
    Write a function that determines how many loan applicants have matching credit risk scores across their historical records, current assessments, and industry benchmarks at the same index positions. The function should analyze three credit score lists - historical scores, current assessment scores, and industry benchmarks - and return the count of perfect matches where all three scores are identical at the same position. The matching positions represent cases where risk assessment patterns align perfectly across all three evaluation methods.
    

    
def count_samepair(list1, list2, list3):
    """
    Write a function that determines how many loan applicants have matching credit risk scores across their historical records, current assessments, and industry benchmarks at the same index positions. The function should analyze three credit score lists - historical scores, current assessment scores, and industry benchmarks - and return the count of perfect matches where all three scores are identical at the same position. The matching positions represent cases where risk assessment patterns align perfectly across all three evaluation methods.
    

    """
def count_samepair(list1, list2, list3):
    """
    Write a function that determines how many loan applicants have matching credit risk scores across their historical records, current assessments, and industry benchmarks at the same index positions. The function should analyze three credit score lists - historical scores, current assessment scores, and industry benchmarks - and return the count of perfect matches where all three scores are identical at the same position. The matching positions represent cases where risk assessment patterns align perfectly across all three evaluation methods.
    

    """
    result = sum((m == n == o for (m, n, o) in zip(list1, list2, list3)))
    return result