
    Design a fraud detection algorithm that calculates the number of possible ways n consecutive suspicious transactions can be arranged such that exactly m transactions show higher risk scores than their subsequent transactions in the sequence. This pattern analysis helps identify potential fraudulent transaction chains by examining the relative risk levels between adjacent transactions in real-time monitoring.
    

    
def eulerian_num(n, m):
    """
    Design a fraud detection algorithm that calculates the number of possible ways n consecutive suspicious transactions can be arranged such that exactly m transactions show higher risk scores than their subsequent transactions in the sequence. This pattern analysis helps identify potential fraudulent transaction chains by examining the relative risk levels between adjacent transactions in real-time monitoring.
    

    """
def eulerian_num(n, m):
    """
    Design a fraud detection algorithm that calculates the number of possible ways n consecutive suspicious transactions can be arranged such that exactly m transactions show higher risk scores than their subsequent transactions in the sequence. This pattern analysis helps identify potential fraudulent transaction chains by examining the relative risk levels between adjacent transactions in real-time monitoring.
    

    """
    if m >= n or n == 0:
        return 0
    if m == 0:
        return 1
    return (n - m) * eulerian_num(n - 1, m - 1) + (m + 1) * eulerian_num(n - 1, m)