| EPSRC Reference: |
GR/R55191/01 |
| Title: |
FEDAURA |
| Principal Investigator: |
Austin, Professor J |
| Other Investigators: |
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| Researcher Co-Investigators: |
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| Project Partners: |
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| Department: |
Computer Science |
| Organisation: |
University of York |
| Scheme: |
LINK |
| Starts: |
01 November 2001 |
Ends: |
31 December 2004 |
Value (£): |
362,920
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| EPSRC Research Topic Classifications: |
| Information & Knowledge Mgmt |
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| EPSRC Industrial Sector Classifications: |
| Communications |
Financial Services |
| Retail |
Information Technologies |
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Summary on Grant Application Form |
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This is a collaborative project under the DTI Management of Information Programme LINK initiative, in partnership with the Benefits Agency, EDS Ltd., SEMA Ltd., Cybula Ltd., and Sun Microsystems. The overall aim is to develop better methods for detecting fraud in the benefits system. The work is based primarily on the use of neural network based methods. The York part of the project (funded by EPSRC) will extend the AURA binary neural network methods for use in detecting fraud in social security benefits claims. It will extend the AURA methods to incorporate individual claims as cases and use methods based on the k-NN classification methods to assess the level of fraud risk of a given claim. The AURA methods will be extended and evaluated for use on the very large (>10million) cases typical in social security fraud problems. It will develop the preand post processing needed to undertake this. The work will be compared with other existing and other neural network methods as well being assessed in live trials.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.york.ac.uk |