A computer-implemented method for fraud detection comprising: receiving a request to perform a fraud analysis on an entity involved in a business transaction; generating a network graph to represent the business transaction, wherein the entity is represented as a primary node and one or more other entities associated with the entity in the business transaction are represented as secondary nodes in the network graph, and wherein relationships between the secondary nodes, and between the secondary nodes and the primary node are represented as edges in the network graph; detecting one or more cycles within the generated network graph, wherein a cycle is a path, formed by a sequence of edges, originating from the primary node and terminating at the primary node without traversing the secondary nodes in the path more than once; and highlighting the detected one or more cycles to indicate a potential fraud associated with the entity; wherein the one or more cycles are detected by determining one or more back nodes and wherein a back node is a node in a cycle with a maximum edge distance from the primary node; and wherein the one or more back nodes are determined by a breadth first search algorithm, the breadth first search algorithm including steps of - starting from the primary node, visiting nodes having depth one, - storing the nodes having depth one into a breadth-first queue and marking the nodes having depth one as visited, - traversing further, wherein, while traversing, if a node is visited for the first time, storing the node visited for the first time into the breadth-first queue and marking the node visited for the first time as visited, and if a node already marked as visited is visited again, determining the node visited again as a back node. receiving a request to perform a fraud analysis on an entity involved in a business transaction; generating a network graph to represent the business transaction, wherein the entity is represented as a primary node and one or more other entities associated with the entity in the business transaction are represented as secondary nodes in the network graph, and wherein relationships between the secondary nodes, and between the secondary nodes and the primary node are represented as edges in the network graph; detecting one or more cycles within the generated network graph, highlighting the detected one or more cycles to indicate a potential fraud associated with the entity; wherein the one or more cycles are detected by determining one or more back nodes and wherein a back node is a node in a cycle with a maximum edge distance from the primary node; and wherein the one or more back nodes are determined by a breadth first search algorithm, the breadth first search algorithm including steps of - starting from the primary node, visiting nodes having depth one, - storing the nodes having depth one into a breadth-first queue and marking the nodes having depth one as visited, - traversing further, wherein, while traversing, if a node is visited for the first time, storing the node visited for the first time into the breadth-first queue and marking the node visited for the first time as visited, and if a node already marked as visited is visited again, determining the node visited again as a back node. - starting from the primary node, visiting nodes having depth one, - storing the nodes having depth one into a breadth-first queue and marking the nodes having depth one as visited, - traversing further, wherein, while traversing, if a node is visited for the first time, storing the node visited for the first time into the breadth-first queue and marking the node visited for the first time as visited, and if a node already marked as visited is visited again, determining the node visited again as a back node. if a node is visited for the first time, storing the node visited for the first time into the breadth-first queue and marking the node visited for the first time as visited, and if a node already marked as visited is visited again, determining the node visited again as a back node.