Intelligent Document Processing (IDP)
Cygnet Fintech’s Intelligent Document Processing is a template agnostic solution transform unstructured document information into simplified data. It uses Artificial Intelligence (AI) technologies such as natural language processing (NLP), deep learning and machine learning (ML) to Extract and Validate data, Process Data for Smart Reporting, and Analyze Data for apt decisioning.
How does Intelligent Document Processing (IDP) work?
IDP uses complex deep-learning AI technology to scan documents and classify them.
Once the data is classified, the next step is extraction. IDP uses cognitive AI technologies to identify specific pieces of data from the larger document. This data is then validated with fuzzy logics, organized, and presented in an easily accessible format.
Pain Points
Benefits
Leverage template agnostic Intelligent Document Processing solution for multiple business documents
Lending Documents
- Bank statements
- Financial statements
- IT return
- GST return
- Pay Slips
- Bureau reports
- Credit card statements
- Loan ledgers/statement of accounts
- Application forms
- MSME certificates
- Agreements
KYC Documents
- Aadhaar card
- PAN card
- Driver’s license
- Passport
- I-20
- I-94
- RC book
Accounts related Documents
- Invoice
- Proforma invoice
- Credit & debit invoice
- Purchase order
- Delivery note
- Packing list
- Bill of lading
- e-Way Bills
Bridge automation gap with Intelligent Document Processing?
Businesses are generating & collating more and more data, the need for automation solutions to extract and process that data has become increasingly important. One of the main challenges businesses are facing today is the gap between data that is stored in structured databases and data that is stored in unstructured documents. Most of the business data is embedded in unstructured formats like business documents, emails, images, and PDF documents. This is where Intelligent Document Processing can make a significant difference.
IDP can have a significant impact on business processes. By automating the process of extracting and processing data from unstructured documents, businesses can reduce the time and resources needed for data entry and minimize the likelihood of errors. This can improve efficiency and productivity, ultimately leading to cost savings and improved customer experiences.
Banks often face the hurdle of managing multiple documents submitted by loan applicants after filling them manually. Data extraction from those documents is a challenging task for a Relationship Manager (RM). Intelligent document processing simplifies the task as the RM is only required to submit the scanned copies along with digital copies of bank statements, and other financial documents required. The automation enables the magic of data extraction followed by analysis empowering the credit officer with the right insights.
Moreover, IDP can also help to comply with regulations and improve data security. With IDP, sensitive information can be automatically identified and secured, reducing the risk of data breaches and other security threats.
