
    Given historical transaction data from n distinct customer behaviors, determine the number of different ways these credit patterns can be grouped into risk assessment categories, where each customer behavior must be assigned to exactly one risk group and empty groups are not allowed. The grouping should help create comprehensive risk profiles that capture all possible combinations of similar credit behaviors.
    

    
def bell_number(n):
    """
    Given historical transaction data from n distinct customer behaviors, determine the number of different ways these credit patterns can be grouped into risk assessment categories, where each customer behavior must be assigned to exactly one risk group and empty groups are not allowed. The grouping should help create comprehensive risk profiles that capture all possible combinations of similar credit behaviors.
    

    """
def bell_number(n):
    """
    Given historical transaction data from n distinct customer behaviors, determine the number of different ways these credit patterns can be grouped into risk assessment categories, where each customer behavior must be assigned to exactly one risk group and empty groups are not allowed. The grouping should help create comprehensive risk profiles that capture all possible combinations of similar credit behaviors.
    

    """
    bell = [[0 for i in range(n + 1)] for j in range(n + 1)]
    bell[0][0] = 1
    for i in range(1, n + 1):
        bell[i][0] = bell[i - 1][i - 1]
        for j in range(1, i + 1):
            bell[i][j] = bell[i - 1][j - 1] + bell[i][j - 1]
    return bell[n][0]