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Most of the projects in data science or data analysis deal with individual data points. Even if the data points are extracted out of relational databases, while doing any kind of analysis work, it is necessary to merge multiple tables (if the data is tabular) to get consistent data structure. But in a lot of real world use cases, it might be important to analyze the relationship between data points rather than individual data. Usual databases are not adept to handle such relationship. Thus the usage of graph databases like Neo4j.

In the financial and banking sector, detecting fraud accounts or anomalous behavior (Transactions) is of utmost importance. Traditional approaches of fraud detection usually rely on analyzing individual account data or individual transactions and predict fraudulence from that. In recent days, fraudsters have adopted different methods to prevent detection like creating multiple false accounts and identities and creating fraud rings. By simply analyzing individual data points, detecting such fraud rings becomes very difficult. On the other hand, a graph database basically contains data in the form of nodes and relationships- thus allow to retain different connection information. It becomes much easier using a graph-theoretic approach to find patterns in fraudulent activity. Fraud rings are contained within a limited number of users when visualized graphically. This kind of pattern is easily discernible from a graphical representation of the data that neo4j allows.

Apart from Fraud detection, Neo4j can be utilized in other use cases involving a connected data. A lot of the real-world problems like Recommendation of products or services, Network analysis, Knowledge graph analysis, Identity, and access management, etc., and many more can be thought and approached graphically since they all involve connected data. Neo4j provides not only a graph database, but a complete ecosystem to work with graph data, visualization, and analysis.