When it comes to capturing all data types, especially unstructured, for end-to-end process automation initiatives, companies employ OCR, the technology that has its own limitations.
Companies struggle to extract relevant information, which provides them erroneous reports, data loss, and paperwork overload. In such situations, companies cannot rely only on OCR, and this is where CMR becomes a potential disruptor.
CMR can automate data extraction from structured and unstructured documents that may contain:-
CMR stands for Cognitive Machine Reading and enables you to surmount the deterrents of digitizing unstructured data and extracting information from credentials.
CMR is developed using proprietary pattern-matching via methods based on content-based object retrieval, which renders significant levels of precision. It detects patterns and associated confidence scores rather than font sets, which helps avoid anomalies.
Here are some potential benefits of implementing CMR in your system:-
CMR is the only data ingestion engine that satisfies the complex Machine Vision requirements of extremely unstructured and disparate data, and caters to most document formats.
CMR provides state-of-the-art Natural Language-based classification with modest data sets, identifying complex patterns in data. CMR extracts information from unstructured data that is complicated to analyze and summarize.
It also analyzes data that cannot be processed with rules, has a minimal or no structure or metadata, and includes non-text-based materials.
Owing to its streamlined configuration, CMR can get implemented expeditiously as its engine is capable enough to grasp from a limited representative set of documents. You can synthesize massive amounts of customer data via Natural Language Modelling (NLM) to create deeper insights.
OCR (Optical Character Recognition) has its limitation in reading unstructured data. Powered by fractal science, CMR is competent enough to read all data types and maintain data certainty and accuracy.
While CMR portrays extreme competency in capturing all data types, here are specific means through which it benefits your enterprise -
CMR lets you leverage 85% of the untapped and unstructured data prevalent in your organization, implying that you receive enhanced results by automating deeper processes as well as more complex data.
CMR offers a better capture rate with over 80% accuracy of consistently capturing information. CMR lets you promote the level of data certainty to attain a higher percentage of straight-through processing.
Streamlined configuration and faster implementation by employing smaller data sets for training, CMR boosts automation with efficiency and speed. CMR also keeps learning from your processes and promotes ongoing optimization.
CMR’s approach to automation provides enterprises with competitive leverage because it -
The perks of employing Cognitive Automation Reading extend to cognitive scanning of information as intelligent engines consistently keep learning from a representative set of documents through Machine Learning. These engines share the acquired knowledge in one process with another.
CMR and Machine Learning transcend way beyond traditional OCR, which, despite being widely employed from a legacy perspective, processes only structured data.
Independent of zones, formats, templates, modes and languages, CMR processes, curates, and categorizes data across several languages, establishing that it stays available for downstream policy processing.
You can also export data for downstream consumption across several formats such as CSV, XML, JSON, and DB schema.
CMR resolves the challenge of processing unstructured data that haunts various industries that deal with data capturing each day. CMR is particularly beneficial for legacy systems of records and copious documents and data stored in multiple formats.
CMR expands your automation scope and provides a better ROI, enhanced data certainty, and constant improvement for optimizing the automation of business processes.
CMR offers substantial benefits over OCR, considering its ability to process structured and unstructured data without missing the intricacies.
Companies with data extraction in their list of mundane activities must consider switching to CMR to gain better insights into data, extract more information, enjoy a better ROI, and process almost all data types.
In today’s dynamic business world, filing and archiving official documents in the digital form makes it handy, and works wonders in the future or in unforeseen circumstances.
With an automated data extraction solution, loan documents can automatically be processed end-to-end without any human errors and delays. Automation in loan document processing prevents downtimes, eliminates data redundancy, and allows companies to respond faster to client queries. By combining machine learning with deep learning and OCR, companies can eliminate huge costs, derive actionable insights, and streamline loan processing and approvals through efficient data extraction and analysis.
Mortgage lenders receive multiple identity and income verification documents along with different forms from loan applicants in a variety of formats and styles. Traditional OCR solutions fail to extract data from these semi-structured documents and that’s why more and more lenders are adopting intelligent document processing solutions. IDP solutions not only extract data correctly, they are able to validate extracted data against predefined rules in order to improve accuracy.
Intelligent Document Processing is an automation technology that captures information from a myriad of documents and data sources, extract data, and organizes it for further processing. IDP solutions enable businesses to seamlessly integrate with core processes, eliminate manual labour, address challenges faced in reading different document layouts, and meeting legal & compliance requirements. Accurate data is the foundation of every organization, and IDP assists businesses in dealing with the complexity of processing huge volumes of documents, helping them automate manual data entry processes, and move away from traditional semi-automated OCR workflows.