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Fraudulent Transaction Detection - Flipkart Grid 8.0 Interview Question | Codinzhub

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Fraudulent Transaction Detection

The bank wants to identify successful transactions that may be fraudulent. The bank stores account formation data, merchant profiles, device activities, and location risks. The fraud risk must be calculated using account transaction behavior. Given a list of transactions, a list of merchants, a list of device activities, and a list of location risks, determine the risk level of each transaction.

Example 1:

Input:merchants = [[1, 'low', 1], [2, 'medium', 2], [3, 'high', 3]], device_activities = [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3]], location_risks = [[1, 1], [2, 2], [3, 3]], transactions = [[1, 1, 1, 1, 1], [2, 2, 2, 2, 2], [3, 3, 3, 3, 3]]

Output:[1, 2, 3]

Example 2:

Input:merchants = [[1, 'low', 1], [2, 'medium', 2], [3, 'high', 3]], device_activities = [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3]], location_risks = [[1, 1], [2, 2], [3, 3]], transactions = [[1, 1, 1, 1, 2], [2, 2, 2, 2, 2], [3, 3, 3, 3, 3]]

Output:[1, 2, 3]

Example 3:

Input:merchants = [[1, 'low', 1], [2, 'medium', 2], [3, 'high', 3]], device_activities = [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3]], location_risks = [[1, 1], [2, 2], [3, 3]], transactions = [[1, 1, 1, 1, 3], [2, 2, 2, 2, 2], [3, 3, 3, 3, 3]]

Output:[1, 2, 3]

Constraints:

1 <= N <= 10^5
1 <= M <= 10^5
1 <= D <= 10^5
1 <= L <= 10^5
1 <= T <= 10^5

solution.cpp

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Fraudulent Transaction Detection

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The bank wants to identify successful transactions that may be fraudulent. The bank stores account formation data, merchant profiles, device activities, and location risks. The fraud risk must be calculated using account transaction behavior. Given a list of transactions, a list of merchants, a list of device activities, and a list of location risks, determine the risk level of each transaction.

Example 1:

Input:merchants = [[1, 'low', 1], [2, 'medium', 2], [3, 'high', 3]], device_activities = [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3]], location_risks = [[1, 1], [2, 2], [3, 3]], transactions = [[1, 1, 1, 1, 1], [2, 2, 2, 2, 2], [3, 3, 3, 3, 3]]

Output:[1, 2, 3]

Example 2:

Input:merchants = [[1, 'low', 1], [2, 'medium', 2], [3, 'high', 3]], device_activities = [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3]], location_risks = [[1, 1], [2, 2], [3, 3]], transactions = [[1, 1, 1, 1, 2], [2, 2, 2, 2, 2], [3, 3, 3, 3, 3]]