Grouping of Insurance Policies Meets Artificial Intelligence
Deloitte Grouping Optimizer Machine Learning – how actuarial projections for life/health insurers can run faster and cost less
We have developed the Deloitte Grouping Optimizer Machine Learning (DGO ML) based on its predecessor – Deloitte Grouping Optimizer – for the grouping of large portfolios of life/health insurance contracts. At its core the solution uses an advanced mathematical model which is implemented with the help of the latest technologies. DGO ML can be easily integrated within your IT architecture.
The insurance industry is in the midst of a profound change. It faces a variety of challenges, such as accelerating automation, digital transformation and increased financial reporting requirements stemming from IFRS 17, Solvency II revision and asset liability management. All over the world insurers are recognizing the need to improve their data processing for life/health actuarial projections. Machine learning techniques can be effectively applied to optimize existing procedures and cope with those challenges in an adequate manner.
A compact overview of the DGO ML advantages
Runtime reduction of your actuarial projections
A small but representative selection of portfolio contracts (model points) is determined in a highly efficient and reliable way, reflecting the composition of your complete portfolio. This enables you as a user to perform actuarial calculations only for these resulting model points almost without losing accuracy in the results. This leads to a significant reduction of the actuarial projections run time.
The entire DGO ML process is fundamentally designed to meet the high traceability requirements of various accounting standards.
Insurance portfolio grouping with DGO ML
The experience of our national and international DGO ML customers shows: The machine learning techniques contribute to an enormous runtime reduction of actuarial projections and effort reduction for those involved in calculations. Therefore, actuaries can invest more time in important analyses of the results and extensive discussions with other departments.
A user-friendly graphical interface ensures intuitive handling. It is additionally possible to steer DGO ML utilizing your workflow engine which allows the grouping process to be performed in a fully automated manner.
DGO ML – versatile support
DGO ML will support you with regard to the segmentation requirements of IFRS 17 as well as meeting the reporting deadlines according to Solvency II and the deadlines of your fast close process. The reduced runtime of your actuarial model will provide you more room for an effective asset liability management.
Contact us. We are available with our expertise
Please feel free to contact us. We will be happy to support you with your questions and carry out an individual test grouping for your portfolio.