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| Table of Contents |
|---|
Overall process
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Deduplication is the task of finding records in finding records in a data set (MPI) that refer to the same Person. In order to guarantee data quality also should be make task of fraud defining. In case there are different persons with same document number/tax_id etc such cases won't be duplicated and it's a scope of fraud defining task.
In order to deduplicate persons next steps must be done:
data cleaning and preparation
blocking and finding possible pairs
calculating of variables for each pair
probability linkage using model
finding master persons in a pair
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and deactivation of person(s) which are not masters
fetch declaration(s) which belongs to deactivated person(s) and declaration termination
Data cleaning and preparation
For fields which are used for blocking or calculating variables next regular expressions must be applied:
for first_name, second_name and last_name, birth_settlement change: [ --'] to '', 'є' to 'е', 'и' to 'і'
for birth_certificate change: [ /%#№ _-] to '', [iі!IІ] to 1
for other documents: [ /%#№ _-] to ''
for birth_settlement - ([сc][ \.,])|([сc]ело[\.,]*)|([сc]мт[\.,]*)|([сc]елище [мm][іi][сc]ького типу)|([сc]елище[\.,]*)|([мm][іi][сc][tт][оo][\.,]*)|([мm][\.,]*)
Blocking and finding possible pairs
Duplicate records almost always share something in common. If we define groups of data that share something and only compare the records in that group, or block, then we can reduce the number of comparisons we will make. In other words it's we should apply smart comparison.
Predicate blocks
We need to define in such way to have much less pairs to compare and still and still have confidence that will compare records that truly are duplicates.
Current blocks is matching either of
tax_id
documents.number
authentication number
residence settlement + first_name
...
residence settlement + last_name
Building index for blocks
In order to make process of pairing more efficient
build index for each field that take part in blocks
add boolean flag field checked and build index on it
one by one take persons with flag "checked" is null and take ids of persons with same characteristic (tax_id, document and all block fields)
Variables calculation calculation for each pair
After building model on train dataset there is a knowledge which variables are correlated with target and have high IV.
There is no linear dependency on variable and target. For example, if name has difference in 0 symbols the probability that this is same persons is high. In case the difference is 1 symbols the probability decreases, 2 symbols 2 symbols - dramatically decreases, there is no linear dependency. So, we should to bin continuous variables in categorical, base on hit rate. Such approach also helps to define linear, non-linear dependency, handle missing values and predict power of missing values.
Variables that included into model are next:
Variable | Descirption |
|---|---|
d_first_name | levenshtein distance(first_name1, first_name2) |
d_last_name | levenshtein distance(last_name1, last_name2) |
d_second_name | levenshtein distance(second_name1, second_name2) |
d_documents | min(levenshtein distance(document1, document2)) for any types of documents |
docs_same_number | min(same/not) number |
birth_settlement_substr | min(position(birth_settlement_1 in birth_settlementt_2) and position(birth_settlement_2 in birth_settlementt_1) |
d_tax_id | levenshtein distance(tax_id1, tax_id2) |
authentication_methods | same/not authentification OTP number flag |
residence_settlement_flag | same/not residence settlement flag |
gender_flag | same/not gender |
twins_flag | distance last_name <=2, same birth_date, distance in document numbers between 1 and 2 |
Each categorical categorical variable must be convert to continuous using WOE (which was calculated for train data sample). WOE describes the relationship between a predictive variable and a binary target variable.
The WOE/IV framework is based on the following relationship:
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logP(Y=1|
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Xj)P(Y=0|
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Xj) =
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logP(Y=1)P(Y=0) (sample log-odds) +log(
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Xj|Y=1)f(
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Xj|Y=0) (WOE)
WOE = ln (% of non-merge/
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% of merge)
After decoding each variable we can apply model and calculate probability
Probability linkage using model
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Logistic regression itself is a function:
where 
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After that the threshold should be used to define which probability if satisfying to call the pair a duplicate.
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Field | Description |
|---|---|
Score | Probability that pair of persons should be merged |
Sum of target | Quantity of pairs which should be merged |
Qty | Total quantity of pairs |
Hit_rate | Ratio of 'Quantity of pairs which should be merged' to 'Total quantity of pairs' |
Merge_acu_% | Accumulated ratio of all records which marked as merge |
Qty_acu_% | Accumulated sample distribution by score |
Accuracy_rate | Percantage of errors on data sample |
From test sample suggestions for cut off are next:
score >=0.9 - merge (as minimum score that should be saved to `
merge candidates` and auto merged)score between 0.7 and 0.9 - manual merge (as minimum score that should be merged manually)
score < 0.7 - do not merge
Define master record and terminate declaration
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Make a record into merge_candidates:
column | value | description |
|---|---|---|
id | UUID | record unique ID |
person_id | UUID | the person which will be merged |
master_person_id | UUID | the person who will stay active |
status | NEW, MANUAL, MERGED | |
inserted_at | DATETIME = now() | |
updated_at | DATETIME = now() | |
config | {"person_id" |
"candidate_id" |
variables} | ||
details | ||
score | value from 0 to 1 |
Manual merge overview
For person_id in status 'NEW' and score>=max_manul_score (0.9) from merge_candidates find declaration in status 'VERIFIED' and change status to 'TERMINATED' and change persons.id.status to INACTIVE
OPS kafka consumer should be used for the declaration termination. Declaration termination and person deactivation should be done at the same time.
Change merge_candidates.status from `NEW` to `MERGED`
Fetch record in status 'NEW' and (0.8) min_manul_score < score < max_manul_score (0.9) from merge_candidates and write them into table manual_merge_candidates:
column | value | description |
|---|---|---|
id | UUID | record unique ID |
merge_candidate_id | UUID | pair identifier |
person_id | UUID | the person which will be merged |
master_person_id | UUID | the person who will stay active |
status | NEW | NEW, PROCESSED |
status_reason | null, text | |
assignee | UUID | user, who currently reviews request, for new request it's null |
inserted_at | DATETIME = now() | |
updated_at | DATETIME = now() |
decision | null, text | null, SPLIT, MERGE, POSTPONE |
After that change merge_candidates.status from `NEW` to `MANUAL`
After decision reach more than decision_amount and final_decision is MERGE OPS kafka consumer should find declaration in status 'VERIFIED' and change status to 'TERMINATED'
and change persons.id.status to INACTIVE and
Deactivate user:
search user by person_id, if exists (Mithril.users DB)
set is_active = false
set updated_at = now()
call expire_user_tokens function
Auto merge overview
After there was a decision to merge pair of persons do
Validate persons
check if person or master_person exists in DB
check persons `updated_at`, if (merge_candidate.person.inserted_at or merge_candidate.
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measter_person.inserted_at) < mpi.person.updated_at set status STALE to merge_candidates
Deactivate person
search declaration for person_id, if exist
create event to kafka
set status DECLARATION_READY_DEACTIVATE to merge_candidates
change person_id status to inactive
add info to merged_pairs with person_id and master_person_id
set status MERGED to merge_candidates
add person status change info to event_manager
Deactivate user
search user by person_id, if exists (Mithril.users DB)
set user.is_active = false
set updated_at = now()
call expire_user_tokens function
Merge_candidates state diagram
Status | Description |
|---|---|
NEW | New pair of merge candidates |
MERGED | Pair that was merged after either manual merge process or auto merge process |
STALE | Pair is not merged as one of person records has been updated after the de-duplication score has been calculated. |
DECLARATION_READY_DEACTIVATE | The event is sent to kafka to deactivate declaration of the person |
DECLINED | Pair is not merged. Not because of person, but because of related to persons entities. For example, declarations. |
IN_PROCESS | The event is sent to kafka to deactivate the person, which is not master person |