Role of AI & Machine Learning in Straight Through Processing

Role of AI & Machine Learning in Straight Through Processing

With the advancement of technology, everything in today’s world can be automated. That includes cars, warehousing, restaurant servicing and so on. With such leaps of innovation, legacy-based organisations and other firms from various sectors that are usually tech-averse, are thinking to automate certain processes to become leaner and more agile in their operations.

Document automation is one such area wherein businesses can evaluate to introduce automation in their organizations. No matter which industry does the organisation fall in, document automation is the need of the hour across every sector.

Straight Through Processing is a layer of document automation process that makes end-to-end document digitization achievable. Invoices, receipts, loan agreements and other such documents can be automatically processed.

The document automation process has plenty of benefits. To start with, accurate extraction of information is one of the most important benefits of document automation. Apart from that, classification of documents, ensuring the datasets are uniform, are other important aspects of document automation. It also helps in making business operations seamless and smooth.

The role of Straight Through Processing in document automation is crucial in order to achieve higher throughput in lesser time with minimal exceptions for human review.

Elements of AI & ML in Document Automation & STP

Artificial Intelligence and Machine Learning play a very important role to automate and implement an end-to-end document processing. Here are some of the key elements of AI & ML that are leveraged to achieve Straight Through Processing.

Contextual AI:

The text on the documents is understood in its contextual form by the AI. For example, the details of the supplier may be written on the upper left side or upper right side of an invoice. The details of the manufacturer may be written on the bottom left or bottom right of an invoice. With the ability of contextual AI, the technology can automatically detect and autofill the details on the supplier form and manufacturer form.

Details of the manufacturer and supplier is just one element of contextual AI. There can be multiple factors and multiple data fields that need to be automatically extracted and sent for processing in further stages.

Deep Learning:

Deep learning AI, in the context of document automation, refers to understanding the variety of documents and layouts that are processed just as a human would do. Deep learning is part of machine learning.

Let’s say there is a document that is being processed for the first time in the system. The deep learning AI will encapsulate the information using image processing of the document. The document is eye-balled by the computer vision and natural language processing deep learning models and based on contextual understanding of the data, the information is accurately extracted in the respective fields.

There are many other elements to deep learning such as recurrent neural networking (RNN), convolutional neural network (CNN), that help in interpreting data based on the context.

Self-Learning Abilities:

After a certain number of documents are processed and based on human correction feedback, the AI continuously refines it’s predictions to improve accuracy. Certain fields for some documents may require human inputs and that is where the role of self-learning AI comes into play.

Say there has been an error caused while auto-filling the fields from the information extracted. Such errors can be rectified with human intervention. Once the human inputs are in and the document has been rectified, the documents are sent for processing at further stages. Next time the similar document is being processed, that particular error that occurred last time, will not happen again. This also helps at the stage of data validation and Straight Through Processing.

With the help of self-learning, human intervention is minimized and might only be required in the case of processing completely new documents.

Natural Language Processing:

Natural Language Processing is used in addition to Computer Vision. With NLP, data can be accurately parsed, identified and normalized based on the text information flow and can be split into well-defined data elements that the system can understand. This helps in the highly accurate form of data extraction and formatting of the datasets.

For example, let’s assume that there are two different invoices and in both the invoices, the date is written in a different format. In one of the invoices, it is in DD-MM-YYYY format and in another it is in MM-DD-YY format. Through NLP, when the data is extracted and auto-filled, NLP technology will first identify exactly which date amongst many mentioned on that page is the invoice date and then standardize the format of date according to the system configuration which can be DD-MMM-YYYY. This means that the data that is extracted from any type of document will be standardized according to the system.

The KlearStack Advantage

KlearStack AI can automate invoice and receipt processing with help of state-of-the-art solutions that help you achieve almost > 90% Straight Through Processing in data extraction. With help of Straight Through Processing, document automation becomes even more seamless. This makes your organization even more agile, efficient and can allocate resources to focus on expanding your business.

Our team of experts is available to walk you through and make you understand how exactly our document automation works. If you are interested in implementing an automation solution that will help your organization to reduce the clutter of physical documents, click here and connect with our experts.

Ashutosh Saitwal
Ashutosh Saitwal

Ashutosh is the founder and director of the award winning KlearStack AI platform. You can catch him speaking at NASSCOM events around the world where he speaks and is an evangelist for RPA, AI, Machine Learning and Intelligent Document Processing.