DBSCAN — a density-based unsupervised algorithm for fraud detection

According to a recent report financial losses due to fraudulent transactions have reached about $17 billion USD, with as many as 5% of consumers experiencing fraud incidents of some kind.

In light of such a big volume of financial losses, every industry is taking fraud detection seriously. It’s not just the financial industries that are susceptible, anomalies are prevalent in every single industry and can take many different forms — such as network intrusion, disturbances in business performances and abrupt changes in KPIs etc.

Fraud/anomaly/outlier detection has long been the subject of intense research in data science. In the ever-changing landscape of fraud detection, new tools and techniques are being tested and employed every day to screen out abnormalities. In this series of articles, so far I’ve discussed six different techniques for fraud detection:

Today I’m going to introduce another technique called DBSCAN — short for Density-Based Spatial Clustering of Applications with Noise.

As the name suggests, DBSCAN is a density-based and unsupervised machine learning algorithm. It takes multi-dimensional data as inputs and clusters them according to the model parameters — e.g. epsilon and minimum samples. Based on these parameters, the algorithm determines whether certain values in the dataset are outliers or not.

Below is a simple demonstration in Python programming language.

#fraud-detection #machine-learning #anomaly-detection #outlier-detection #data-science

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DBSCAN — a density-based unsupervised algorithm for fraud detection

DBSCAN — a density-based unsupervised algorithm for fraud detection

According to a recent report financial losses due to fraudulent transactions have reached about $17 billion USD, with as many as 5% of consumers experiencing fraud incidents of some kind.

In light of such a big volume of financial losses, every industry is taking fraud detection seriously. It’s not just the financial industries that are susceptible, anomalies are prevalent in every single industry and can take many different forms — such as network intrusion, disturbances in business performances and abrupt changes in KPIs etc.

Fraud/anomaly/outlier detection has long been the subject of intense research in data science. In the ever-changing landscape of fraud detection, new tools and techniques are being tested and employed every day to screen out abnormalities. In this series of articles, so far I’ve discussed six different techniques for fraud detection:

Today I’m going to introduce another technique called DBSCAN — short for Density-Based Spatial Clustering of Applications with Noise.

As the name suggests, DBSCAN is a density-based and unsupervised machine learning algorithm. It takes multi-dimensional data as inputs and clusters them according to the model parameters — e.g. epsilon and minimum samples. Based on these parameters, the algorithm determines whether certain values in the dataset are outliers or not.

Below is a simple demonstration in Python programming language.

#fraud-detection #machine-learning #anomaly-detection #outlier-detection #data-science

Ismael  Stark

Ismael Stark

1618128600

Credit Card Fraud Detection via Machine Learning: A Case Study

This is the second and last part of my series which focuses on Anomaly Detection using Machine Learning. If you haven’t already, I recommend you read my first article here which will introduce you to Anomaly Detection and its applications in the business world.

In this article, I will take you through a case study focus on Credit Card Fraud Detection. It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. So the main task is to identify fraudulent credit card transactions by using Machine learning. We are going to use a Python library called PyOD which is specifically developed for anomaly detection purposes.

#machine-learning #anomaly-detection #data-anomalies #detecting-data-anomalies #fraud-detection #fraud-detector #data-science #machine-learning-tutorials

Michael  Hamill

Michael Hamill

1618310820

These Tips Will Help You Step Up Anomaly Detection Using ML

In this article, you will learn a couple of Machine Learning-Based Approaches for Anomaly Detection and then show how to apply one of these approaches to solve a specific use case for anomaly detection (Credit Fraud detection) in part two.

A common need when you analyzing real-world data-sets is determining which data point stand out as being different from all other data points. Such data points are known as anomalies, and the goal of anomaly detection (also known as outlier detection) is to determine all such data points in a data-driven fashion. Anomalies can be caused by errors in the data but sometimes are indicative of a new, previously unknown, underlying process.

#machine-learning #machine-learning-algorithms #anomaly-detection #detecting-data-anomalies #data-anomalies #machine-learning-use-cases #artificial-intelligence #fraud-detection

Nat  Kutch

Nat Kutch

1595045340

Fraud detection — Unsupervised Anomaly Detection

One of the greatest concerns of many business owners is how to protect their company from fraudulent activity. This concern motivated large companies to save data relative to their past frauds, however, whoever performs a fraud aims not to be caught then this kind of data usually is unlabeled or partially labeled.

On this article, we will talk about how to discover frauds on a credit card transaction dataset, unlike most fraud datasets this dataset is completely labeled however, we won’t use the label to discover frauds. Credit card fraud is when someone uses another person’s credit card or account information to make unauthorized purchases or access funds through cash advances. Credit card fraud doesn’t just happen online; it happens in brick-and-mortar stores, too. As a business owner, you can avoid serious headaches — and unwanted publicity — by recognizing potentially fraudulent use of credit cards in your payment environment.

One of the most common approach to find fraudulent transactions was randomly select some transactions and ask and auditor to audit it. This approach was quite unaccurate since the relation between the number of fraudulent transactions and normal transactions is close to 0.1%.

Then, we aim to leverage machine learning to detect and prevent frauds and make fraud fighters more efficient and effective. Commonly, there are the supervised and the unsupervised approach:

Image for post

Also, these models can then be deployed to automatically identify new instances/cases of known fraud patterns/types in the future. Ideally the validation of this type of machine learning algorith sometimes need to be a temporal validation since fraud patterns can change over time, however to simplify this article, the validation will be simplified.

The dataset

The project uses a dataset of around 284000 credit card transactions which have been taken from Kaggle.

Credit Card Fraud Detection

The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions. It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, the original features and more background information about the data are not provided. Features V1, V2, …, V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are “Time” and “Amount”, and there are no null values (Dataset page).

Since just the “Time” and “Amount” features are easely intepreted, we can use some visualizations to see the impact of the features on the target variable (fraud). First, frauds happen more on small transactions or big ones?

#anomaly-detection #deep-learning #fraud #artificial-intelligence #machine-learning #deep learning

Vern  Greenholt

Vern Greenholt

1596024840

Focus On Growing Your Good Customer Base While Keeping Bad Actors Out

Achieving complete protection for the entire customer lifecycle is a challenge in today’s fast-evolving fraud and risk landscape, but it’s essential that businesses have these capabilities. Digital fraudsters have access to an unending stream of illicit data they can use for a wide range of advanced attack tactics, and they’re additionally harnessing the power of AI and automation to mount bot-powered attacks of unprecedented speed and scale. Given how rapidly businesses are migrating essential services online, and the extent to which more and more transactional activity is taking place on mobile, a vastly expanded attack surface area is emerging that demands a new approach to fraud detection.

DataVisor’s advanced fraud and risk management platform, and the products that comprise it, was built to make the goal of complete protection for the entire customer lifecycle a reality — today.

I write in detail about these products — and how they serve to advance the goal of complete protection — in a new blog post titled “How to Choose a Fraud and Risk Management Platform That Delivers Complete Protection for the Entire Customer Lifecycle.”

In my article, I cover the following products:

dVector

dCube

dEdge

Feature Platform

Advanced Rules Engine

I also address case management and our machine learning engine. I discuss each of the products independently, and also detail how they seamlessly integrate to offer a comprehensive fraud solution. As I note in my conclusion:

“Complete protection for the entire customer lifecycle is about ensuring exemplary experiences for good users of a platform, while simultaneously blocking bad actors from infiltrating and infecting that platform.”

#fraud-detection #fraud #fraud-prevention #ai #machine-learning #deep learning