm_medium=Issue+Brief&utm_campaign=RIP+Series

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Excerpt:

This research paper is designed to introduce various terms used in defining
discrimination by stakeholders in the insurance industry (regulators, consumer
advocacy groups, actuaries and insurers, etc.). The paper defines protected class,
unfair discrimination, proxy discrimination, disproportionate impact, disparate
treatment and disparate impact.
Stakeholders are not always consistent in their definitions of these terms, and
these inconsistencies are highlighted and illustrated in this paper. It is essential to
elucidate key elements and attributes of certain terms as well as conflicting
approaches to defining discrimination in insurance in order to move the industry
discussion forward.
While this paper does not make a judgment on the appropriateness of the
definitions put forth, nor does it promulgate what the definitions should be,
readers will be empowered to understand the components of discrimination terms
used in insurance, as well as be introduced to the potential implications for
insurers.
Actuaries who have a strong foundational knowledge of these terms are likely to
play a key role in informing those who define and refine these terms for insurance
purposes in the future. This paper is not a legal review, and thus discusses terms
and concepts as they are used by insurance stakeholders, rather than what their
ultimate legal definition will be. However, it is important for actuaries to
understand the point of view of various stakeholders, and the potential impact it
could have on actuarial work. As the regulatory and legislative landscape
continues to shift, this brief should be considered a living document, that will
periodically require update.

Author(s): Kudakwashe F. Chibanda, FCAS

Publication Date: March 2022

Publication Site: CAS

METHODS FOR QUANTIFYING DISCRIMINATORY EFFECTS ON PROTECTED CLASSES IN INSURANCE

Mary Pat Campbell

Link: https://www.casact.org/sites/default/files/2022-03/Research-Paper_Methods-for-Quantifying-Discriminatory-Effects.pdf

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Excerpt:

This research paper’s main objective is to inspire and generate discussions
about algorithmic bias across all areas of insurance and to encourage
actuaries to be involved. Evaluating financial risk involves the creation of
functions that consider myriad characteristics of the insured. Companies utilize
diverse statistical methods and techniques, from relatively simple regression
to complex and opaque machine learning algorithms. It has been alleged that
the predictions produced by these mathematical algorithms have
discriminatory effects against certain groups of society, known as protected
classes.
The notion of discriminatory effects describes the disproportionately adverse
effect algorithms and models could have on protected groups in society. As a
result of the potential for discriminatory effects, the analytical processes
followed by financial institutions for decision making have come under greater
scrutiny by legislators, regulators, and consumer advocates. Interested parties
want to know how to quantify such effects and potentially how to repair such
systems if discriminatory effects have been detected.


This paper provides:


• A historical perspective of unfair discrimination in society and its impact
on property and casualty insurance.
• Specific examples of allegations of bias in insurance and how the various
stakeholders, including regulators, legislators, consumer groups and
insurance companies have reacted and responded to these allegations.
• Some specific definitions of unfair discrimination and that are interpreted
in the context of insurance predictive models.
• A high-level description of some of the more common statistical metrics
for bias detection that have been recently developed by the machine
learning community, as well as a brief account of some machine learning
algorithms that can help with mitigating bias in models.


This paper also presents a concrete example of an insurance pricing GLM
model developed on anonymized French private passenger automobile data,
which demonstrates how discriminatory effects can be measured and
mitigated.

Author(s): Roosevelt Mosley, FCAS, and Radost Wenman, FCAS

Publication Date: March 2022

Publication Site: CAS

2021 Academy Legislative/Regulatory Review

Mary Pat Campbell

Link: https://www.actuary.org/sites/default/files/members/alerts/pdf/2022/2022-CP-1.pdf

Excerpt:

The American Academy of Actuaries presents this summary of select significant regulatory and
legislative developments in 2021 at the state, federal, and international levels of interest to the U.S.
actuarial profession as a service to its members.

Introduction

The Academy focused on key policy debates in 2021 regarding pensions and retirement, health, life,
and property and casualty insurance, and risk management and financial reporting.


Responding to the COVID-19 pandemic, addressing ever-changing cyber risk concerns, and analyzing
the implications and actuarial impacts of data science modeling continued to be a focus in 2021.


Practice councils monitored and responded to numerous legislative developments at the state, federal,
and international level. The Academy also increased its focus on the varied impacts of climate risk and
public policy initiatives related to racial equity and unfair discrimination in 2021.


The Academy continues to track the progress of legislative and regulatory developments on actuarially
relevant issues that have carried over into the 2022 calendar year.

Publication Date: 15 Feb 2022

Publication Site: American Academy of Actuaries