Questions tagged [fraud-detection]
Fraud detection using statistical and machine learning methods
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Test Distribution Difference between small sample sizes
My first post of StackExchange.
I am working on data analysis project for comparison of fraud and nonfraud transactions. My objective is to compare the feature vectors of nonfraud transactions and ...
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Books on data fraud detection?
Do you have any good book recommendations on the topic of detecting data manipulation and scientific fraud by statistical analysis (or any other mathematical approach really)?
I see that there are ...
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Probability for the lottery extraction to be rigged
This draw of lottery, based on 7/39 numbers stirred a lot of debates, and an obvious anger of general public here in Serbia. I ran all kinds of scenarios in my head about what really happened here (or ...
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Do imbalanced datasets make removing poor target predictors easier?
I have an imbalanced fraud dataset with ~$1.4$% fraudulent samples across 50,000 rows with 600 columns. I'm performing a binary classification task on this dataset.
I've performed an EDA; some columns ...
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Fraud detection feedback loop
I’ve got an interesting question at one interview.
Assume that we already trained and deployed some fraud detection model for some online service, and it has helped us to decrease amount of fraudulent ...
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Appropriate means comparison test to detect fraud in population dataset by re-measuring outcome over a sample?
As part of a project, organization A contracted organization B to deliver an outcome $X$ across $N=500$ units. B compiled a dataset on $X$ which accounts for the entire population.
$X$ is a discrete ...
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Understanding an outlier detection technique for fraud detection
I came across this article:
http://projetoaprendizagemgrupo4.pbworks.com/f/03.03%20-%20Unsupervised%20Profiling%20Methods%20Fraud%20Detection.pdf
since I am interested in detecting abnormal behavior (...
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How to add account_numbers as input to neural network
Let's say we have a case of money laundering detection, and the only identification for customer and business is their bank_account numbers. How can we encode them for the input to neural networks. ...
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How to properly apply Benford's Law to apartment service charge data?
I want to apply Benford's Law to a breakdown of billing information we recieve for our apartment building's service charges. In other words, I want to compare the distribution of my service charge ...
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Unsupervised anomaly detection and classification with event (log) data
I am trying to detect anomalies in a large set of user log events, where most users would be considered “good” and a small minority would be considered “bad.” There are hundreds of event types, which ...
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Is there a way to verify if numeric data was falsified? Would a simple randomness test work in this situation?
I have a string of numbers that are intended to represent certain stats over time, but some of the numbers appear to be staged to my eye, including a lot of repeating integers (e.g. 121, 5335, etc.) ...
mutatedsyntax
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Sensible data set structure for fraud prediction
I am fairly new to machine learning and am currently working on the following problem. I am trying to compare several machine learning algorithms (ANN, SVM, random forests) regarding their efficacy to ...
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How can I build a model that could predict that a credit card transaction is fraud or not based on previous transactions of the same card [closed]
I am working on a project where I am supposed to detect fraud in credit card transaction. I generated a dataset that looks like this:
supposedly this is the transactions in a database. The job ...
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Benford's law for categorical variables?
I have a dataset on avoided maritime accidents (near-miss) that looks like this:
[
All variables are categorical (type=1-3, position=1-5, area=1-5, risk=1-7, 4 columns, 525 rows - every row is 1 near ...
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How to calculate the best threshold value
I'm working at a motor insurance company and want to build a business rule to detect fraud cases based on the damage value. I have a historical data set that contains a list of accidents info, damage ...
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Method to determine outliers with a skewed dataset [duplicate]
How can we find outliers in a dataset with a (highly) skewed distribution? With a normal distribution, is it well documented to use 2 x Standard Deviation or the upper boundary of the box plot (1.5 x ...
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Feature engineering: including counter-parties of a transaction in a dataset
Background
Say I have a dataset of transfers between bank accounts structured like so:
...
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Random undersampling: is there a way to chose the best majority samples?
I'm modeling credit fraud, where I have a small number of samples that result in fraud (1), and most samples that are not fraud (0). I am creating a models for detecting fraud based on new data.
I'm ...
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Feature engineering using the target/dependent variable
I am a beginner and my question relates to feature engineering. My task is to help develop a model which predicts whether a customer request is a fraud case.
A variable in the dataset is the ...
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How can I make use of zip codes when I am building a model for fraud detection
I have gone through few articles but I am not convinced on what should I do with these. I know from business standpoint it might be good to consider fraudulent transactions happening from unknown ...
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How to detect possible fraudulent administration of a survey questionnaire?
I'm involved in a survey on a highly sensitive topic, where I have reason to believe that the subcontractor who was responsible for data collection (a call centre) may have been pressured / paid / ...
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Combining classification models for fraud detection
i have a classification problem : fraud / non fraud. My classes are inbalanced ( 0.8% fraud rows ).
I first split my data in train and test sets.
Let's say I have 10 fraud and 100 non fraud rows in ...
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Question about sample size for each class for machine learning classifiers
I'm trying to use a machine learning classifier (SVM in particular) on data that I generate. Unlike other applications, the data is not given to me but rather I have the flexibility to generate how ...
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What test should I use to compare 2 group with very similar characteristic?
I am currenty doing a fraud detection analysis. I have 2 groups of data points with several features that I create myself. 1 group is fraud people and another group is non fraud.
I found out that ...
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Feature engineering for fraud detection
I'm doing some research into fraud detection for academic purposes. I' d like to know specifically about techniques for feature selection\engeneering from a transactional dataset. In more details, ...
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How to detect fraudulent values in a data set?
First off, I don't know if this is the appropriate place to ask this question? If not, I apologize, and I'd appreciate any advice on where else to go with it. If it's ok, here's a description of my ...
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Would you flag this data as fraudulent?
Let's suppose you have been given some data from a randomized block design with 4 repetitions and 23 treatments. After an initial inspection of the data, you notice that for 8 treatments all ...
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Hierarchical Dependent Variable
I am building a predictive fraud model for insurance claims. Fraud can be at claim level or service level. "Service level" is more granular. There are multiple services under one claim. See the ...
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How to compare two methods with censored and bias data
I will use an example of comparing two different antifraudes to better illustrate the statistical problem.
(Antifraud is an algorithm that analyzes whether a transaction is a real transaction or a ...
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Alternatives to logistic regression when data is unbalanced [duplicate]
Using Python's pandas, sklearn, and stats models API, I have trained a logistic regression on a training dataset that tells me whether or not a particular purchase (based on gender, and other features)...
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Is an Average Precision of 60% acceptable output in a fraud detection machine learning algorithm? What does it signifies?
First question here, I am new to machine learning and wanted to understand the following:
I used decision trees, boosting to classify fraud users and I am getting average precision around 60% on my ...
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any data i can use for healthcare fraud detection [closed]
I am truly desperate in looking for a healthcare fraud data for my project on fraud detection. Can some kind soul please please help me? I have searched online but found nothing..May i know where to ...
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Methods to detect fraudulent test data
I work for a company that manufactures widgets and we test each widget before it leaves the factory. One of the parameters that we ask our testers to manually record is the cycle time. Lately I've ...
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Experimentally testing classification models
I have implemented a machine learning algorithm (say, algorithm A) to perform binary classifications of fraudulent and non fraudulent clients. I am already using other algorithms (say, B and C) to ...
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How to retrain a production classifier that blocks its own positive examples?
I'm looking for help understanding how to re-train a fraud detection classifier that's been deployed to production (where it successfully blocked much, but not all fraud coming into the system). I ...
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Modeling rare events
I am looking to model fraudulent cases using logistic regression. However there are tow different datasets which are available.
I used to build my model on; it had 4% of fraud cases. My model on this ...
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Strange election results and probability of election fraud
Suppose an election is held for the leadership position in a major political party. Four candidates are running. After the election, the following results are announced:
...
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Analysis of PValues Matrix
in clinical environment I'm setting up a model to identify fraudolent behaviours from specific sites, calculating for each site some aspects that could lead to a p-value (this approach is based on a ...
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Benford's law: analyse individual variables or the entire dataset?
In his 1995 paper, Hill points out that random samples from random samples will usually give rise to data that satisfy Benford's law. He mentions a newspaper frontpage as an example where data may ...
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data mining methods/algorithms for fraud case
I recently got into a topic regarding fraudulent transactions. I am relatively new to data mining and just looking for some input for my case here.
I started with a cluster analysis / anomaly ...
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How to improve a Fraud Classification Model?
I built a classification model (Logistic Regression) in order to classify data in Fraud or Not Fraud.
This data is related with online CNP (Card Not Present) transactions and after choosing some ...
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Machine Learning problem - identifying fake fraudulent names
I have a dataset of fraudulent orders from some business. Each order has a bunch of features such as order_amount, address, state, city, phone_number, and name. Obviously a criminal would not be ...
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Machine learning to catch fraud
I work for a company that ships material (a couple of thousand shipments per day) around the world.
In order to ship anything a customer has to declare the weight of a shipment and declare the ...
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Can p-values for Pearson's correlation test be computed just from correlation coefficient and sample size?
Background: I read one article where authors report Pearson correlation 0.754 from sample size 878. Resulting p-value for correlation test is "two star" significant (i.e. p < 0.01).
However, I ...
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Medical Insurance Fraud Detection: Text analysis
I'm trying to analyse a dataset to detect fraudulent insurance claims. Unfortunately, other than basic demographics the rest of the claim is a free format OCR scanned text file made from documents ...
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Identifying fraudulent questionnaires
Questionaires are often used in social sciences.
Many people try to complete them very quickly and very often they only "guess" answers.
Is there any statistical technique or any research in this area,...
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How to apply cluster analysis to help identify criminal entities out from credit card usage data?
Let's say I have a ton of credit card usage data and have also some means to predict if a given transaction is fraudulent. Now I want to know what kind of criminal entities are behind these frauds. ...
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How is election fraud by ballot stuffing possible?
An NPR story on the upcoming Russian presidential election mentioned that 5% of polling sites would be equipped with new electronic ballot boxes that would reject attempts to submit multiple ballots. ...
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Election forensics using statistical methods in practice?
Are you aware of any examples of election forensics in practice? Or at least any applied research on real large-scale datasets (e.g. govermental elections)? Thanks.
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Reading recommendation on using statistical analysis in online fraud prevention
Can you please recommend good reads on statistical analysis related to online fraud detection and prevention of account abuse?
Thank you.
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