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Basel II uses a "three pillars" concept – (1) minimum capital requirements (addressing risk), (2) supervisory review and (3) market discipline. The Basel I accord dealt with only parts of each of these pillars. For example: concerning the first Basel II pillar, only one risk, credit risk, was dealt with easily while the market risk was an ...
The second pillar of Solvency II plans to complete the quantitative capital requirements with quality requirements and a global and appropriate risk management system. The reform provides measures on governance, internal control and internal audit in order to ensure sound and prudent management practices from insurers.
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The NSFR became a minimum standard on 1 January 2018. However, implementation has been delayed in many countries. Less than half of the G20 members have implemented the rules as of 2018. Among those that lag behind are the US, the EU, Switzerland and Japan. The compliant countries include Australia, Brazil, China, Indonesia and Russia. [7]
In a 2020 survey [1] 88% of respondents reported using the balanced scorecard for strategy implementation management, and 63% for operational management. Although less common, the balanced scorecard is also used by individuals to track personal performance; only 17% of respondents in the survey reported using balanced scorecards in this way.
The implementation maturity model (IMM) is an instrument to help an organization in assessing and determining the degree of maturity of its implementation processes. This model consists of two important components, namely the: five maturity levels, adopted from capability maturity model (CMM) of the Software Engineering Institute (SEI).
Solvency II Directive 2009 (2009/138/EC) is a Directive in European Union law that codifies and harmonises the EU insurance regulation. Primarily this concerns the amount of capital that EU insurance companies must hold to reduce the risk of insolvency.
Business analytics makes extensive use of analytical modeling and numerical analysis, including explanatory and predictive modeling, [2] and fact-based management to drive decision making. It is therefore closely related to management science. Analytics may be used as input for human decisions or may drive fully automated decisions.
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