Are you trying to implement an autonomous data extraction pipeline? Are you tired of struggling to extract financial data from documents with varying layouts and in multiple languages? Post-processing each document layout separately is painful, and fixing errors that occur after fixing previous errors can be such a headache! Did you always think that you or your programmers are the problem? You were wrong. OCR is the problem.
Traditional OCR solutions just don't seem to cut it!
Why OCR Approach Is Outdated (And Hard to Maintain)
- Lack of accuracy. OCR isn't always as accurate as we'd like it to be, which can result in errors and inaccuracies in the extracted data. Also, relying on OCR technology for data extraction may not be sufficient for more complex documents;
- Inability to understand layouts. OCR has trouble extracting and understanding document layouts. This makes getting specific information from required documents with different formats very tricky;
- Сonfusion in detail. OCR can get confused when it comes to the difference between similar fields in a document - it may mix up sender and recipient, buyer and payer, or individual tax identification numbers with business registration numbers;
- Resource-intensiveness. Creating OCR pipelines to handle each new document layout and use case programmatically takes a lot of time and money.. This can be especially problematic in finance where there are many different document types and layouts to consider. Moreover, creating OCR post-processing scripts is a never ending race, where your team has to adopt OCR systems for the newly updated layout of some document.
How We Automate Data Extraction
With the aim of solving the problems mentioned above we at Tensorway have prepared the perfect Data Extraction solution for you! Our innovative approach overcomes the limitations of traditional OCR solutions and provides accurate and reliable results. It is designed to work seamlessly with a variety of document types! Whether you're dealing with ID cards, invoices, contracts, or something else, our approach can be tailored to meet your unique needs. This is the beauty of AI solutions for fintech.
Why Our Data Extraction Is Better than Traditional OCR
- Layout independence. Unlike traditional OCR methods, which require significant manual adaptation to new document layouts, causing delays and increasing the risk of errors, our approach is layout-independent. This eliminates the need for manual adjustments, allowing us to quickly adapt to new document layouts and account types after training the model.
- Several languages. A massive advantage of our approach over simpler OCR methods is that it can extract information from invoices in several languages. We understand that the ability to handle invoices in multiple languages is crucial for businesses that operate globally. This not only saves time and resources but also improves overall accuracy and efficiency.
- Image recognition. Our model works great with photos, too! Unlike traditional OCR systems that struggle with distorted, poorly-lit, or rotated images, our model was prepared to work with low-quality data. That means we can extract data from images with no trouble at all. This feature is especially useful for businesses that deal with physical documents, where people may upload photos of questionable quality.
- Customization. We'll adjust our models to fit your company's particular requirements. We can train our model to extract the fields required for any specific assignment with just a few thousand examples. Our software is highly customizable and can be tailored to meet the unique needs of your business, providing a personalized and effective solution.
- Cost-effectiveness. Using our Data Extraction solution you can save your programmers from never-ending support of your OCR-based document extraction pipeline and let them implement new exciting features for your product, as well as help your support teams be of service for your clients rather than fixing errors in incorrectly-extracted invoices or reports.
How Data Extraction Solution Works
- Uploading. At first, we are uploading a document. Our model is versatile and can support a wide variety of formats! This means it’s possible to extract important data and information from a wide range of file types, making it even easier to streamline your data extraction process.
- Extraction. At this stage, we simply request our model to extract the necessary fields. Our solution is designed to extract important information from documents with different layouts and languages. So, whether you're working with contracts, invoices, or other important documents, our solution has got you covered!
- Verification. You can easily verify if an uploaded image is a document or not before attempting to extract information from it. This helps prevent the misuse of the model and saves time and resources in the process. The verification step is especially beneficial for solutions that are in production and under heavy load.
- Export. All the extracted information can be exported to the desired format. This is a crucial part of the process, as the data is often needed in a specific format. This feature saves businesses a significant amount of time and effort as there is no need to manually enter the extracted data into a different format.
Our Data Extraction Software Development Approach
And just so you know, we take our data extraction software development very seriously. We've got a rigorous approach that involves several key stages to ensure the accuracy and effectiveness of our final solution.
- Data preparation. We carefully select and label a dataset of documents to train our machine learning algorithms.
- Training and validation. We train and validate our machine learning algorithms, adjusting parameters for maximum accuracy.
- Deployment. We deploy it in a production environment, integrating it with other software systems and ensuring it can handle required volumes of data.
- Monitoring and maintenance. We continuously monitor and maintain our data extraction software to ensure it remains accurate and effective over time. Moreover, if your data changes and the production model loses part of its predictive power, we can detect it and adapt the model to effectively handle new cases.
Data Extraction Software Use Cases in Fintech
- Financial statements processing. Financial statements are packed with valuable information that can help make critical decisions. However, analyzing this information can be time-consuming and challenging, especially when dealing with a large volume of data. That's where Data Extraction Software comes in! This software can extract financial data from statements and transform it into a structured format that is easy to analyze. With this tool, financial analysts can quickly obtain the data they need to make informed decisions that can positively impact their organization.
- Invoices processing. With Data Extraction Software, you can streamline your invoice processing by automatically extracting important information such as purchase orders, payment terms, and delivery dates. This software can validate the extracted data and initiate payments without any manual intervention. This can save a lot of time and effort, allowing you to focus on more important tasks. Also, Data Extraction Software can help reduce errors and improve accuracy in invoice processing, as the software can quickly identify and correct mistakes in data entry.
- Loan underwriting automation. When you apply for a loan or a credit card, your financial documents are reviewed to assess your creditworthiness. This includes analyzing your loan applications, credit reports, and other financial documents to determine whether you qualify and at what interest rate. With the help of Data Extraction Software, this process becomes more efficient and accurate as it can quickly extract the necessary data and analyze it to make informed decisions. It significantly helps to reduce the risk of errors and biases that may occur.
- Compliance processes automation. Data Extraction Software helps to automate various compliance processes in the financial industry, including anti-money laundering (AML) and know-your-customer (KYC) checks. By extracting data from customer identification documents and verifying that the customer meets the necessary compliance requirements, the software can help businesses reduce the risk of errors and ensure compliance. This means that compliance teams can spend less time manually checking documents and more time focusing on high-risk cases.
Ready to apply an innovative approach to solving your data extraction challenges?
There's nothing undoable and unextractable for Tensorway DL team. Drop us a line to find out more about what you can get from data extraction.
Data extraction using AI refers to the automatic identification and extraction of relevant information from unstructured or semi-structured data sources, such as text documents or images.
Optical Character Recognition (OCR)
Optical Character Recognition (OCR) is a method for recognizing and reading text in images with Computer Vision technology.
White Label AI
In the AI world, white label AI solutions are ready-made solutions sold under different brand names.