Financial Services & Neo4j: Fraud Detection

Identifying and stopping fraudulent activity is harder than ever for financial services organizations. Standard anti-fraud technologies — such as a deviation from normal purchasing patterns — use discrete data. This is useful for catching individual criminals acting alone, but discrete… Read more →

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How Graph Databases Stop E-commerce Fraud in Real Time

As our lives become increasingly digital, a growing number of financial transactions are conducted online. Fraudsters have been quick to adapt to this trend, and to devise clever ways to defraud online payment systems. While this type of activity can… Read more →

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Catching Insurance Fraud Using Graph Database Technology

The impact of fraud on the insurance industry is estimated to be $80 billion annually in the US, a number that has been growing in recent years. From 2010 to 2012, questionable claims in the U.S. jumped 27 percent, to… Read more →

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Improving First-Party Bank Fraud Detection with Graph Databases

First-party bank fraud involves fraudsters who apply for credit cards, loans, overdrafts and unsecured banking credit lines, with no intention of paying them back. It is a serious problem for banking institutions. U.S. banks lose tens of billions of dollars… Read more →

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Why Modern Fraud Detection Needs Graph Database Technology

Banks and insurance companies lose billions of dollars every year to fraud. Traditional methods of fraud detection play an important role in minimizing these losses. However increasingly sophisticated fraudsters have developed a variety of ways to elude discovery – both… Read more →

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Graph Databases in the Enterprise: Fraud Detection

Banks and insurance companies lose billions of dollars every year to fraud. Traditional methods of fraud detection fail to minimize these losses since they perform discrete analyses that are susceptible to false positives (and false negatives). Knowing this, increasingly sophisticated… Read more →

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