As a crucial element in conducting a successful and secure cashback campaign, Receipt OCR and Validation can play a significant role in preventing fraudulent activities. By leveraging OCR (Optical Character Recognition), we can convert the data from scanned or photographed receipts into a machine-readable format, allowing us to automate the process of verifying receipt information. We can extract essential information such as store name, date, time, total amount, tax, and line items, ensuring that each receipt meets our specific campaign rules. Additionally, we can analyze the purchase patterns to identify potential fraudulent behavior, such as an unusually high number of submissions from a single user or IP address. This guide aims to provide a step-by-step process to implement OCR and validation effectively for the prevention of fraud in cashback campaigns.
Prepare an image file for text extraction.
Utilising Optical Character Recognition (OCR) tools to convert the text from images into machine-readable data.
Post text extraction, parse it to isolate relevant information. This could include the store name, date, time, total amount, tax, and line items. Implement regular expressions (regex) or Natural Language Processing (NLP) libraries for parsing. For instance, to extract dates, you might look for patterns like "MM/DD/YYYY" or "DD/MM/YYYY".
Contrast the parsed data against the data structure of a valid receipt. Implement rules or conditions that verify if required fields are present and contain logical values. For example, ensure that the date and time on the receipt fall within the campaign's eligible timeframe. In some cases, product code and product name are helpful values to validate the receipts.
Set up your rules for validation. For instance, you might disregard receipts with totals under a specific amount, or receipts that do not include certain obligatory purchases. These rules will largely be dependent on the specifications of your cashback campaign.
Examine if an unusually high number of similar receipts are being submitted by the same person. For instance, you might flag users who submit receipts totalling more than a certain dollar amount per day, or users who submit receipts from different cities on the same day. These steps should be adjusted and tailored according to your specific campaign requirements and the quality of your receipt data. High-quality preprocessing and parsing will enhance the accuracy of your validation.
Using the Taggun Receipt Validation API with it's new fraud detection features, you can set up the entire technical process to work with a high degree of automation, with the exception of analysing purchase patterns. Read more about the new fraud detection features and how they could impact you here.
Updated 3 months ago