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22f9e57c-57e7-441e-bc61-83df33f91870.zip

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Banking industries face challenges when it comes to bank checks processed either manually or electronically. Fraudulent check detection is still a challenge when you manually try to do verification and validation. Especially verifying signatures manually is prone to error and time-consuming.

  

The main purpose of this Check Reader and Validator Bot is to extract the MICR, Date, Amount, Name, and Signature from handwritten checks, then apply an AI algorithm for Fraud Detection, reducing all check-processing time and with enhanced accuracy.

  

As part of our solution, we developed automation that essentially does the following 3 things:

  

1\. Acquires the check leaflet key components, essential for the task of check clearance. These components include the check number, legal as well as the courtesy amount, account number, and signature patterns.

  

2\. Apply business rules for Straight Through Processing, apply the Signature Comparison AI model for Fraud Detection.

  

3\. Upload data to a target system / generates a report for processing status.

  

The objective is achieved using UiPath Document Understanding, UiPath Entity Data Services, and AI Center.

Extracts metadata from handwritten and electronic checks, applies business rules for Straight Through Processing, and uses the Signature Comparison AI model for Fraud Detection.

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Visual Basic

8bb0a2d8-2db1-47c4-9593-a50b945419aa.pdf

eef945e6-32e6-4681-9f03-f61fb4c095e4.zip

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a2045d68-5943-420a-b04f-b9d2c897f47d.zip

Banking industries face challenges when it comes to bank checks processed either manually or electronically. Fraudulent check detection is still a challenge when you manually try to do verification and validation. Especially verifying signatures manually is prone to error and is a time-consuming task.

  

The main purpose of this Check Reader and Validator Bot is to extract the MICR, Date, Amount, Name, and Signature from handwritten checks, then apply an AI algorithm for Fraud Detection, reducing all check-processing times with enhanced accuracy.

  

As part of our solution, we developed an automation that essentially does the following 3 things:

  

1\. Acquires the check leaflet key components, essential for the task of check clearance. These components include the check number, legal as well as the courtesy amount, account number, and signature patterns.

  

2\. Applies business rules for Straight Through Processing, applies the Signature Comparison AI model for Fraud Detection.

  

3\. Uploads data to a target system / generates a report for processing status.

  

The objective is achieved using UiPath Document Understanding, UiPath Entity Data Services, and AI Center.

Version	Release Date	Download Link
1.0.1	June 16, 2023
Extracts metadata from handwritten and electronic cheques, applies business rules for Straight Through Processing, and uses the Signature Comparison AI model for Fraud Detection.
1.0.0	January 18, 2023
Extracts metadata from handwritten and electronic cheques, applies business rules for Straight Through Processing, and uses the Signature Comparison AI model for Fraud Detection.
1.0.0	January 12, 2023
Extracts metadata from handwritten and electronic cheques, applies business rules for Straight Through Processing, and uses the Signature Comparison AI model for Fraud Detection.