Strict Identity Matching

Address:

• Standardize common street type abbreviations (e.g., "Ave" to "Avenue", "St" to "Street").

Scoring Algorithm (Binary Policy)

Calculate the match score based on the following logic. Assign only binary scores (0 or 1). No partial scores.

1. Social Security Number (SSN):

   • Compare the normalized 9-digit strings.
   • If they match exactly, score 1. Otherwise, score 0.

2. Name:

   • Be agnostic to prefixes (Mr, Dr) and suffixes (Jr, III).
   • First Name: Score 1 for an exact match. Handle common nicknames (e.g., Bob vs Robert) and initials (e.g., J vs Joe) as a match (1).
   • Last Name: Score 1 for an exact match.
   • Middle Name: Score 1 if the first letter matches (disregarding "."), else score 0. If one is missing, assess based on context (missing middle name often implies a match).

3. Date of Birth (DOB):

   • Compare the normalized YYYYMMDD strings.
   • If they match exactly, score 1. Otherwise, score 0.

4. Address:

   • Street Name: Score 1 for a match (accounting for abbreviations), else 0.
   • City: Score 1 for a match, else 0.
   • State Name: Score 1 for a match, else 0.
   • ZIP Code: Score 1 for a match, else 0.

Conclusion Logic

• If SSN is 1 AND (First Name is 1 OR Last Name is 1) AND DOB is 1, conclude "Same person".
• If SSN is 0, conclude "Different person" (unless SSN is missing, in which case rely on Name + DOB + Address).
• If critical fields (Name + DOB) mismatch, conclude "Different person".

Output Format

Provide the result strictly as a JSON object. Include the breakdown of binary scores and the final conclusion.

{ "ssn": <int_0_or_1>, "name": { "first_name": <int_0_or_1>, "middle_name": <int_0_or_1>, "last_name": <int_0_or_1> }, "dob": <int_0_or_1>, "address": { "street_name": <int_0_or_1>, "city": <int_0_or_1>, "state_name": <int_0_or_1>, "zip_code": <int_0_or_1> }, "conclusion": "<Same person | Different person>" }

Anti-Patterns & Constraints

• Do not assume or infer information not present in the input data.
• Do not include any text outside the JSON object.
• Do not assign partial scores (e.g., 0.5); all scores must be 0 or 1.
• Do not hallucinate matches for missing critical fields.

Triggers

• compare two people
• identity verification
• check if these records match
• calculate identity match score
• are these the same person

Examples

Example 1

Input:

Person A: {"name": {"first": "John", "last": "Doe"}, "ssn": "123-45-6789", "dob": "01/01/1990"} Person B: {"name": {"first": "Jon", "last": "Doe"}, "ssn": "123456789", "dob": "1990-01-01"}

Output:

SSN: 40% (Match), Name: ~28% (High partial match), DOB: 15% (Match). Total: >90%. Conclusion: Same person.