Fraud Detection

Online fraud is growing and it is a costly problem. It is estimated that 6 percent of the U.S Gross domestic product, which comes down to more than 5 trillion USD, is lost to financial fraud. Despite using complex fraud detection tools, often making use of AI or machine learning, businesses lose more and more money to fraud every year. Graph data software can help turn this pattern around.

Why fraud is hard

Many experts have developed accurate machine learning models to detect fraud. However, most data models ignore something critically important, the network structure. Network analysis captures the deep-rooted relationships between different data elements. Tabular data models, meaning data organized in rows and columns, are not built for capturing the complicated network structure inherent in your data. Analyzing data as a graph empowers you to reveal and use its structure for predictions.

What brings GraphPolaris to the table

GraphPolaris “deep analytics“ combined with the major graph capabilities uncovers patterns and connections which are hard to find with the naked eye. Fraud investigation teams can investigate specific connections, high-risk items or counterparty relationships using a graph modeling approach which can all be done in real-time.

GraphPolaris’s software empowers you to explore and analyze

GraphPolaris’s software enables you to explore and analyze network structures using searches, queries and graph algorithms. Graph data science is effective when: - Your data has many relationships - Your data has patterns that are hard to see - Your requirements change frequently - Your data requires context