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    Moody's Analytics Insights

    Moody's Analytics Insights

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    Insight, IFRS 17, and Innovative Technologies - Drivers of Change in the Insurance Industry

    Performance optimization through business insight, dealing with IFRS 17 in a post-Solvency II world, and the challenges associated with stress testing for insurance firms in the US. These were the focus areas for Moody's Analytics at this year's Moody's Insurance Summits in London and New York.

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    Insurance Regulatory Insight Newsletter - November/December 2016

    Coverage this month includes the International Monetary Fund (IMF) recent Article IV on consultation with Chile, are encouraging institutional investors to give preference to investing in companies with good governance standards. The Australian Prudential Regulation Authority (APRA) has highlighted sustainability as a key theme in its submission to a Parliamentary Committee enquiry into the life insurance industry. While APRA has not included sustainability in regulation, the knowledge that they are interested in it might have an influence on insurers' activities. The United States Federal Insurance Office, published its fourth report on the insurance industry, and its first report on the protection of consumers and access to insurance.

    European Commission Vice President

    Insurance Regulatory Insight Newsletter - October/November 2016

    Coverage this month includes an article from the Secretary General of the International Association of Insurance Supervisors (IAIS) which directly addresses the suggestion that a global unified risk-based insurance capital standard is not a realistic goal given the existing divergent approaches. A speech by Verena Ross of the European Securities and Monetary Authority, one theme of the speech is regulators need for high quality data. The UK's Prudential Regulation Authority's (PRA) thinking about insurers using an internal model to calculate their required capital. The PRA is concerned that the output of an insurers internal model may drift, or evolve, over time to become a weaker capital measure.

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    The Next Wave – Implementing a well-designed Internal Model

    Institutions are transforming their analytic capabilities to move beyond static reports that explain what happened in the past, to more modern analytics that can explain why an event occurred and what is likely to happen in the future.

    timeline illustrating the requirement, the reporting sets required and delivery dates to

    The Challenges as Solvency II Reporting Goes Live

    This paper is the first in a series of short whitepapers where Brian Heale examines the major challenges and issues insurers face for report production, data management, and SCR calculation for Solvency II. The series of papers also examines the approaches insurers have taken in their Solvency II projects to date.

    Figure 1; Negative yields at end Q1 2015

    Optimising model capabilities

    This article explains how Moody's Analytics can help insurers with their solvency monitoring, reporting and stress-testing requirements in Solvency II.

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    Crafting a Successful Risk Management Culture

    This article addresses the two interdependent needs of effective integrated risk training and measuring optimal risk management to make recommendations for how to train and track behavior.

    Historical FTSE 100 faily log-returns (2 January 1986 to 31 October 2014)

    Quantifying the effect of dynamic hedging on 1-year VaR capital

    In this note, we consider some of the technical challenges and solutions in adapting internal models to account for the effect of dynamic hedging strategies in

    Exhibit 1 – The challenge of look-through

    Solvency II and Asset Data

    In this White Paper, we look at the challenges that insurers, fund managers and market data providers face in providing and aggregating the asset data required for the completion of the QRT templates and the SCR calculation.

    Histogram of 10000 raw data points sampled at random from the student t distribution with 10 degrees of Figure 1freedom. Also shown in black is the exact PDF of this distribution

    Percentile Estimation for Stable Capital Requirement

    When Monte Carlo methods are used to determine solvency capital requirements, the end result will depend on the way in which the set of net asset valuations are obtained. For example, if the samples are generated at random, then changing the random number seed would lead to a different capital requirement. This document examines the percentile estimation process in order to alleviate or lessen the variability of the capital requirement.