| SGF |
2019 |
The Anatomy of an Artificial Intelligence Solution for Automation of Underwriting Decision-Making |
Tanya Kolosova |
source |
17 |
| WUSS |
2018 |
The Anatomy of Artificial Intelligence Solution for Automation of Underwriting Decision-Making Process. |
Samuel Berestizhevsky, Tanya Kolosova |
source |
16 |
| SGF |
2018 |
The Cox Hazard Model for Claims Data |
Tanya Kolosova |
source |
16 |
| SCSUG |
2017 |
The Cox Hazard Model for Claims Data |
Samuel Berestizhevsky, Tanya Kolosova |
source |
17 |
| MWSUG |
2017 |
The Cox Hazard Model for Claims Data: Bayesian non-parametric approach |
Samuel Berestizhevsky, Tanya Kolosova |
|
17 |
| WUSS |
2017 |
The Cox Hazard Model for Claims Data |
Samuel Berestizhevsky, Tanya Kolosova |
source |
17 |
| SGF |
2013 |
Extraction, Transformation, and Loading (ETL) for Outcome Measures of Workers? Compensation Benefits |
Mike Maier |
source |
10 |
| SGF |
2011 |
Why Government Can Take the Lead in Fighting Fraud, and Use the SAS Fraud Framework to Get There |
Carl Hammersburg |
source |
8 |
| SUGI |
2005 |
Predicting Workers' Compensation Insurance Fraud Using SAS Enterprise Miner 5.1 and SAS Text Miner |
Terry J. Woodfield |
source |
6 |
| NESUG |
2004 |
Analyzing Complex Survey Data: Some SAS-SUDAAN Comparisons |
Xiuhua Chen, Paul Gorrell |
|
15 |
| SUGI |
2001 |
Predictive Modeling in the Insurance Industry Using SAS |
Terry J. Woodfield |
source |
5 |