Processing And Managing Complex Data for Decision Support

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And, while the data is critical, many insurers will also have to review their actuarial models and develop new ones e.

Why Data Driven Decision Making is Your Path To Business Success

They will also need to look for new tools and techniques, such as economic capital calculators and proxy modeling, particularly for large complex liability portfolios, where full stochastic modeling is required for internal model solvency capital calculations. Generating correct and meaningful information, reports, and dashboards is undoubtedly an important part of the decision-making process, but so too is the willingness of management to take action on the basis of the information provided.

In some situations, management actions can be pre-built into certain scenarios, so that in the event of the scenario materializing, a series of pre-planned actions are triggered. In other circumstances, actions will have to be much more reactive. Complying with regulation such as Solvency II is a major cost to insurers and requires a vast amount of data.

That data, however, has tremendous value if it is enhanced and used properly.

Deriving benefits from Solvency II programs is a topic on the agenda of most boards. One of those benefits is undoubtedly better decision-making. To support this, insurers need high quality data that is stored in a structured repository to generate the reports, dashboards, and KPIs the business needs. Data is one of the most valuable assets an insurer has — they should make sure they use it to the fullest. This process effectively translates data into information, which can be presented in the format of reports, dashboards, or other forms of graphical output.

IFRS 17 subject matter expert; actuary; risk management and capital projection specialist.

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IFRS subject matter expert; accounting authority; risk management specialist. Asset and liability management expert; capable modeler; risk and capital specialist. 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.

The connection between decision-making and regulatory compliance data

The way insurance and investment products are distributed and managed in the future will undoubtedly change, but firms can benefit from the new paradigm. This article addresses how financial institutions can remain competitive by delivering intuitive customer journeys at a low cost using the latest technology. In this paper, we look at the latest developments in the Quantitative Reporting Templates.

We consider how insurers can address the challenge of maintaining Solvency II reporting systems to keep pace with the changing and emerging regulatory requirements. This article details the organizational and data challenges that insurers face when harnessing the historical and forward-thinking information needed to create interactive dashboards.

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. Learn how global regulations, demographic trends, and technology will impact insurers over the next few years and how they can best prepare for the changes. This article focuses on developing an effective data management framework for the analytical data used for regulatory and business reporting.

The SCR calculation process is complex, requiring significant data consolidation, cleansing and transformation to produce accurate and consistent results. Many recognize the challenges of data consolidation, data cleansing, calculating accurate results and formatting reports to submit to the regulators. This publication addresses the full spectrum of data challenges: data governance, data quality, tactical and strategic reporting,.

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7 Steps of the Decision Making Process

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  • The price was too high, and no one was willing to buy. Another example is extrapolation bias, in which current trends — such as a rise in housing prices — are expected to continue in the same direction, a fallacy that Stephens often observes in finance. The field of behavioral economics is rife with examples of how common misconceptions lead to enormous financial loss — Stephens outlined several more in this blog post.

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    While financial decisions can be weighed objectively, unfortunately, there's no economic model for morally guided decision-making. This becomes even trickier when employees must act as decision-making agents, where they're more likely to act on personal financial incentive rather than what's best — morally or financially — for the company as a whole. This is where instituting a decision-making best practice can be useful. Stephen Schwartz, CEO of Varfaj Partners , referred to it in his company as a "pseudo-Kantian framework for decision-making around the office.

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