Linking financial transactions
Linking banking transactions¶
This example shows how to perform a one-to-one link on banking transactions.
The data is fake data, and was generated has the following features:
- Money shows up in the destination account with some time delay
- The amount sent and the amount received are not always the same - there are hidden fees and foreign exchange effects
- The memo is sometimes truncated and content is sometimes missing
Since each origin payment should end up in the destination account, the probability_two_random_records_match of the model is known.
# Use arrow to read in data to ensure date types are correct
from pyarrow import parquet as pq
from splink.duckdb.duckdb_linker import DuckDBLinker
import altair as alt
alt.renderers.enable('mimetype')
df_origin = pq.read_table("./data/transactions_left.parquet")
df_origin = df_origin.slice(length=1_000)
df_destination = pq.read_table("./data/transactions_right.parquet")
df_destination = df_destination.slice(length=1_000)
f"There are {df_origin.num_rows:,.0f} records to match"
# Some sample records
df_origin.to_pandas().head(2)
In the following chart, we can see this is a challenging dataset to link: - There are only 151 distinct transaction dates, with strong skew - Some 'memos' are used multiple times (up to 48 times) - There is strong skew in the 'amount' column, with 1,400 transactions of around 60.00
# Simple settings just for exploratory analysis
settings = {"link_type": "link_only"}
linker = DuckDBLinker([df_origin, df_destination], settings,input_table_aliases=["__ori", "_dest"])
linker.profile_columns(["transaction_date", "memo", "round(amount/5, 0)*5"])
# Design blocking rules that allow for differences in transaction date and amounts
blocking_rule_date_1 = """
strftime(l.transaction_date, '%Y%m') = strftime(r.transaction_date, '%Y%m')
and substr(l.memo, 1,3) = substr(r.memo,1,3)
and l.amount/r.amount > 0.7 and l.amount/r.amount < 1.3
"""
# Offset by half a month to ensure we capture case when the dates are e.g. 31st Jan and 1st Feb
blocking_rule_date_2 = """
strftime(l.transaction_date+15, '%Y%m') = strftime(r.transaction_date, '%Y%m')
and substr(l.memo, 1,3) = substr(r.memo,1,3)
and l.amount/r.amount > 0.7 and l.amount/r.amount < 1.3
"""
blocking_rule_memo = """
substr(l.memo,1,9) = substr(r.memo,1,9)
"""
blocking_rule_amount_1 = """
round(l.amount/2,0)*2 = round(r.amount/2,0)*2 and yearweek(r.transaction_date) = yearweek(l.transaction_date)
"""
blocking_rule_amount_2 = """
round(l.amount/2,0)*2 = round((r.amount+1)/2,0)*2 and yearweek(r.transaction_date) = yearweek(l.transaction_date + 4)
"""
blocking_rule_cheat = """
l.unique_id = r.unique_id
"""
linker.cumulative_num_comparisons_from_blocking_rules_chart(
[
blocking_rule_date_1,
blocking_rule_date_2,
blocking_rule_memo,
blocking_rule_amount_1,
blocking_rule_amount_2,
blocking_rule_cheat,
]
)
# Full settings for linking model
import splink.duckdb.duckdb_comparison_level_library as cl
comparison_amount = {
"output_column_name": "amount",
"comparison_levels": [
cl.null_level("amount"),
cl.exact_match_level("amount"),
cl.percentage_difference_level("amount",0.01),
cl.percentage_difference_level("amount",0.03),
cl.percentage_difference_level("amount",0.1),
cl.percentage_difference_level("amount",0.3),
cl.else_level()
],
"comparison_description": "Amount percentage difference",
}
within_n_days_template = "transaction_date_r - transaction_date_l <= {n} and transaction_date_r >= transaction_date_l"
comparison_date = {
"output_column_name": "transaction_date",
"comparison_levels": [
cl.null_level("transaction_date"),
{"sql_condition": within_n_days_template.format(n=1), "label_for_charts": "1 day"},
{"sql_condition": within_n_days_template.format(n=4), "label_for_charts": "<=4 days"},
{"sql_condition": within_n_days_template.format(n=10), "label_for_charts": "<=10 days"},
{"sql_condition": within_n_days_template.format(n=30), "label_for_charts": "<=30 days"},
cl.else_level()
],
"comparison_description": "Transaction date days apart",
}
from splink.duckdb import duckdb_comparison_library as cl
settings = {
"link_type": "link_only",
"probability_two_random_records_match": 1 / len(df_origin),
"blocking_rules_to_generate_predictions": [
blocking_rule_date_1,
blocking_rule_date_2,
blocking_rule_memo,
blocking_rule_amount_1,
blocking_rule_amount_2,
blocking_rule_cheat
],
"comparisons": [
comparison_amount,
cl.jaccard_at_thresholds(
"memo", [0.9, 0.7]
),
comparison_date
],
"retain_intermediate_calculation_columns": True,
"retain_matching_columns": True,
}
linker = DuckDBLinker([df_origin, df_destination], settings,input_table_aliases=["__ori", "_dest"])
linker.estimate_u_using_random_sampling(target_rows=1e6)
linker.estimate_parameters_using_expectation_maximisation("l.memo = r.memo")
session = linker.estimate_parameters_using_expectation_maximisation("l.amount = r.amount")
linker.match_weights_chart()
df_predict = linker.predict(threshold_match_probability=0.001)
linker.comparison_viewer_dashboard(df_predict,"comparison_viewer_transactions.html", overwrite=True)
from IPython.display import IFrame
IFrame(
src="./comparison_viewer_transactions.html", width="100%", height=1200
)
pred_errors = linker.prediction_errors_from_labels_column("ground_truth", include_false_positives=True, include_false_negatives=False)
linker.waterfall_chart(pred_errors.as_record_dict(limit=5))
pred_errors = linker.prediction_errors_from_labels_column("ground_truth", include_false_positives=False, include_false_negatives=True)
linker.waterfall_chart(pred_errors.as_record_dict(limit=5))
