| SGF |
2020 |
Deploying Machine Learning Models in an Anti-Money Laundering (AML) Program |
Beth Herron, Saurabh Duggal |
source |
11 |
| SGF |
2018 |
Harvesting Unstructured Data to Reduce Anti-Money Laundering (AML) Compliance Risk |
Austin Cook, Beth Herron |
source |
10 |
| SGF |
2018 |
Using SAS Visual Investigator to Enforce Model Tuning Best Practices in a Regulatory Environment |
Scott Wood |
source |
15 |
| SGF |
2017 |
Optimizing Anti-Money Laundering Transaction Monitoring Systems Using SAS Analytical Tools |
Stephen M. Overton, Eric Hale, Leigh Ann Herhold |
source |
10 |
| SGF |
2017 |
You Imported What? Supporting International Trade with Advanced Analytics |
Susan Trueman |
source |
9 |
| SGF |
2016 |
Alerts Don't Launder Money (and Finance Terrorism)--People Do! |
Kathy Hart |
source |
7 |
| SGF |
2016 |
Arrest Prediction and Analysis based on a Data Mining Approach |
Karan Rudra, Goutam Chakraborty, Maitreya Kadiyala |
source |
9 |
| SGF |
2016 |
How to Make Your SAS Web Applications More Secure: Top Ten Tips |
Heesun Park |
source |
9 |
| SGF |
2016 |
Making Better Decisions about Risk Classification Using Decision Trees in SAS Visual Analytics |
Stephen M. Overton, Ben Murphy |
source |
13 |
| SGF |
2015 |
Addressing AML Regulatory Pressures by Creating Customer Risk Rating Models with Ordinal Logistic Regression |
Edwin Rivera, Jim West |
source |
12 |
| SGF |
2014 |
Making It Happen: A Novel Way to Combine, Construct, Customize, and Implement Your Big Data with a SAS Data Discovery, Analytics, and Fraud Framework in Medicare |
Vivek Sethunatesan |
source |
15 |
| SGF |
2010 |
Improving the Defense Lines: The Future of Fraud Detection in the Insurance Industry (with Fraud Risk Models, Text Mining, and Social Networks) |
Terisa Roberts |
source |
7 |
| SEUGI |
2003 |
Uncovering the Hidden Fiance Crime - the SAS Anti-Money Laundering Solution [presentation] |
Sascha Schubert |
source |
39 |