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http://hdl.handle.net/11718/23047
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DC Field | Value | Language |
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dc.contributor.author | Dutta, Goutam | - |
dc.contributor.author | Rao, Harish V. | - |
dc.contributor.author | Basu, Sankarshan | - |
dc.date.accessioned | 2020-06-01T06:53:00Z | - |
dc.date.available | 2020-06-01T06:53:00Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Basu, S., Rao, H. V., & Dutta, G. (2018). New asset liability management model with decision support system for life insurance companies: interface design issues for database and mathematical models. International Journal of Revenue Management, 10(3-4), 259-289. doi:10.1504/IJRM.2018.10016758 | en_US |
dc.identifier.issn | 1741-8186 | - |
dc.identifier.uri | http://hdl.handle.net/11718/23047 | - |
dc.description.abstract | We introduce a new asset liability management (ALM) model based on decision support system (DSS) for a life insurance firm. The DSS is based on multi-stage stochastic linear programming (SLP) with recourse for strategic planning. The model can be used with minimal knowledge of management sciences. The model maximises the expected value of total reserve (policy holders’ reserve and shareholders’ reserve) at the end of the time period of planning with constraints both on the asset and the liability side of the firm’s balance sheet. We discuss issues related to DSS interface design, one to one correspondences between the SLP model and the database and the difficulty in multi stage DSS compared to two stage DSS. We also compare and contrast the similarities and differences with our earlier work on SLP based DSS for process industries. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Inderscience | en_US |
dc.title | New asset liability management model with decision support system for life insurance companies: interface design issues for database and mathematical models | en_US |
dc.type | Article | en_US |
Appears in Collections: | Journal Articles |
Files in This Item:
File | Description | Size | Format | |
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New asset liability management.pdf Restricted Access | 3.33 MB | Adobe PDF | View/Open Request a copy |
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