In the world of digital transformation, documents are still the primary source of data for many businesses. A large number of documents are still manually processed to ensure businesses yield desired outcomes. These documents are an indispensable part of the digital ecosystem, as they contain critical information. Presently, a great deal of time and money is spent analyzing these assets — which include paper, signatures, handwritten documents, PDFs, emails, faxes, images, drawings, graphs, reports, voice memos, and videos — to gain meaningful insights.

Businesses looking to enable data processing with cutting-edge technologies are turning to intelligent document processing (IDP). IDP techniques help businesses process assets. They address data-deficient areas by adding additional reinforcements to business KPIs. IDP extracts and separates applicable data for further processing with the use of cognitive/AI instruments, like optical character recognition (OCR), natural language processing (NLP), deep learning, and machine learning.

Simply put, IDP helps businesses generate insights that are vital for decision-making and were previously unavailable.

Industry trend

Several industries are using their digital transformations to pivot to IDP. While most IDP initiatives start with an OCR (optical character recognition) approach, frontline capabilities are necessary for analyzing and understanding things like complex documents, images, and email trails. However, audio and video sources make it difficult to derive insights from meaningful and contextualized data. Sophisticated deep learning models help to decipher and extract data points that will complement already-generated insights.

Variations in input forms and source components demand model retraining that is followed by injection into the mainstream. All these activities have a tremendous storage/compute appetite, and they become ideal candidates for container-based, server-less deployments on the cloud, addressing economic and scalability dimensions.

Enterprises that are just beginning their IDP journeys are guided by business imperatives and ROI potential. They expect system integrators and vendors to help via a consultative, collaborative approach. As these companies move forward with IDP, the entire digitization value chain demands deliverables that, from the start, holistically encompass the user interface/experience, workflow orchestration, the biz rules arena, and the core AI/ML/OCR engine. With a solid realization of business benefits, these companies can progress to orbits that were initially out of reach.

Meanwhile, most of the customers who are already on the digitization journey expect IDP platform capabilities that are delivered through microservices-based architecture via RESTful APIs. The favored approach is the effortless integration of microservices into the existing digitization apparatus, representing seamless and instantaneous assimilation.

Use cases & applicability

Healthcare
Healthcare payers are adopting IDP solutions to streamline processes (enrollment, claims, billing, appeals/grievances) across the payer value chain. Some healthcare organizations have used OCR technologies to both improve claims intake with higher accuracy when moved from manual to IDP, and to increase worker productivity for data entry roles
Banking
Organizations using IDP for check processing have seen a 90-95% reduction in cycle time, increased customer delight, and better CX. Cases include loan processing signature validation; Know Your Customer (KYC); check leaf authentication for rebates; and the extraction of amount, date, and customer account information from scanned checks for a swift realization-classification-extraction of information.
Financial services
The demand for IDP adoption within financial services is driven by the need for processing a substantial chunk of financial statements to determine the credibility of business entities when doing business in any particular country. Financial spreading, an activity to extract relevant biz metrics from various forms of financial documents and assets for downstream processing, is dominant in this space. In the trade finance sector, the classification and extraction of information from documents (like a bill of lading, cover letters, invoices, or letters of credit) are critical for faster/efficient realization of purchased goods and payments.

Perspective for the next 3-5 years

Digitization is expected to play a pivotal role in the modernization of enterprises, with the lion's share of reports and other business work being products from legacy systems. Most require the integration into modern business applications built on SOA and microservices architecture, as applicable. This is where organizations may face challenges in the years to come, owing to a lack of a knowledge base due to SME turnover and retirement. Enterprises will look to circumvent these issues by digitizing information from legacy systems and extracting data from reports and dashboards. This way, downstream integration can be performed with relative ease. This is a massive opportunity for IDP in the legacy modernization space, and it will come to customers' rescue in the near future.

Next, organizations will look for vendors who can seamlessly provide the brainpower behind IDP without closely coupling with the user interface. In essence, IDP services must be utilized in a manner that can effortlessly combine with the customers' own digitization journey.

Consequently, with the growing number of use cases and the emergence of customer-specific contexts, IDP platforms should operate with greater flexibility, agility, and interoperability. As IDP platforms possess an innate capability to process structured, semi-structured and unstructured data, they will be anchored in the next few years as central repositories for organizations' broader content needs. Businesses will look at the IDP ecosystem as one of the crucial sources of data factories for its insight-seeking capabilities.

Conclusion

The last few years have seen tremendous growth in IDP capabilities; however, IDP should move beyond OCRs, as the industry is already overwhelmed with high heterogeneity at data origination points. This requires significant investment in research and development, which is vital for unstructured data crunching. As a complement to IDP, cognitive capabilities driven by ML, DL, NLP, computer vision, and RPA will be frontrunners in taking digitization to the next level. Evidently, the science behind such capabilities shall be much more powerful with regular manual oversight and timely intervention. Digitization can then move to the next orbit and maximize ROIs.

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