Use Case

Financial Fraud Detection

 

Models for detecting financial fraud continue to get more robust, but detecting 100% of fraud depends on testing 100% of transactions and blocking 100% of the fraudulent ones.

Risk Mitigation

Automatically detect fraud across 100% of transactions

Efficiency Gains

Reduce operations costs spent on fraud detection

Customer Experience

Reduce rate transactions are incorrectly flagged as fraud

Business Problem

Financial fraud affects everything from gift cards to home mortgages, often involving identity theft along the way. When a consumer’s accounts are fraudulently accessed or used, the goal is for detection engines to stop activity immediately. But in practice, multiple fraudulent transactions often go through before the engines block the accounts. As a result, financial institutions suffer losses and consumers endure inconvenience or, worse, identity theft. The ideal solution would check 100% of financial transactions across multiple data sources for fraud, then stop every transaction that the rules determined was fraudulent.

Alteryx Solution

Detecting fraud usually depends on correlating data from multiple sources. Most organizations aren’t set up for examining transactions across silos, so even if they can detect that fraud is taking place, they have trouble proving or blocking it.

With analytics, companies can automate the testing of 100% of data points, then identify outliers that indicate risk and investigate further. With insight into real-time, transactional data, investigators can readily see fraud as it is occurring and take immediate action to reduce the losses stemming from the fraud.

Alteryx Machine Learning provides a scalable machine learning platform with no coding required, where data from past fraud can be used to predict future fraud. Our platform will even recommend a machine learning model that best fits the data and goal. Once the model is created, you can use that model to predict fraud in future transactions.

 

1 – Data Connection

Select target you want to predict, and automatically generate predictive models with no coding required

2 – Advanced Analytics

Find the correlation of features to fraudulent transactions

3 – Communicate Results

Export results to communicate and operationalize model output

 

The ability to see near real-time transactions and confirm fraud has been a game changer

Scott Peacock, Director, Data Analytics and Insight

Walmart

 

Additional Resources

 
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