Data Driven Government

September 25, 2019 – Washington, D.C.

Intended Audience: Fraud analysts and managers interested in learning the latest array of data science and machine learning techniques useful for reducing and eliminating fraud, waste, and abuse.

 Knowledge Level: Attendees should have some basic experience with data-driven modeling.

Workshop Description

Dramatically reducing fraud and other abuse is a challenging task requiring a comprehensive and flexible analytics solution. Wherever your organization is along the wide spectrum of fraud analytics capabilities — from basic rules and reporting to complex network graph analysis – this workshop will show you how to build out all the components of a holistic and robust fraud analytics platform. We will review critical technical details as well as problems to watch out for based on lessons learned over years of consulting on real-world fraud cases.

This one-day session teaches best practices for fraud analytics constrained by the realities of incomplete data and unrecognized or emerging fraud patterns. Existing fraud models have proven value, but nagging concerns remain about “unknown unknowns”. A new view of the problem is required.

• You should attend this workshop to learn:

• What anti-fraud techniques work best
• Graph (network) features that highlight fraud
• How to extract information hidden in unlabeled data
• A new way to powerfully combine supervised and unsupervised models

Attendees will learn how to:

• Use new unsupervised learning techniques to identify previously unseen cases of fraud
• Recognize and remove common biases
  (such as stemming from rare known fraud cases and from selection effects)
• Prioritize anomaly types by their fraud likelihood
• Incorporate multiple techniques and processes into an effective anti-fraud platform

Intended Audience: Interested in the fundamentals of modern machine learning techniques.

Program Level: Intermediate
For this introductory-level workshop, it is helpful for attendees to already be familiar with the basics of probability and coding.

Recommend Field of Study:

• Computer Science
• Analytics
• Statistics
• Mathematics
• Finance and Marketing

Instructional Method: Group – Live
CPE Credit’s received for completion:

• 1.5 CPE Credits for Management Adv Service
• 1.5 Statistics
• 1.5 Specialized Knowledge and Apps
• 1.5 Business mgmt. and org

Workshop Description

This one-day session surveys standard and advanced methods for predictive modeling (aka machine learning).

Predictive analytics has proven capable of generating enormous returns across industries – but, with so many machine learning modeling methods, there are some tough questions that need answering:

• How do you pick the right one to deliver the greatest impact for your business, as applied over your data?
• What are the best practices along the way?
• How do you make it sure it works on new data?

In this workshop, renowned practitioner and hugely popular instructor Dr. John Elder will describe the key inner workings of leading machine learning algorithms, demonstrate their performance with business case studies, compare their merits, and show you how to select the method and tool best suited to each predictive analytics project.

Attendees will leave with an understanding of the most popular algorithms, including classical regression, decision trees, nearest neighbors, and neural networks, as well as breakthrough ensemble methods such as bagging, boosting, and random forests.

This workshop will also cover useful ways to visualize, select, reduce, and engineer features – such as principal components and projection pursuit. Most importantly, Dr. Elder reveals how the essential resampling techniques of cross-validation and bootstrapping make your models robust and reliable.

Throughout the workshop day, Dr. Elder will share his (often humorous) stories from real-world applications, highlighting mistakes to avoid.

If you’d like to become a practitioner of predictive analytics – or if you already are and would like to hone your knowledge across methods and best practices – this workshop is for you.

What you will learn:

• The tremendous value of learning from data
• How to create valuable predictive models with machine learning for your business
• Best Practices, with real-world stories of what happens when things go wrong

Prerequisites:
The workshop is filled with real-world stories and explanations of methods that are visual and analogy-based, rather than mathematical. Each section is designed to make clear the gist of its concept to a complete novice, and to conclude with intriguing ideas for advanced researchers. Experience has shown that attendees who get the very most out of the course:

• Have some experience with programming, or algorithmic approaches to problem-solving
• Have taken an introductory course in probability or statistics … but most importantly
• Have a problem to solve that inspires and anchors their learning as techniques are introduced