The Vital Role of High-Quality Data
When we talk about the data you send to Salv, emphasising its quality isn't just a matter of preference — it's crucial for the effectiveness of every task. Here are some examples how low quality data can affect various processes:
Transaction Screening:
Let’s say you are screening
sender_nameagainst Sanctions.Your clients receives a transaction with sender ‘Eleazar Medina Rojas’ who is a sanctioned person - you get an alert.
Unexpectedly, another transaction from ‘Eleazar Medina Rojas’ arrives, however, this time the sender_name field also includes ‘Nuevo Laredo’, part of an address. Due to non-name parts in the
sender_namefield fuzzy matching score is below the set threshold, so no alert is generated.
This example shows just how crucial it is to maintain clean and consistent data formats for accurate screening and why even small deviations can lead to big misses in catching risky transactions.
Person screening:
Let’s say you screen your customers against PEP/RCA lists.
You onboard a Person with
first_name last_namewritten in non-latin alphabet, e.g. სალომე ზურაბიშვილი - Georgian alphabet.Because Salv currently supports only Latin and Cyrillic alphabets, it fails to recognise this person as a PEP, and no alert is generated.
Country screening:
Let’s say you have country screening set up using a Custom list that checks country fields of both Transaction (e.g.
sender_country,receiver_country) and Person (e.g.country).The Custom list has a list of sanctioned countries in ISO2 format (two letter country codes).
If you send three-letter country codes (ISO3) or full country names instead, the system might not recognise these entries. This mismatch can lead to missed or incorrect alerts.
Monitoring scenarios or Risk rules
Let's consider a scenario where:
You have a Monitoring scenario that generates an alert when client has reached the expected monthly turnover amount - OR - a Risk rule that checks your client’s expected monthly turnover and based on it assigns a risk level.
In your case system expects data field ‘
expected_monthly_turnover’ to contain numerical values representing the upper limit of expected turnover, e.g. ‘10000’, ‘50000’, ‘100000’ and so on.Unexpectedly, you start sending values in different formats, such as ‘10k’, ‘50k-100k’, ‘1m’ and so on - it's like the system is looking for apples, but you're giving it oranges.
In such cases, the system might not correctly interpret the expected monthly turnover, potentially leading to incorrect risk levels or missed alerts.
💡 NB! The examples we've shared show just a few ways low-quality data can reduce the effectiveness of your compliance efforts. Remember, there are many more ways data inconsistencies and inaccuracies can hamper the system's performance.
We check the quality of your data during onboarding, but if you ever need assistance with your data, please don't hesitate to ask for help. We're here to support you.
Enhancing Your Data Quality
Here are a few tips to ensure the data you provide is of the highest quality:
Consistency: Ensure that data formats and values are consistent.
Completeness: Missing information can lead to gaps in Screening, Monitoring and Risk assessment. Make sure each data field is as complete as possible.
Accuracy: Regularly review and update your data. Outdated information can lead to incorrect Risk assessments and compliance issues.
Source: Knowing where and how your data is collected can help identify potential quality issues before they reach us.
By focusing on the quality of the Person Data and Transaction Data you provide, you empower us to unleash the full potential of Salv’s capabilities. It's a collaborative effort that enhances our shared goal of protecting the financial system from money laundering and financial crime. Your commitment to data quality is a testament to your institution's integrity and dedication to making the financial world a safer place.