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With the inclusion of AI and ML, IDP software can easily capture data from structured, unstructured, and semi-structured invoices. IDP’s pre-processing methods help identify the invoice structure and prepare them accordingly for efficient data capture. As a result, AI-powered document processing boosts the company’s efficiency by 10X.
This is just one aspect of data capture improved by AI.
10 Ways AI-based document processing is revolutionizing data capture
The combination of AI and ML removes complexity from traditional data capture processes. The system learns and evolves depending on the accounting department’s requirements. Let’s break down the different ways AI is improving on the already superior document data capture processes of IDP.
1. Adaptive learning
Adaptive learning is one of the trademark features of AI-powered software. AI uses machine models to learn the user's input. Eventually, it adjusts the processes, removes redundancies, and reduces reliance on human inputs.
AI significantly improves the accuracy and quality of data captured. The machine learning model constantly adapts to changing spending patterns, document formats, and new human inputs. It changes the data capture pattern to maximize the processing speed.
The adaptive learning capability of AI-based document processing systems is widely used in industries processing a high volume of invoices or documents regularly such as finance, banking and lending, real estate, logistics, and insurance among many others.
2. Contextual understanding
Unlike humans, rule-based document processing systems lack the ability to understand context while capturing data. This lack of contextual understanding is most noticeable when processing semi-structured, like PDFs and XML files, and unstructured invoices, like JPG and printed receipts.
AI solves this problem by understanding the grammatical context of the words and sentences used in the document. After identifying the purpose of the document, it sorts them for pre-processing and converts the unstructured and semi-structured documents into structured formats.
An AI-powered Intelligent Document Processing software can identify the document type and auto-classify them. For instance, the IDP system can effortlessly differentiate between shipment details and transport permits in a logistics company. After doing so, it sends them to the relevant departments for data capture and approval.
The best part about using an AI-enhanced IDP platform is that its screening technology, contextual understanding, and data capture speed exponentially increase as the volume of documents increases.
3. Complex data capture
AI is not limited to just understanding the context in a document, it also enables the organization to capture complex data from semi-structured and unstructured documents.
Semi-structured documents, like PDF, email, and XML, have the same data set but in different formats. For example, one invoice records purchase orders under the heading PO No., while another has it under PO Number. NLP allows the IDP software to understand the context of these varied invoice formats and capture data from them.
AI performs a quality check of the documents before sending them for pre-processing.. The pre-processing checks of complex data formats involve cleaning the data sets, and organizing the raw data so the IDP software can easily ingest the documents.
For unstructured document formats, the Intelligent Document Processing software deploys more rigorous pre-processing checks. The pre-processing includes annotating key data fields and labeling them accordingly. Machine learning models are used for this step. Labeling and annotations are minimal if the IDP software is using a global-trained and robust machine learning module.
The skew of the scanned document is also corrected in the pre-processing step. Finally, the software uses data validation to check the compatibility of the unstructured document with avail document formats in the system. Then, the raw data is converted into a structured format and prepared for data capture.
The multiple checks ensure that data processing accuracy remains over 99%.
4. Integration with other technologies
AI-based IDP software is compatible with other business applications, like customer relationship management systems (CRMS), human resource management systems (HRMS), enterprise resource planning (ERP), and accounting software.
The integration of AI-based data capture software with CRM platforms, like Salesforce and Zendesk, helps the business conduct cohort analysis to identify opportunities in different customer segments. Similarly, integration with accounting software, like QuickBooks and Xero, predicts future cash flow and gets a real-time view of financial runaways.
Recurring revenue management software, like Chargebee, and payment management applications, like Stripe, are also highly compatible with AI-based document processing systems. In short, all the different business applications treat the data captured by IDP as a single source of truth.
5. Advanced image recognition
The image recognition capabilities of AI-powered IDP systems are far superior to that of traditional image processing technologies.
Here’s a closer look at the primary pre-processing procedures- that enable the IDP software to achieve a high accuracy rate of 99%-
The IDP platform deskews the scanned image by straightening the corners and aligning them with the gridlines. Deskewing makes it easier to read the texts in the document.
The next step is to remove uneven contrast, unnecessary pen marks, printing spots, and other non-textual noise.
The document is converted into grayscale to prevent colors from interfering with the data capture process. In addition, grayscale makes the texts more visible and makes it easier to extract data.
Finally, the AI crops out unwanted white spaces and optimizes the image for data capture.
6. Enhanced data extraction
Data extraction capabilities are vastly enhanced by the application of AI. AI-based IDP systems can extract data from structured, unstructured, and semi-structured documents.
Detailing the data extraction from the three types of documents:
#1. Structured documents - They have a set of information that is consistent across documents: formatting, number, and layout. Think of utility bills, W9 and W2 forms, acord forms, passports, and payment slips.
#2. Semi-structured documents - Although these documents have a fixed set of data, the format of this data is not fixed. For example, a purchase order: in one variation could be PO number while the other variation would have Purchase Order number.
#3. Unstructured documents - Emails, letters, and reports contain information in a free format without any specific layout or organization of fields/content. As the data is not organized in a specific format or separated into fields, extracting information from unstructured documents is challenging.
7. Real-time data capture
The higher efficiency of AI-based data capture software enables the organization to capture data in real-time, which is highly valued by accounting departments, C-suite managers, FP&A, and finance teams.
AI-powered IDP software offers up-to-date data to the various teams. Accountants are empowered to create better and more accurate financial strategies. Finance teams make better investing decisions thanks to improved working capital performance due to real-time data tracking.
C-suite managers make quicker and more informed decisions due to the availability of fresh data.
The decision-making capabilities of C-suite managers also receive a significant boost. Live tracking the performance of the company allows them to make strategic decisions to steer the organization in the right direction.
8. Customizable workflows
The traditional data capture software was technologically limited and would struggle to optimize the data capture process for multiple organizations with the same template. But, that is not the case with AI.
AI-powered document processing platforms allow the user to create highly customizable end-to-end document management workflows to suit the organization’s needs. For example, the organization can set up a separate workflow with fewer approval checkpoints for invoices from established suppliers. In doing so, they can boost the STP rate to over 95%.
Similarly, AI data extraction offers more insights and helps the accounting team improve their existing workflows. The accountants, with the help of AI, replace repetitive and redundant tasks with optimized data capture and processing. In the long run, it improves the overall productivity of the organization.
9. Automated data validation
Manually validating the data is time-consuming and error-prone. The accounts payable teams have to compare the details in the processed documents with the available information to determine their legitimacy. Sometimes, it can take a few days to validate a single document. This process is resource intensive and slow.
AI-based data capture tackles the problem head-on and brings note-worthy enhancement to the document processing workflow. Data is instantly validated against all the available documents immediately after it is captured.
Invoice processing is one of the most important aspects revolutionized by AI-powered data validation techniques. When a vendor sends the invoice, the AI compares the bill amount, shipping details, payment information, and due date with the corresponding information available in the ERP.
The validation process is completed within seconds instead of days. The organization saves valuable working hours and decreases its operational costs by 70%.
There is no denying that data-backed decision-making is responsible for scaling the business. But, fast-scaling businesses often face difficulty in capturing and processing large volumes of data. Especially, when they are dealing with physical or semi-structured documents. And, employing more human labor is not a viable solution as it decreases the overall ROI.
AI based document processing is ideal when documents need to be processed at scale. The ML modules keep learning and improving the processes to accommodate the growing document inflow from these businesses. In a sense, AI document processing software grows with the business.
The introduction of automated workflows reduces the manual workload and frees up the accounting team’s time to focus on crucial business activities. This, in turn, decreases operational costs and improves cash liquidity. The cost savings are reinvested in the businesses to enhance the working capital management. And, with better wording capital management, the organization can invest more money to improve their AI software.
Docsumo for AI-based document processing
Docsumo is an AI-based document processing and data capture software that can be integrated with existing ERPs and business workflows. What’s more, this cloud-based IDP supports bulk uploading of documents and supports batch processing at scale.
Docsumo assists the business with-
Processing invoices within 30-60 seconds
Decreasing operational cost by 70%
Increasing STP rate to 95%
Increasing the overall efficiency of the organization by 10X
Extracting data from receipts, utility bills, landing bills, etc.
Automated data extraction from IRS forms
Enhancing identity verification through AI-powered image processing
The most cumbersome part of employing an AI is training it from scratch. However, that is not the case with Docsumo.
Docsumo comes with pre-trained APIs for processing common document templates. In other words, you do not have to train the AI from scratch. The APIs are designed to highlight errors, remove duplicate entries and eliminate redundancies.
Docsumo also helps the user establish customized workflows for their organization. Using the dashboard, accountants can track the data in real-time and analyze captured information using sophisticated tools to gain insights.
If you are looking for a reliable and pre-trained AI data capture system for your organization, try out Docsumo’s 14-day free trial.
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