Learning Theories
Pedagogical Strategies
Fraud Background
Analytic View of Fraud and Challenges
Feature Extraction
Analytic Fraud Identification Methods and Assessment
Deployment and Automation
Cross-Industry Standard Process for Data Mining (CRISP-DM)
There are many learning theories. They all fall under the three major theories.
Behaviorism Learning Theory: knowledge is independent and on the exterior of the learner. It focuses on the outside environment’s influences on learning.
Cognitive Learning Theory: processing information received rather than just responding to a stimulus as in behaviorism learning theory. It uses metacognition - “thinking about thinking”—to understand how thought processes influence learning .
Constructivism Learning Theory: constructing learning new ideas based on the prior knowledge and experiences through active engagement with the world (such as experiments or real-world problem solving)
I am a firm believer in constructivism learning theory.
Knowledge is constructed. This is the basic principle, meaning that knowledge is built upon the foundation of previous learning.
Learning is a social activity. Learning is something we do together, in interaction with each other, rather than an abstract concept.
There is no knowledge independent of the meaning attributed to experience (constructed) by the learner, or community of learners.
Learning is contextual: we do not learn isolated facts and theories that are separated from the rest of our lives.
Motivation is key to learning. Cognitive motivation is rooted in the availability of information and past experience/ prior knowledge.
Providing experience with the knowledge construction process - students determine how they will learn.
Providing experience in and appreciation for multiple perspectives - evaluation of alternative solutions.
Embedding learning in realistic contexts - authentic tasks.
Embedding learning in social experience – collaborative learning.
Encourage awareness of the knowledge construction process - reflection, metacognition.
Facilitate students to make sense of information presently available and in determining how to respond or relate to the current situation.
Data Preparation - thinking of automation in the phase.
Modeling - train / retrain models and algorithms according to the change in the fraud dynamics.
Credit card fraud is a form of identity theft that involves an unauthorized taking of another’s credit card information for the purpose of charging purchases to the account or removing funds from it.
Credit Card Fraud Types: Credit card fraud schemes generally fall into one of two categories of fraud: application fraud and account takeover.
Identity theft
Why Combat Credit Fraud Loss: Card fraud over the next decade will cost the industry a collective $408.50 billion in losses globally, according to an annual report from the industry research firm Nilson Report.
Pre-authorization: timestamp, geo-info of POS, Card information (card number, expiration date, billing address, security code)
Authorization: Pre-auth info + requested payment amount
Authentication: the issuing bank will
verify the authorization information sent from the processor: validating card info and checking the availability of funds (credit line); and
send the result of the authentication to the merchant: approval or denial.
The merchant will send the complete transaction information to the issuing bank or the processor.
Based on credit card processing and the general fraud detection system, The following information is available in different processing stages:
Pre-authorization Data
Authorization and Authentication Data
Historical Data
Other Publicly Data: crime rate, etc.
Goal: detect/identify fraudulent transactions.
Challenges:
No information about fraudsters!
Real-time detection.
the rarity of fraud.
What information is relevant?
Current transaction: card info, timestamp, amount, POS info.
Historical transactions: timestamp, amount, POS info, fraud labels.
Account information: Card holder’s info.
Derived merchant site info (including publicly available info).
Key Point: Fraudulent activity alters genuine customers’ spending patterns!
Cross-sectional Data: current transactions.
Longitudinal /Panel Data: current and historical transactions
Hybrid Cross-sectional and Longitudinal Data: both current transactions and aggregated information of historical transactions
Business rules (expert system).
Supervised classification models/algorithms
Unsupervised anomaly detection methods
Other probabilistic models/algorithms such as HMM.
The transaction dollar amount is significantly different from that of genuine customers.
The genuine customers spending frequency will be changed.
The genuine customers’ transaction gap times (time between consecutive transactions) will be changed.
Process capability compares the output of an in-control process to the specification limits by using capability indices.
Illustration: Defining a fraud index using historical payment dollar amounts.
idx=(USL−μ)29(max−μ)2+(T−μ)2
The fraud index will be used as a feature variable.
Models and algorithms need to account for imbalance labels.
Firth penalized logit models.
King and Zeng's rare event logistic model.
Qing's semi-parametric logistic model.
penalized tree-based algorithm (including BAGGING. RF is not an option for this particular case).
regular logit models based on over-/under sampled data.
asymmetric-link GLMs.
The Champion/challenger scheme in the real world DM systems.
Continuous updating models/algorithms - retraining/retesting
Importance of automation in the DM process.
There are many moving parts in the definition of the fraud index and the ways of using it. Even with the same data, students can build their projects using the combination of the following
Methods of estimating USL and LSL
One-sided fraud indexes?
parametric and parametric indexes?
Supervise methods using both labels and index
statistical models
machine learning algorithms
Index as a standalone algorithm - high quantile decision boundary
parametric distribution of the indexes
non-parametric distribution of the indexes
Learning Theories
Pedagogical Strategies
Fraud Background
Analytic View of Fraud and Challenges
Feature Extraction
Analytic Fraud Identification Methods and Assessment
Deployment and Automation
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