AI for Document Processing

Organizations across the world receive thousands of documents daily which require continuous processing. These documents could be account opening or transfer forms, accounts receivable or payable, invoices, purchase orders, contract documents, Know Your Customer (KYC) forms etc. Everyday, such documents are handled manually by company associates, who spend a lot of time in tedious repetitive information extraction from these documents in order to facilitate business processes. Mphasis AI for Document Processing service utilizes cognitive solutions leveraging Artificial Intelligence, Pattern Recognition, Image Processing, Natural Language Processing and Machine Learning for processing of documents. Mphasis AI for Document Processing solution performs multiple functions to transform end-to-end document processing and bring efficiency in the process:

• Improving document quality for better OCR processing
• Perform automated classification of documents for better queue management
• Extract information from a variety of formats such as PDFs, scanned PDFs, TIF Images, JPG Images, Doc files etc.
• Extract non-text information like check marks/OMRs through image processing
• Refine the extracted information by incorporating field format rules, validation checks (internally as well as with external databases), consistency checks etc.
• Reduce the manual intervention to exception handling, thereby allowing for scaling of document processing without a commensurate significant increase in manpower required

Mphasis AI for Document Processing works on various documents to bring structure to unstructured data. Data in a structured format facilitates analysis and the generation of actionable insights from the information, and documents that would otherwise be stashed in some corner are transformed into a treasure trough of valuable new information.

Assessments & Workshops

Our Assessments and Workshops help clients get started with intelligent document processing. This engagement enables clients to plan and identify the relevant business problems which could be candidates for solving using AI for Document Processing. Assessments are a fast way of identifying if AI for Document Processing is the relevant technology to leverage for solving a business problem. In the workshops, the Mphasis AI/ML experts, SMEs, architects, and data scientists work closely with the client stakeholders to understand the system and data. The engagement includes activities such as stakeholder interviews, assessment and user journey workshop, identification of frameworks and tooling, gap analysis, understanding and assumptions, recommendation of solution, and report-out. The typical duration of the assessment and workshop exercise is spread across 3-4 weeks and the outcome of this exercise is a prioritized set of use cases along with recommended infrastructure to solve the problems, recommendations on frameworks and tooling, and reference architecture.

Algorithm Development and Implementation

Algorithms Development and Implementation is a 2–3 month long engagement, where we bring our AI for Document Processing IP as well as customizations to solve the specific client problems. Solutions under Mphasis AI for Document Processing services have been implemented for clients across various verticals like insurance, banking, logistics, pharma, manufacturing etc. Our services are powered by our patented AI/Ml platforms and frameworks such as DeepInsights, PACE-ML, HyperGraf and InfraGraf. The Mphasis IP overcomes the limitations of systems not being able to work on varied input datasets and consists of 200+ pre-built and customizable Machine Learning, Deep Learning and Natural Language Processing algorithms. Our IP helps clients arrive at actionable recommendations faster and provide measurable results across scenarios. Our experience in AI/ML algorithm development and implementation are spread across several industries as well as use cases such as improved customer experience, recommendation of right products at the right time for the right customer, profit margin maximization, faster checkout processes, improved productivity, reduced disputes, reduce false positives, anomaly detection, predictive analytics recommendations, text categorization on large document corpus, natural language generators etc.