Fuzzy match based on names + DOB
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I'm trying to develop an approach to match tenant applicant data to public records. We require name plus one of the following: DOB, SSN, Address, DL.
We can match fine with name + SSN, address history, DL; however, we are struggling with name + DOB. For example, we may have a lot of Joseph Smiths born on 5/6/1972 in the public record data, and when we get an applicant named Joseph Smith DOB 5/6/1972, we need to make sure we're matching him to the correct public record data.
I'm trying to develop a way to reduce the number of cases that go to manual review when we have that common name + DOB match. I'm looking to develop a confidence score for cases (and eventually, set a confidence threshold to send cases to manual review or confirm that we have a match automatically).
Any advice? Ideas? (I was thinking some sort of confidence based on the popularity of the name by year... but I'm not really sure where I'm going with that... ) Where to start?
fuzzy-logic
asked 4 mins ago
aks85aks85
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$begingroup$
I'm trying to develop an approach to match tenant applicant data to public records. We require name plus one of the following: DOB, SSN, Address, DL.
We can match fine with name + SSN, address history, DL; however, we are struggling with name + DOB. For example, we may have a lot of Joseph Smiths born on 5/6/1972 in the public record data, and when we get an applicant named Joseph Smith DOB 5/6/1972, we need to make sure we're matching him to the correct public record data.
I'm trying to develop a way to reduce the number of cases that go to manual review when we have that common name + DOB match. I'm looking to develop a confidence score for cases (and eventually, set a confidence threshold to send cases to manual review or confirm that we have a match automatically).
Any advice? Ideas? (I was thinking some sort of confidence based on the popularity of the name by year... but I'm not really sure where I'm going with that... ) Where to start?
fuzzy-logic
asked 4 mins ago
aks85aks85
1
New contributor
aks85 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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add a comment |
$begingroup$
I'm trying to develop an approach to match tenant applicant data to public records. We require name plus one of the following: DOB, SSN, Address, DL.
We can match fine with name + SSN, address history, DL; however, we are struggling with name + DOB. For example, we may have a lot of Joseph Smiths born on 5/6/1972 in the public record data, and when we get an applicant named Joseph Smith DOB 5/6/1972, we need to make sure we're matching him to the correct public record data.
I'm trying to develop a way to reduce the number of cases that go to manual review when we have that common name + DOB match. I'm looking to develop a confidence score for cases (and eventually, set a confidence threshold to send cases to manual review or confirm that we have a match automatically).
Any advice? Ideas? (I was thinking some sort of confidence based on the popularity of the name by year... but I'm not really sure where I'm going with that... ) Where to start?
fuzzy-logic
asked 4 mins ago
aks85aks85
1
New contributor
aks85 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I'm trying to develop an approach to match tenant applicant data to public records. We require name plus one of the following: DOB, SSN, Address, DL.
We can match fine with name + SSN, address history, DL; however, we are struggling with name + DOB. For example, we may have a lot of Joseph Smiths born on 5/6/1972 in the public record data, and when we get an applicant named Joseph Smith DOB 5/6/1972, we need to make sure we're matching him to the correct public record data.
I'm trying to develop a way to reduce the number of cases that go to manual review when we have that common name + DOB match. I'm looking to develop a confidence score for cases (and eventually, set a confidence threshold to send cases to manual review or confirm that we have a match automatically).
Any advice? Ideas? (I was thinking some sort of confidence based on the popularity of the name by year... but I'm not really sure where I'm going with that... ) Where to start?
fuzzy-logic
fuzzy-logic
asked 4 mins ago
aks85aks85
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aks85 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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asked 4 mins ago
aks85aks85
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aks85 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 4 mins ago
aks85aks85
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aks85 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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asked 4 mins ago
aks85aks85
1
asked 4 mins ago
aks85aks85
1
1
New contributor
aks85 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
aks85 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
aks85 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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