Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
SAFE: A Neural Survival Analysis Model for Fraud Early Detection
Authors: Panpan Zheng, Shuhan Yuan, Xintao Wu1278-1285
AAAI 2019 | Venue PDF | LLM Run Details | Input Tokens: 17,341 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,793 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on two real world datasets demonstrate that SAFE outperforms both the survival analysis model and recurrent neural network model alone as well as state-of-the-art fraud early detection approaches. |
| Researcher Affiliation | Academia | Panpan Zheng, Shuhan Yuan, Xintao Wu University of Arkansas, Fayetteville, AR, USA EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Repeatability. Our software together with the datasets are available at https://github.com/Panpan Zheng/SAFE. |
| Open Datasets | Yes | We conduct our experiments on two real-world datasets: Twitter... We adopt the UMDWikipedia dataset (Kumar, Spezzano, and Subrahmanian 2015)... Our software together with the datasets are available at https://github.com/Panpan Zheng/SAFE. |
| Dataset Splits | Yes | We randomly divide the dataset into a training set, a validation set, and a testing set with the ratio (7:1:2). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments. |
| Software Dependencies | No | The paper mentions using Adam for optimization and Lifelines for CPH implementation, but no specific version numbers for any software dependencies are provided. |
| Experiment Setup | Yes | SAFE is trained by back-propagation via Adam (Kingma and Ba 2015) with a batch size of 16 and a learning rate 10 3. The dimension of the GRU hidden unit is 32. |