NJ SAS Users Group 2018 Spring Meeting

The meeting is Friday, June 1st - 9:00am - 1:00pm


The Chubb Corporation

202 Hall's Mill Rd, Building A
Whitehouse Station NJ 08889

At 1st Floor Training Rooms

For Directions Click Here


Registration will start at 9:00am, followed by four informative presentations. The meeting will be wrapped up around noon..


1. If you are planning to be with us on June 1st, click on the Register button above and pre-register. Email the completed form to mailto:nj.sas.users.group@gmail.com

When arriving for the meeting, park at Level A (ground level).
The parking garage is limited to Chubb employees only.

2. This meeting is FREE to all in attendance!

3. However, NO CONTINENTAL BREAKFAST will be served. You are free to purchase whatever you like at the adjacent Chubb cafeteria.

Your Donations are always welcome. NJ SUG relies on your support to continue operating.

  1. Contribute with Pay Pal.
    Send payment to nj.sas.users.group@gmail.com. * PREFERRED *
  2. Contribute with cash or a check at the door.
    Checks are payable to: "NJ SAS Users Group".
  3. Also, we now accept credit cards.

Your support will help us to continue hosting informative meetings about SAS.

Our Speakers

Kevin Lee, Director of Data Science at Clindata Insight

Kevin_Lee Kevin has been supporting data-driven technology nearly 20 years and a very active data-driven solution architecture including CDISC standards data development, semantic data, metadata-driven programming, data analytics, Big Data and machine learning. He is also a current member of the data standards team at CDISC and the data visualization team at PhUSE.

Kevin has presented more than 60 papers at various conferences. He earned an M.S. in Applied Statistics at Villanova University following a B.S. from the University of Pennsylvania. Kevin is a lifetime learner who loves to learn and share.

Gourish Hosangady, Executive Vice President of Data Analytics, Aithent

Gourish Hosangady Gourish is an Executive Vice President of Business Analytics at Aithent based in New York city. He is a software and consulting professional with senior management experience for the past 15 years including at executive levels for the past 10 years. His specialty is in business development, marketing and consulting to help organizations grow in revenues, profitability and customer base. He has worked with both small and large software organizations including at SAS for about 10 years in the sales and marketing organization during which time he worked as country manager in the Asia Pacific region for 5 years.

His background in analytics comprises all its components at enterprise levels: big data, data science, machine learning, business intelligence and predictive (advanced) modeling. He has done graduate studies in marketing from the University of Texas at Austin, an MS in Engineering from the University of Missouri, and BS in Engineering from the Indian Institute of Technology.

He is also a member of the NJ SUG Steering Committee for the past 5 years.

Proposed Time Schedule

  • 9:00 - 9:20 am Welcome and Registration
  • 9:20 - 10:30 am Big Data for SAS programmers by Kevin Lee
  • 10:30 - 10:45 am Break
  • 10:45 - 11:00 pm Random Access: Ask questions, make announcements, etc.
  • 11:00 - 12:00 am A Fraud Management Solution for Middle Market Banks and Ways to Reduce False Positives by Gourish Hosangady
  • 12:00 noon Farewell Announcements


Abstracts of Our Presentations


Big Data for SAS Programmers

by Kevin Lee of Clindata Insight

We are living in the world of “Big Data”. “Big Data” is mainly expressed with three Vs – Volume, Velocity and Variety. The presentation will discuss how Big Data impacts us and how SAS programmers can use their skills in Big Data environment. This presentation will introduce Big Data storage solution – Hadoop and NoSQL. First, the presentation will introduce Hadoop and its two major capabilities - Hadoop Distributed File System (HDFS) and Map/Reduce (parallel computing in Hadoop). The presentation will show how SAS can work with Hadoop using HDFS LIBNAME, FILENAME, SAS/ACCESS to Hadoop HIVE and SAS GRID Managers to Hadoop YARN. As another “Big Data” storage solution, the presentation will also introduce NoSQL database. The presentation will show how unstructured data are stored in the NoSQL database and how they are easily extracted to analytic systems (e.g., SAS, R, Python) for further analysis. Using use cases, the presentation will show how programmers can extract unstructured data (e.g., XML or JSON) from NoSQL database using REST API - PROC HTTP in SAS.  


A Fraud Management Solution for Middle Market Banks and Ways to Reduce False Positives

by Gourish Hosangady of Aithent

Financial institutions generate enormous amounts of transaction data each day. The pressure on compliance and the need for quick detection of fraud continues to increase. The same institutions need to reduce losses from penalties and fraud as a consequence. The challenge lies in how best to use a select set of rules coupled with modeling—using data science and machine learning techniques to address this challenge. Suspicious transactions should be flagged with minimal false positives. The process also should maximize productivity and create a degree of seamlessness in both alert creation and investigations. Once compliance and fraud are both addressed, further analysis of customer and transaction data might be performed to gain insights into customer behavior. Such an approach can achieve the following goals: a) Reduce false positives, achieve cost benefits. This outcome also maintains customer satisfaction as excessive false alerts cause customer attrition for banks, in addition to reputational damage; b) An ability to create new rules and thus be ahead of the game with respect to fraudsters. Rules can get outdated quickly, so tweaking thresholds and modifying rules is much needed; c) Create an end-to-end process from alert generation to case management to reporting; and d) Create a closed loop system so that data about true fraud can be fedback into the source data for corrective modeling