Auto-extraction & application pre-fill - Streamline On-boarding

Description

By leveraging AI and ML techniques for auto-extracting information and auto-filling customer onboarding applications, banks and payment providers can streamline the onboarding process, reduce manual data entry errors, and improve operational efficiency. This approach saves time for both the customers and the institution, providing a smoother and more user-friendly onboarding experience.

Document Capture: The first step is to capture the ID document provided by the customer. This can be done through various means, such as scanning the physical document, taking a photo with a mobile device, or uploading a digital copy. The document should be captured in a clear and readable format.

Image Preprocessing: Once the document is captured, preprocessing techniques are applied to enhance the image quality and extract the relevant regions of interest. This may involve tasks like image resizing, noise reduction, contrast adjustment, and image cropping to isolate the ID document from the background.

Optical Character Recognition (OCR): OCR technology is employed to extract the text and numerical information from the ID document. ML-based OCR algorithms are trained on a large dataset of ID document samples to recognize and interpret characters, numbers, and other relevant details. This step involves extracting data from fields such as name, address, date of birth, ID number, and any other pertinent information from the document.

Data Validation and Quality Check: The extracted data is then validated and verified to ensure its accuracy and completeness. This may involve comparing the extracted information with predefined rules or databases to validate the ID number, confirming the consistency of the extracted fields, and checking for any potential errors or missing information.

Natural Language Processing (NLP): NLP techniques can be applied to further understand and interpret the extracted text. For instance, entity recognition algorithms can identify specific pieces of information like the person's name, address, or date of birth. This step helps in structuring and organizing the extracted data for further processing.

Auto-Filling the Application Form: The validated and structured data is automatically populated into the relevant fields of the customer onboarding application form for the new banking or payments product. The application form is designed to capture additional information required for account setup, such as contact details, employment information, and financial details.

User Review and Confirmation: After auto-filling the application form, the system presents the pre-filled information to the customer for review and confirmation. The customer can verify the accuracy of the populated fields and make any necessary corrections or additions if required.

Manual Review and Approval: Although the AI and ML techniques significantly automate the data extraction and filling process, a manual review is typically conducted by bank personnel to ensure the accuracy and integrity of the information. This step acts as a final quality check before the application is processed further.

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Key Business Outcomes

Key Metrics

Faster on-boarding: Auto-extraction and application pre-fill can significantly reduce the time required to complete the on-boarding process, improving customer satisfaction and reducing drop-offs.

Improved data accuracy: By using AI to extract and pre-fill customer information, the bank can reduce the risk of errors and improve the accuracy of customer data.

Reduced costs: Automating data extraction and pre-fill can help reduce costs associated with manual data entry and improve the efficiency of the on-boarding process.

Drop-off rates: Measuring the percentage of customers who abandon the on-boarding process can help the bank assess the effectiveness of its auto-extraction and application pre-fill in improving the customer experience.

Time to complete on-boarding: Measuring the time taken to complete the on-boarding process can help the bank assess the efficiency of its auto-extraction and application pre-fill process in reducing on-boarding time and improving customer satisfaction.

Data accuracy rates: Measuring the accuracy of customer data pre-filled by AI can help the bank assess the effectiveness of its auto-extraction and application pre-fill in improving data quality.

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