By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData Collective
  • Analytics
    AnalyticsShow More
    data analytics in sports industry
    Here’s How Data Analytics In Sports Is Changing The Game
    6 Min Read
    data analytics on nursing career
    Advances in Data Analytics Are Rapidly Transforming Nursing
    8 Min Read
    data analytics reveals the benefits of MBA
    Data Analytics Technology Proves Benefits of an MBA
    9 Min Read
    data-driven image seo
    Data Analytics Helps Marketers Substantially Boost Image SEO
    8 Min Read
    construction analytics
    5 Benefits of Analytics to Manage Commercial Construction
    5 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-23 SmartData Collective. All Rights Reserved.
Reading: Predictive Analytics World Addresses Risk and Fraud Detection
Share
Notification Show More
Latest News
data analytics in sports industry
Here’s How Data Analytics In Sports Is Changing The Game
Big Data
data analytics on nursing career
Advances in Data Analytics Are Rapidly Transforming Nursing
Analytics
data analytics reveals the benefits of MBA
Data Analytics Technology Proves Benefits of an MBA
Analytics
anti-spoofing tips
Anti-Spoofing is Crucial for Data-Driven Businesses
Security
ai in software development
3 AI-Based Strategies to Develop Software in Uncertain Times
Software
Aa
SmartData Collective
Aa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Analytics > Predictive Analytics > Predictive Analytics World Addresses Risk and Fraud Detection
Predictive Analytics

Predictive Analytics World Addresses Risk and Fraud Detection

DeanAbbott
Last updated: 2010/10/21 at 9:41 PM
DeanAbbott
3 Min Read
SHARE

 


 

More Read

Image

The NSA, Link Analysis and Fraud Detection

Top 10 Ways to Apply Predictive Analytics in the Insurance Industry — and Your Industry?
PAW: High-Performance Scoring of Healthcare Data
PAW: The High ROI of Data Mining for Innovative Organizations
PAW: Five Ways to Lower Costs with Predictive Analytics
Eric Siegel focused his plenary session on predicting and assessing risk in the enterprise, and in his usual humorous way, described how big, macro or catastrophic risk  often dominates thinking, micro or transactional risk can cost organizations more than macro risk. The micro risk is where predictive analytics is well suited, what he called data-driven micro risk management.
The point is well-taken because the most commonly used PA techniques are work better with larger data than “one of a kind” events. Micro risk can be quantified in a PA framework well. 
During the second day, an excellent talk described a fraud assessment application in the insurance industry. While the entire CRISP-DM process were covered in this talk (from Business Understanding through Deployment), there was one aspect that struck me in particular, namely the definition of the target variable to predict. Of course, the most natural target variable for fraud detection is a label indicating if a claim has been shown to be fraudulent. Fraud often has a legal aspect to it, where a claim can only be truly “fraud” after it has been prosecuted and the case closed. This  has at least two difficulties for analytics. First, it can take quite some time for a case to close, making the data one has for building fraud models lag by perhaps years from when the fraud was perpetrated. Patterns of fraud change, and thus models may perpetually be behind in identifying the fraud patterns. 
Second, a there are far fewer actual proven fraud cases compared to those that are suspicious and worthy of investigation. Cases may be dismissed or “flushed” for a variety of reasons ranging from lack of resources to investigate, statutory restrictions, and legal loopholes which do not reduce the risk for a particular claim at all, but rather just change the target variable (to 0), making these cases appear the same as benign cases. 
In this case study, the author described a process where another label for risk was used, a human-generated label that only indicated a high-enough level of suspicious behavior rather than only using actual claims fraud, a good idea in my opinion.

TAGGED: fraud
DeanAbbott October 21, 2010
Share this Article
Facebook Twitter Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

data analytics in sports industry
Here’s How Data Analytics In Sports Is Changing The Game
Big Data
data analytics on nursing career
Advances in Data Analytics Are Rapidly Transforming Nursing
Analytics
data analytics reveals the benefits of MBA
Data Analytics Technology Proves Benefits of an MBA
Analytics
anti-spoofing tips
Anti-Spoofing is Crucial for Data-Driven Businesses
Security

Stay Connected

1.2k Followers Like
33.7k Followers Follow
222 Followers Pin

You Might also Like

Image
AnalyticsBig DataData ManagementData MiningPolicy and GovernancePredictive AnalyticsPrivacyTransparency

The NSA, Link Analysis and Fraud Detection

5 Min Read

Top 10 Ways to Apply Predictive Analytics in the Insurance Industry — and Your Industry?

2 Min Read

PAW: High-Performance Scoring of Healthcare Data

6 Min Read

PAW: The High ROI of Data Mining for Innovative Organizations

9 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

ai is improving the safety of cars
From Bolts to Bots: How AI Is Fortifying the Automotive Industry
Artificial Intelligence
data-driven web design
5 Great Tips for Using Data Analytics for Website UX
Big Data

Quick Link

  • About
  • Contact
  • Privacy
Follow US

© 2008-23 SmartData Collective. All Rights Reserved.

Removed from reading list

Undo
Go to mobile version
Welcome Back!

Sign in to your account

Lost your password?