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Formerly known as Milliman CHESS, our award-winning solution delivers a distinctive combination of production efficiency and expertise support, along with state-of-the-art modelling catalog and high-quality data for both market-consistent and real-world applications.
Milliman Economic Scenario Generator
To match demanding timelines for risk-management decisions and regulatory compliance, you need to scale and automate the ESG production process. At the same time, you need results you can trust.
Achieve a high level of automation and scalability by integrating the Milliman Economic Scenario Generator into your existing systems. The cloud-based application delivers economic scenarios in a scalable environment, with easy-to-understand visualizations for rapid validation. The Milliman Economic Scenario Generator was named Best ESG Software by Insurance ERM.
Execute the calibration, simulation, and validation functionalities from any vendor or in-house system through the integration-ready RESTful API of the Milliman Economic Scenario Generator.
Receive expert advice to ensure you use the best model to achieve your goals for optimal efficiency, risk-management accuracy, and regulatory compliance.
Review audit features, model documentation, and Excel files that detail step-by-step derivations of economic scenarios. The Milliman Economic Scenario Generator’s calibration reports are based on alliance partnerships with the most renowned financial data providers and are subject to thorough data quality assessments.
Leverage Milliman’s comprehensive model catalog as you work with our team to determine which models apply to your specific scenarios.
See validation results quickly in the form of tables and graphs.
Get help from Milliman consultants as you navigate any external or internal review of results generated by the Milliman Economic Scenario Generator.
Access services from the Milliman Economic Scenario Generator deployed in Europe, the United Kingdom, United States, and Asia. Methodologies and data choices from the most renowned data providers have been tailored to reflect the highest degree of robustness and compliance in each market, including Solvency II, IFRS 17, and LDTI.
Use advanced and custom parameters for model selection, data loading, calibration, and simulation steps.
Enable ESG experts and risk managers to exhaustively validate model performance using visualizations and data.
Milliman Economic Scenario Generator (ESG) offered this client flexible capabilities with a cloud-native solution driven by the leading mathematical modeling of Milliman’s expert R&D team.
With deep customization capabilities, Milliman Economic Scenario Generator (ESG) provided the long-term volatility modeling at the time required.
By providing an effective solution alongside individualized support, the client found a better way to satisfy regulators.
Leveraging Milliman Integrate architecture, Milliman Economic Scenario Generator was integrated into this insurer’s framework with the ability to deliver up to 10,000 scenarios at D+1
We offer some valuable insights into scenario reduction and trajectory selection for stochastic ALM valuation that could help improve results and minimize computation.
This paper investigates how the choice of financial data can impact the calibration and the simulation of credit spread (credit default) scenarios within an economic scenario generator, as well as the insurance liability…
Increasing use of stochastic economic scenarios for valuation of liabilities has put more pressure on the operational process of carriers, but the RNG can help.
We highlight a Libor market model with constant elastic volatility, showing an interesting trade-off between parameters used and quality of results.
Many insurance companies are struggling to overcome the computational challenges involved in computing the SCR under the Solvency II regime.
With better understanding of climate transition risk exposure and stress tests proposed by regulators, internal models can help provide insights.
This paper explores options available to address the challenge of deriving market-consistent but stable long-term volatility assumptions for valuation of liabilities.
Challenges for companies in the alternative asset space include building an internal model and accounting for regulatory treatment for capital set aside.
We describe a recent realistic modelling approach of equity volatility that offers advantages when using real world economic scenarios to analyze balance sheets.
Least Squares Monte Carlo (LSMC) is a widely used proxy modelling technique in the European insurance industry.
Sensitivity testing with dependence has the potential for a wide range of applications in reporting, such as for Solvency II, IFRS 17, and balance sheet valuation.
We present a calibration technique for one complex risk neutral model, relying…
The best 'one-size-fits-all' economic scenario generator solution is one that…
The European Insurance and Occupational Pensions Authority (EIOPA) in 2019…
The principles-based approach under International Financial Reporting Standard…
In the landscape of Solvency II internal models, market and credit are…
This report details the use of cloud computing and machine learning in the…
This paper is part of a series covering the overall process for discount rates…
This paper presents a solution to address (re)insurers’ challenges,…
IFRS 17 requires preparers of accounts to derive discount rates for the…
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