Fraud Detection in Python
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Learn to detect fraud in Python using resampling for imbalanced data, tuned classifiers, clustering and text mining in interactive exercises.
You build Python models that flag fraud. Labeled examples help you catch schemes similar to known cases, and clustering on unlabeled data surfaces new kinds of suspicious activity. Fraud cases are heavily outnumbered by legitimate ones, so the work starts by resampling that skewed data before any model is trained. The final chapter mines text data with term searches and topic modeling for signs of fraudulent behavior.
What You Will Achieve
- Rebalance a skewed fraud dataset with SMOTE and check how the class ratio changes
- Train a random forest with adjusted weights, tune it with GridSearchCV and judge it by precision and recall
- Segment customers with K-means or DBSCAN and flag outliers and the smallest clusters as possible fraud
- Flag suspicious records by searching text for key terms and reading the topics of an LDA model
Program Overview
Imbalanced Data and Resampling
Sets out the typical challenges of fraud detection and shows how to resample skewed data before training a first classifier.
- Fraud to non-fraud ratio and plotting the data
- SMOTE and other resampling methods
- The traditional way to catch fraud and ML classification
- Logistic regression with SMOTE in a pipeline
Supervised Fraud Detection with Labeled Data
Flags fraudulent transactions with classifiers that are adjusted and compared to find the most efficient model.
- Random forest classifier and the natural hit rate
- Performance metrics and the precision-recall curve
- Algorithm weights and GridSearchCV
- Voting classifier ensembles
Clustering and Text Mining for Fraud
Turns to unlabeled data, where normal behavior is defined so abnormal cases stand out, and then to text data and topic modeling.
- Customer segmentation and scaling
- K-means, the elbow method and DBSCAN
- Term lists, stopword removal and text cleaning
- LDA topics used to flag fraud
Certificate Awarded (Paid)
Job Outlook
Fraud analytics skills are used in financial services, insurance, e-commerce and healthcare, wherever teams screen transactions and customer data for suspicious activity.
Good for: Analysts and data scientists who want to apply classification and clustering in Python to the specific problem of catching fraud.
Keep in mind: Four detection approaches share one short course, so methods such as DBSCAN, voting classifiers and LDA each get a handful of exercises rather than deep coverage.
Bottom line: It pairs resampling and tuned classifiers with clustering and text mining, so it covers fraud that resembles past cases and fraud that does not.
Frequently Asked Questions
How does the course deal with imbalanced fraud data?
You first check the ratio of fraud to non-fraud cases and plot the data, then apply resampling methods such as SMOTE and compare the result with the original data. Later, adjusting algorithm weights in the random forest and the voting classifier offers a second way to handle the imbalance.
How can fraud be found when the data has no labels?
The unsupervised chapter defines normal behavior with statistics and customer segmentation, then scales the data and applies K-means clustering, using the elbow method to choose the number of clusters. Outliers and the smallest DBSCAN clusters are treated as suspicious and checked against the results.
What part does text data play in detecting fraud?
The last chapter works with text in dataframes. You search for a list of terms and create a flag, clean the text and remove stopwords, then build a dictionary and corpus for an LDA topic model whose topics help point to likely fraudsters.
Does the course share lessons from real fraud analytics work?
Yes. Alongside the technical methods, it passes on tips drawn from real-life fraud analytics experience to help you avoid common mistakes, and it sets the traditional way of catching fraud beside machine learning classification.
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