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.
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.