# Linking financial transactions

## Linking banking transactionsÂ¶

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.

```
from splink.datasets import splink_datasets
from splink.duckdb.linker import DuckDBLinker
import altair as alt
df_origin = splink_datasets.transactions_origin
df_destination = splink_datasets.transactions_destination
display(df_origin.head(2))
display(df_destination.head(2))
```

ground_truth | memo | transaction_date | amount | unique_id | |
---|---|---|---|---|---|

0 | 0 | MATTHIAS C paym | 2022-03-28 | 36.36 | 0 |

1 | 1 | M CORVINUS dona | 2022-02-14 | 221.91 | 1 |

ground_truth | memo | transaction_date | amount | unique_id | |
---|---|---|---|---|---|

0 | 0 | MATTHIAS C payment BGC | 2022-03-29 | 36.36 | 0 |

1 | 1 | M CORVINUS BGC | 2022-02-16 | 221.91 | 1 |

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
from splink.duckdb.blocking_rule_library import block_on, and_
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 = block_on("substr(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 = block_on("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.comparison_library as cl
import splink.duckdb.comparison_level_library as cll
comparison_amount = {
"output_column_name": "amount",
"comparison_levels": [
cll.null_level("amount"),
cll.exact_match_level("amount"),
cll.percentage_difference_level("amount",0.01),
cll.percentage_difference_level("amount",0.03),
cll.percentage_difference_level("amount",0.1),
cll.percentage_difference_level("amount",0.3),
cll.else_level()
],
"comparison_description": "Amount percentage difference",
}
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]
),
cl.datediff_at_thresholds("transaction_date",
date_thresholds = [1, 4, 10, 30],
date_metrics = ["day", "day", "day", "day"],
include_exact_match_level=False
)
],
"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(max_pairs=1e6)
```

```
----- Estimating u probabilities using random sampling -----
FloatProgress(value=0.0, layout=Layout(width='auto'), style=ProgressStyle(bar_color='black'))
Estimated u probabilities using random sampling
Your model is not yet fully trained. Missing estimates for:
- amount (no m values are trained).
- memo (no m values are trained).
- transaction_date (no m values are trained).
```

```
linker.estimate_parameters_using_expectation_maximisation(block_on("memo"))
```

```
----- Starting EM training session -----
Estimating the m probabilities of the model by blocking on:
l."memo" = r."memo"
Parameter estimates will be made for the following comparison(s):
- amount
- transaction_date
Parameter estimates cannot be made for the following comparison(s) since they are used in the blocking rules:
- memo
Iteration 1: Largest change in params was -0.529 in the m_probability of amount, level `Exact match`
Iteration 2: Largest change in params was -0.237 in the m_probability of transaction_date, level `Within 1 day`
Iteration 3: Largest change in params was -0.0185 in the m_probability of transaction_date, level `Within 1 day`
Iteration 4: Largest change in params was 0.00459 in the m_probability of transaction_date, level `Within 30 days`
Iteration 5: Largest change in params was 0.00129 in the m_probability of transaction_date, level `Within 30 days`
Iteration 6: Largest change in params was 0.000332 in the m_probability of transaction_date, level `Within 30 days`
Iteration 7: Largest change in params was 8.32e-05 in the m_probability of transaction_date, level `Within 30 days`
EM converged after 7 iterations
Your model is not yet fully trained. Missing estimates for:
- memo (no m values are trained).
<EMTrainingSession, blocking on l."memo" = r."memo", deactivating comparisons memo>
```

```
session = linker.estimate_parameters_using_expectation_maximisation(block_on("amount"))
```

```
----- Starting EM training session -----
Estimating the m probabilities of the model by blocking on:
l."amount" = r."amount"
Parameter estimates will be made for the following comparison(s):
- memo
- transaction_date
Parameter estimates cannot be made for the following comparison(s) since they are used in the blocking rules:
- amount
Iteration 1: Largest change in params was -0.356 in the m_probability of memo, level `Exact match`
Iteration 2: Largest change in params was -0.0328 in the m_probability of memo, level `Exact match`
Iteration 3: Largest change in params was 0.0442 in the m_probability of memo, level `All other comparisons`
Iteration 4: Largest change in params was 0.0483 in the m_probability of memo, level `All other comparisons`
Iteration 5: Largest change in params was 0.0427 in the m_probability of memo, level `All other comparisons`
Iteration 6: Largest change in params was 0.0318 in the m_probability of memo, level `All other comparisons`
Iteration 7: Largest change in params was 0.0211 in the m_probability of memo, level `All other comparisons`
Iteration 8: Largest change in params was 0.0131 in the m_probability of memo, level `All other comparisons`
Iteration 9: Largest change in params was 0.00792 in the m_probability of memo, level `All other comparisons`
Iteration 10: Largest change in params was 0.00474 in the m_probability of memo, level `All other comparisons`
Iteration 11: Largest change in params was 0.00283 in the m_probability of memo, level `All other comparisons`
Iteration 12: Largest change in params was 0.00169 in the m_probability of memo, level `All other comparisons`
Iteration 13: Largest change in params was 0.00101 in the m_probability of memo, level `All other comparisons`
Iteration 14: Largest change in params was 0.000602 in the m_probability of memo, level `All other comparisons`
Iteration 15: Largest change in params was 0.00036 in the m_probability of memo, level `All other comparisons`
Iteration 16: Largest change in params was 0.000216 in the m_probability of memo, level `All other comparisons`
Iteration 17: Largest change in params was 0.000129 in the m_probability of memo, level `All other comparisons`
Iteration 18: Largest change in params was 7.74e-05 in the m_probability of memo, level `All other comparisons`
EM converged after 18 iterations
Your model is fully trained. All comparisons have at least one estimate for their m and u values
```

```
linker.match_weights_chart()
```

```
df_predict = linker.predict(threshold_match_probability=0.001)
```

```
FloatProgress(value=0.0, layout=Layout(width='auto'), style=ProgressStyle(bar_color='black'))
```

```
linker.comparison_viewer_dashboard(df_predict,"dashboards/comparison_viewer_transactions.html", overwrite=True)
from IPython.display import IFrame
IFrame(
src="./dashboards/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))
```

```
FloatProgress(value=0.0, layout=Layout(width='auto'), style=ProgressStyle(bar_color='black'))
```

```
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))
```