Introduction to Module 8

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Data Analytics Foundations for Accountancy II

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University of Illinois at Urbana-Champaign

Data Analytics Foundations for Accountancy II

4 оценки

Welcome to Data Analytics Foundations for Accountancy II! I'm excited to have you in the class and look forward to your contributions to the learning community. To begin, I recommend taking a few minutes to explore the course site. Review the material we’ll cover each week, and preview the assignments you’ll need to complete to pass the course. Click Discussions to see forums where you can discuss the course material with fellow students taking the class. If you have questions about course content, please post them in the forums to get help from others in the course community. For technical problems with the Coursera platform, visit the Learner Help Center. Good luck as you get started, and I hope you enjoy the course!

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Module 8: Introduction to Anomaly Detection

This module introduces the concept of an anomaly, or outlier, and different techniques for identifying these unusual data points. First, the general concept of an anomaly is discussed and demonstrated in the business community via the detection of fraud, which in general should be an anomaly when compared to normal customers or operations. Next, statistical techniques for identifying outliers are introduced, which often involve simple descriptive statistics that can highlight data that are sufficiently far from the norm for a given data set. Finally, machine learning techniques are reviewed that can either classify outliers or identify points in low density (or outside normal clusters) areas as potential outliers.

Introduction to Module 83:46

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Robert Brunner
Professor
Accountancy

Hello. I'm standing inside the atrium of

the Civil Center which houses

the Computer Science department here at the University of Illinois.

The open atrium with a grand staircase and glass walls makes

a strong visual impression and the convenient coffee shop on the first floor,

and ample meeting spaces encourage discussion and collaboration.

In some ways, this space makes the Civil Center an outlier from most buildings on campus.

Especially those that are much older and

that were designed and constructed in a different era.

In this module, we introduce the concept of

anomaly detection which generally means finding outliers.

While the common perception is that outliers are always bad,

that is not always the case.

An outlier is generally defined as an instance in a

data set that is outside the normal ranges spanned by the data.

If you were interested in identifying your best customers,

say those that buy the most from your stores,

or visit your shops the most often,

you likely would want to find the outliers on the high side.

On the other hand, you might wish to find those who visit your store often but spend very

little to see if you can engage more productively with them to drive new revenue.

Thus, outliers are often dichotomous and that they can be

bad such as fraud or goods such as your highest revenue customers.

In either case, finding them can be both challenging and rewarding.

The first lesson in this module includes several readings on

fraud detection including from the perspective of an account.

Given the importance of identifying fraud,

most of the work an anomaly detection has been focused in finding even potential fraud.

The second lesson introduces statistical techniques for identifying outliers.

In many cases, this task simplifies to assuming a distribution for the data of

interest and using descriptive statistics to identify the data that are in the tails.

When dealing with time series data or streaming data,

these techniques can become more complicated.

But the basic concepts remain the same.

The final lesson introduces machine learning techniques for identifying outliers.

This can be done in several ways.

First, we can use clustering to find clusters of data and then identify data that

are near the edge of a cluster or that are outside of identified clusters in the data.

Second, we can use classification to

identify data that are somehow different than the rest of the data.

This is known as a one versus many classification approach.

Finally, we can use regression to model the data and then use

descriptive statistics on this new model to

identify the data that are considerably different than the rest of the data.

Fraud detection has become big business

from credit card companies providing real time alerts,

to banks working to prevent money laundering,

to stores trying to minimize theft.

New techniques are constantly being developed and deployed.

Increasingly these techniques are also finding a home in

the audit process and as data rates increase with real time reporting,

the importance of algorithmic fraud detection will only increase. Good luck.

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