
    In a bank's automated loan approval system, determine the number of different ways to arrange n risk assessment criteria where exactly m criteria receive a higher risk score than their subsequent criteria, to establish optimal credit risk thresholds. The result will help determine the statistical likelihood of different risk assessment patterns and inform the bank's loan approval decision boundaries.
    

    
def eulerian_num(n, m):
    """
    In a bank's automated loan approval system, determine the number of different ways to arrange n risk assessment criteria where exactly m criteria receive a higher risk score than their subsequent criteria, to establish optimal credit risk thresholds. The result will help determine the statistical likelihood of different risk assessment patterns and inform the bank's loan approval decision boundaries.
    

    """
def eulerian_num(n, m):
    """
    In a bank's automated loan approval system, determine the number of different ways to arrange n risk assessment criteria where exactly m criteria receive a higher risk score than their subsequent criteria, to establish optimal credit risk thresholds. The result will help determine the statistical likelihood of different risk assessment patterns and inform the bank's loan approval decision boundaries.
    

    """
    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)