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Resolve routing-number to bank mapping

Company: Plaid

Role: Software Engineer

Category: Coding & Algorithms

Difficulty: easy

Interview Round: Technical Screen

You are given bank-routing data coming from one or more JSON sources. Each source provides a list of records like: ```json { "routing": "026009593", "bank_name": "Bank of America" } ``` Complications: - The same bank may appear under multiple names (e.g., "Bank of America", "BoA", "BofA"). - The same routing number may appear multiple times (even within a source). - Across multiple sources, the same routing number may map to different banks (conflicts). Task: 1) Normalize bank names using a provided alias mapping (e.g., a dictionary that maps each observed name to a canonical bank id/name). 2) Produce a final mapping `routing -> canonical_bank`. 3) If multiple sources disagree for the same routing number, resolve it using this policy: - Choose the canonical bank that appears in the most sources for that routing number (majority vote across sources). - If there is a tie, break ties by a given source-priority order (e.g., SourceA > SourceB > SourceC). 4) Also output a list of routings that had conflicts (i.e., more than one canonical bank was claimed across sources), along with the votes that led to the decision. Write a function that takes: - `sources: List[List[Record]]` (each inner list is one source’s records) - `alias_to_canonical: Map[str, str]` - `source_priority: List[int]` (or equivalent) and returns the resolved mapping and the conflict report.

Quick Answer: This question evaluates a candidate's skills in data normalization, deduplication, aggregation, and conflict resolution across multiple JSON data sources, with emphasis on canonicalization of aliases and handling inconsistent mappings.

Part 1: Normalize Routing-Number Bank Mappings

You are given raw bank mapping records loaded from JSON. Each record contains a routing number and a bank name. You are also given an alias dictionary where one canonical bank can have several alternative names. Normalize bank names case-insensitively, trim extra spaces, and build a final routing-number-to-bank mapping. If the same routing number appears multiple times and all names resolve to the same canonical bank, keep that bank. If a routing number resolves to different canonical banks, store 'CONFLICT' for that routing number. If a bank name does not appear in the alias dictionary, use its normalized lowercase form as its own canonical name.

Constraints

0 <= len(records) <= 100000
0 <= total number of alias strings across all banks <= 100000
Each record contains keys 'routing_number' and 'bank_name'
Routing numbers should be treated as strings in the output
After normalization, alias groups do not overlap

Examples

Input: ([{'routing_number': '111000025', 'bank_name': ' Bank of America '}, {'routing_number': '111000025', 'bank_name': 'BOFA'}, {'routing_number': '021000021', 'bank_name': ' chase '}, {'routing_number': '021000021', 'bank_name': 'JPMORGAN CHASE'}], {'Bank of America': ['BOFA', 'B.O.A.', 'Bank of america'], 'Chase': ['JPMorgan Chase', 'JP Morgan Chase']})

Expected Output: {'111000025': 'Bank of America', '021000021': 'Chase'}

Explanation: Both records for 111000025 resolve to Bank of America, and both records for 021000021 resolve to Chase after case-insensitive normalization and alias matching.

Input: ([{'routing_number': '111', 'bank_name': 'alpha bank'}, {'routing_number': '111', 'bank_name': 'Beta Bank'}, {'routing_number': '111', 'bank_name': ' ALPHA BANK '}], {'Alpha Bank': ['Alpha bank'], 'Beta Bank': ['beta bank']})

Expected Output: {'111': 'CONFLICT'}

Explanation: The same routing number resolves to Alpha Bank and Beta Bank, so the final value must be CONFLICT. Once conflicted, it stays conflicted.

Input: ([{'routing_number': '333', 'bank_name': ' First National Bank '}, {'routing_number': '333', 'bank_name': 'first national bank'}], {})

Expected Output: {'333': 'first national bank'}

Explanation: This bank does not appear in the alias dictionary, so its normalized lowercase form becomes its canonical name.

Input: ([], {'Bank of America': ['BOFA']})

Expected Output: {}

Explanation: With no records, the result is an empty mapping.

Input: ([{'routing_number': '555', 'bank_name': ' wElLs fArGo '}], {'Wells Fargo': ['Wells Fargo Bank']})

Expected Output: {'555': 'Wells Fargo'}

Explanation: A canonical name should also match directly after normalization, even if the raw record does not use one of the listed aliases.

Hints

Build one reverse hash map from every known alias to its canonical bank name before processing the records.
For each routing number, remember the first resolved bank. If a later resolved bank is different, mark that routing number as a conflict.

Part 2: Resolve Routing Numbers Across Multiple Data Sources

You are given several data sources that each map routing numbers to bank names. Different sources may disagree, and the same source may even contain inconsistent records for the same routing number. You are also given a bank alias dictionary. Resolve each routing number using weighted voting across sources. First normalize bank names using the alias dictionary, trimming spaces and ignoring case. Inside a single source, if a routing number appears multiple times with equivalent names, that source casts one vote for that bank. If a single source contains conflicting banks for the same routing number, that source casts no vote for that routing number. Across sources, add up the source weights for each candidate bank. Return the bank with the highest total weight. If there is a tie for highest weight, or if no valid source votes remain for a routing number that appeared in the input, return 'AMBIGUOUS'.

Constraints

0 <= len(sources) <= 10000
The total number of records across all sources is at most 100000
Each source contains keys 'source', 'weight', and 'records'
Each weight is a positive integer
After normalization, alias groups do not overlap

Examples

Input: ([[('111', ' Bank of America '), ('111', 'boa')], [('111', 'Bank of America')]], [2, 1], {'boa': 'Bank of America'})

Expected Output: {'111': 'Bank of America'}

Explanation: In the first source, both names normalize to Bank of America, so that source casts one vote worth 2. The second source adds another vote worth 1. Bank of America wins with total weight 3.

Input: ([[('222', 'Chase'), ('222', ' JP Morgan Chase ')], [('222', 'Wells Fargo')], [('222', ' chase ')]], [3, 4, 2], {'jp morgan chase': 'Chase'})

Expected Output: {'222': 'Chase'}

Explanation: Source 1 contributes 3 to Chase because both records are equivalent after alias normalization. Source 2 contributes 4 to Wells Fargo. Source 3 contributes 2 to Chase. Final totals: Chase 5, Wells Fargo 4.

Input: ([[('333', 'Citi')], [('333', 'Wells Fargo')]], [3, 3], {})

Expected Output: {'333': 'AMBIGUOUS'}

Explanation: Citi and Wells Fargo both receive total weight 3, so the routing number is ambiguous.

Input: ([[('444', 'Alpha Bank'), ('444', 'Beta Bank')], [('444', ' alpha bank '), ('444', 'Beta Bank')]], [5, 1], {})

Expected Output: {'444': 'AMBIGUOUS'}

Explanation: Each source has conflicting normalized banks for routing 444, so neither source casts a valid vote. Since the routing number appeared but no valid votes remain, the result is AMBIGUOUS.

Input: ([[('001', ' First Bank '), ('002', 'Second Bank'), ('002', 'second bank')], [], [('001', 'first bank'), ('003', 'Third Bank'), ('003', 'Third Bank')]], [1, 5, 2], {})

Expected Output: {'001': 'First Bank', '002': 'Second Bank', '003': 'Third Bank'}

Explanation: Routing 001 gets one vote from source 1 and one from source 3 for the same normalized bank. Routing 002 appears twice in source 1 with equivalent names, so that source casts one vote. Routing 003 appears twice in source 3 with the same bank, which still counts as one vote.

Input: ([], [], {})

Expected Output: {}

Explanation: No routing numbers appear in the input, so the result is an empty dictionary.

Hints

Resolve duplicates and contradictions inside each source before doing any cross-source voting.
Use a nested hash map like routing -> bank -> total score to accumulate weighted votes.

Resolve routing-number to bank mapping

Company: Plaid

Role: Software Engineer

Category: Coding & Algorithms

Difficulty: easy

Interview Round: Technical Screen

Part 1: Normalize Routing-Number Bank Mappings

Constraints

0 <= len(records) <= 100000
0 <= total number of alias strings across all banks <= 100000
Each record contains keys 'routing_number' and 'bank_name'
Routing numbers should be treated as strings in the output
After normalization, alias groups do not overlap

Examples

Expected Output: {'111000025': 'Bank of America', '021000021': 'Chase'}

Explanation: Both records for 111000025 resolve to Bank of America, and both records for 021000021 resolve to Chase after case-insensitive normalization and alias matching.

Expected Output: {'111': 'CONFLICT'}

Explanation: The same routing number resolves to Alpha Bank and Beta Bank, so the final value must be CONFLICT. Once conflicted, it stays conflicted.

Input: ([{'routing_number': '333', 'bank_name': ' First National Bank '}, {'routing_number': '333', 'bank_name': 'first national bank'}], {})

Expected Output: {'333': 'first national bank'}

Explanation: This bank does not appear in the alias dictionary, so its normalized lowercase form becomes its canonical name.

Input: ([], {'Bank of America': ['BOFA']})

Expected Output: {}

Explanation: With no records, the result is an empty mapping.

Input: ([{'routing_number': '555', 'bank_name': ' wElLs fArGo '}], {'Wells Fargo': ['Wells Fargo Bank']})

Expected Output: {'555': 'Wells Fargo'}

Explanation: A canonical name should also match directly after normalization, even if the raw record does not use one of the listed aliases.

Hints

Build one reverse hash map from every known alias to its canonical bank name before processing the records.
For each routing number, remember the first resolved bank. If a later resolved bank is different, mark that routing number as a conflict.

Part 2: Resolve Routing Numbers Across Multiple Data Sources

Constraints

0 <= len(sources) <= 10000
The total number of records across all sources is at most 100000
Each source contains keys 'source', 'weight', and 'records'
Each weight is a positive integer
After normalization, alias groups do not overlap

Examples

Input: ([[('111', ' Bank of America '), ('111', 'boa')], [('111', 'Bank of America')]], [2, 1], {'boa': 'Bank of America'})

Expected Output: {'111': 'Bank of America'}

Input: ([[('222', 'Chase'), ('222', ' JP Morgan Chase ')], [('222', 'Wells Fargo')], [('222', ' chase ')]], [3, 4, 2], {'jp morgan chase': 'Chase'})

Expected Output: {'222': 'Chase'}

Input: ([[('333', 'Citi')], [('333', 'Wells Fargo')]], [3, 3], {})

Expected Output: {'333': 'AMBIGUOUS'}

Explanation: Citi and Wells Fargo both receive total weight 3, so the routing number is ambiguous.

Input: ([[('444', 'Alpha Bank'), ('444', 'Beta Bank')], [('444', ' alpha bank '), ('444', 'Beta Bank')]], [5, 1], {})

Expected Output: {'444': 'AMBIGUOUS'}

Explanation: Each source has conflicting normalized banks for routing 444, so neither source casts a valid vote. Since the routing number appeared but no valid votes remain, the result is AMBIGUOUS.

Input: ([[('001', ' First Bank '), ('002', 'Second Bank'), ('002', 'second bank')], [], [('001', 'first bank'), ('003', 'Third Bank'), ('003', 'Third Bank')]], [1, 5, 2], {})

Expected Output: {'001': 'First Bank', '002': 'Second Bank', '003': 'Third Bank'}

Input: ([], [], {})

Expected Output: {}

Explanation: No routing numbers appear in the input, so the result is an empty dictionary.

Hints

Resolve duplicates and contradictions inside each source before doing any cross-source voting.
Use a nested hash map like routing -> bank -> total score to accumulate weighted votes.

Quick Overview

Quick Overview