DIRECTIVE Account Based Transactions Data Cleaning Challenges for Banks

•Identifying Customers with more than One customer IDs.

•Identifying Duplicate Customer Names without National ID

•Customers with multiple accounts under different customer IDs but same Names

Cutting edge technologies

Surveys reveal that its common for businesses to have up to 30% duplicate data, costing upwards of $600 billion nationally each year. The costs associated with duplicates can quickly add up, ranging from wasted marketing budget when the same customer is sent marketing material multiple times to lost productivity when your sales reps must sift through the CRM to correctly identify and deal with a customer having multiple entries.

Machine learning and AI based
- Latest text similarity matches
- Categorize duplicates
- Filter only duplicate data
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Fuzzy dedupe uses
- Sophisticated fuzzy logic to find duplicates in any data even when entries are misspelled (John Smith = John Smyth) or when they are reversed (John Smith = Smith John)
- Find duplicate payments to vendors
- Use it to clean up master data such as contact names, customer names, or any structured data list
- Look for duplicates in any type of data where 2 entries are similar but not identical
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Dats Inputs and Outputs

Supports both CSV and Excel inputs

Supports bout CSV and Excel data outputs

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Cutting edge technologies

Machine learning and AI based

Fuzzy dedupe uses

Dats Inputs and Outputs

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