Identify fraudulent activity quickly and end it

Fraud eats at many organizations – financial services companies, healthcare providers, government agencies, and more. It not only negatively impacts profitability and other business results, but also the ability to serve customers and achieve mission and purpose. The faster and more accurately you can spot fraud, the more likely you are to put a stop to it in time. Data science can revolutionize the fraud detection process. Use all available data to identify non-obvious fraud patterns, and monitor operations to spot fraudsters when they’d otherwise remain hidden.

Identify patterns of fraud

Move beyond simple fraud identification methods by identifying complex patterns to watch for. Update models frequently as perpetrators shift behavior to avoid detection.

Monitor activity for signs of fraud

Apply patterns and models to large volumes of streaming data, constantly watching for signs of suspicious behavior. Leave no place for perpetrators to hide.

Stop fraud quickly

Detect fraud quickly and early enough to take action before it has a widespread damaging impact.

Reduce costs, improve service

Preventing or minimizing fraud reduces losses and associated costs and protects margin and profit. It also frees organizations to focus on legitimate customers and provide better service.

Fraud Detection Use Case

“Machine learning allowed the US state auditor to integrate and consider various data sources, create meaningful features and scores, provide context and explanations, and detect networks of fraudsters.”

Get started on your fraud detection project today!

View Other Use Cases

Churn Prevention

Identify customers likely to leave, take preventative action.

Customer Lifetime Value

Distinguish between customers based on business value.

Customer Segmentation

Create meaningful customer groups for more relevant interactions.

Demand Forecasting

Know what volumes to expect to improve planning.

Next Best Action

The right action at the right time for the right customer.

Predictive Maintenance

Predict equipment failure, plan cost-effective maintenance.

Price Optimization

Set prices that balance demand, profit, and risk.

Product Propensity

Predict what your customers will buy, before even they know it.

Quality Assurance

Resolve quality issues before they become a problem.

Risk Management

Understand risk to manage it.

Text Mining

Extract insight from unstructured content.

Up- and Cross-Selling

Convince customers to buy more.