OCR Document Verification – The Next Big Thing for the Finance Sector
The finance sector deals with tons of daily data that is manually generated, extracted, processed, and analyzed. However, this process is prone to human errors, data discrepancies, and accurate validation. Due to loopholes, criminals are laundering huge sums of money, illegitimately availing benefits, and taking over accounts for funding terrorism.
With the use of artificial intelligence and machine learning-based ocr document verification, the finance sector can deal with various types of data. These tools automate the information extraction process while driving more efficiency. Furthermore, banks and other financial firms can mitigate inaccurate data entries. This blog covers ways ocr document verification solutions streamline the onboarding process of the finance sector.
OCR Recognition Algorithm Provides Accurate Data Extraction
The application of ocr machine learning technology is widespread as it provides error-free data extraction and makes document verification easier. AI-powered tools leverage techniques that serve as long-term support for the finance sector. Similarly, banks and other firms need to integrate more efficient ocr document verification data extraction solutions to perform well in the digital world. Furthermore, to stay in line with the AML/KYC legal obligations, accurate customer identification has become a must-have for financial firms.
The finance firms can overcome challenges they face while authenticating legitimate customers. Criminals perform document forgeries that are hard to detect through manual verification methods. With ocr document verification technology in place, financial firms can automatically extract required information while validating the format. This technology is applicable to almost every document such as bank drafts, cheques, and others. The next section provides insights into the type of documents that ocr recognition algorithms extracts data from.
Unstructured Documents
These documents do not have a fixed format and the information is in freestyle design. Data within articles, reminders, and letters are prominent examples of unstructured documents. As this information is physically filtered, finance firms’ verification mechanisms fall insufficient in identifying fraud attempts. Therefore, banks and other firms require ocr document verification to accurately extract information from unstructured formats as well.
Documents that are not available within databases or defined formats also fall under this category. Despite the fact that they might hold important and valuable data, unstructured information is still hard to capture. However, the AI-powered ocr document verification extracts it in the form of objects, entities, objects, and relations. This way, ocr technology further adds to accurate unstructured information analysis.
Semi-structured Documents
As the name implies, semi-structured documents involve information that is neither raw nor completely in a definite pattern. Organizational information, invoices, IRS forms, and tags are some of the prominent features of these types of documents. However, the identifiers vary with respect to data within the papers.
Semi-structured data is difficult for format-based ocr document verification as it will extract inaccurate information from elsewhere on the paper. However, with advanced AI-powered IDV solutions in place, the finance sector can streamline the information extraction process. This way, invoice ocr scanning ensures accurate post-extraction analysis while limiting the hassle for customers during onboarding.
Structured Documents
Structured documents contain defined formats and are a part of global databases. These are the authentic information datasets that are way easier to analyze. Moreover, as ocr document verification tools easily extract data from structured formats, the finance sector can minimize further hassle and discrepancies they might face. This process involves the following steps:
- Submission of required documents.
- The system ensures the paper is not crumbled and the image is not blurred.
- Ocr then extracts required information in the form of mathematical algorithms.
While using AI-powered ocr machine learning tools ocr provides accurately extracted data for further analysis. As the system is automated, it eliminates human errors, cuts down operational costs, and provides seamless customer onboarding.
Use-Cases of OCR Machine Learning for the Finance Sector
Ocr document verification tools convert handwritten and pdf texts into machine-readable codes that are easier to analyze. This machine learning technology is capable of self-learning, extracting data from huge datasets, and providing easy-to-validate information varying in different formats.
The ocr companies are coming up with advanced technology to replace the finance sector’s labor-intensive tasks by providing automation. Search engines use this technology to index documents, old handwriting is easily extractable, and number plate recognition can be done in minimal time. The following are more of the ocr document verification use cases:
Automated Identity Validation
Banks and other firms striving to bring more innovations for their customers such as digital payments require more robust identification checks. To comply with regulatory standards such as BSA, the finance sector needs to develop efficient ocr document verification mechanisms.
Information Retrieval
Searchable PDF format falls under the use cases of ocr document verification application. These tools can extract data from the image-only files into searchable information.
In Summation
Ocr technology cuts down the resources finance firms as well as invoice processing companies invest in automating their operations. By extracting data from piles of paper, these AI-powered validation tools eliminate the need for manual interpretation. Ocr document verification solutions provide accurate information that is further analyzed by employing machine learning algorithms. Hence, this technology streamlines customer onboarding while authentically validating their proof of identity.
