Introduction:
Digitally transforming an enterprise is not a solo play of extensive use of software to conduct
previously manual business activities. It signifies the percolation of the use of technology to
inspire an electronic way of doing clerical tasks, which indeed facilitates the following:
• Reduction in processing time and effort
• Elimination of error-prone situations
• Optimization and elimination of costly repetitive work
• Acceleration of mundane business activities causing extensive delays
• Elevation in productivity across the various segments of business activities
Digitization is a critical faculty to empower technology to transform enterprises. Data being
the enterprise's epicenter, business process orchestrations immensely depend on the availability
of the right data at the right time to stimulate "data-driven insights."
Documents are still the primary source of data for many businesses. An extensive set of
documents must be read, comprehended, and understood to ensure business processes yield
desired outcomes. They are an indispensable part of the digital ecosystem as they contain
critical information to augment mainstream business processes. There are pockets of human
actions across customers, spanning industry verticals, where huge money is spent to sieve
assets (across paper, signatures, handwritten documents, PDF, Emails, faxes, images, drawings,
graphs, reports, voice, and video) to derive meaningful data to complement business activities
and functions manually.
Thus, Intelligent Document Processing (IDP) techniques come as a boon to unleash modern
methods of enabling data processing by leveraging cutting-edge technologies. These
techniques help business processes by addressing DATA DEFICIENT expanses by
augmenting BIZ KPIs with additional reinforcements. In other contexts, they help generate
brand-new business insights vital for Razor Sharp decision-making.
What is IDP?
Intelligent Document Processing (IDP) is a capability that captures meaningful data from
documents (physical documents, digital documents, images, email, PDF, word docs, audio, and
video). It extracts and segregates applicable data for further processing using cognitive and AI
instruments like Optical Character Recognition (OCR), Natural Language Processing (NLP),
deep/machine learning, etc. to address:
• Business process challenges, partly or in their entirety
• Business outcomes with workflow automation solutions
• Business technology amalgamation in the workflow to interface effectively
Industry trend
Research shows the IDP market trend is in the lower single-digit billions, accelerating at a
healthy CAGR, YoY. An indication that the industry is at a nascent stage of embarking on the
IDP journey as part of more significant digitization initiatives – leading to modernization. Most
customers are in the experimentation mode, testing waters, yet with overwhelming confidence
in ROI potential. While most IDP initiatives start with an OCR (Optical Character Recognition)
based approach, front-line capabilities are needed to analyze and understand complex
documents, images, email trails, etc. Audio and Video source forms present additional
challenges in interpreting and deriving meaningful and contextual data. They require
sophisticated deep learning models to decipher and extract data points to complement
generated insights.
Generally, processing heterogeneous data from structured, semi-structured, and unstructured
formats poses enormous challenges for ML, DL, and NLP techniques. There are distinct
activities such as preparatory pre-processing steps, learning-based model training, and post-
processing activities like data quality, data mapping, formatting, interpretation of extracted
data, business rules application and transformation, etc. A variance in input forms and source
components demands model retraining and consequent injection into the mainstream. All these
activities have tremendous storage or compute appetite and hence become ideal candidates for
container-based server-less deployments on the cloud to address economy and scalability
dimensions.
Enterprises early in their IDP pursuits are obligated to be guided by business imperatives and
ROI potential. They expect SI's (system integrators) and vendors to help with a consultative
approach in a collaborative manner. As they embark on the IDP journey, the entire digitization
value chain demands deliverables holistically encompassing user interface/experience,
workflow orchestration, biz rules arena, and core AIML/OCR engine to start with. With a solid
realization of business benefits, subsequent orbits can be reached progressively as each orbit
tastes success.
Meanwhile, customers already in the digitization journey expect IDP platform capabilities
delivered through microservices-based architecture through RESTFUL APIs. Fluent
integration of microservices into their existing digitization apparatus for seamless and
instantaneous assimilation is the favored approach.
Use cases & Applicability
Industries like insurance, banking, finance, retail, travel, and hospitality are hyperactive in
leveraging IDP to deliver significant business value. For instance, healthcare payers are
adopting IDP solutions to streamline processes across the payer value chain (enrolment, claims,
billing, appeals, grievances, etc.). Some healthcare organizations have used OCR technologies
to:
• Improve claims intake with higher accuracy when moved from manual to IDP
• Increase worker productivity considerably for data entry roles
Banking organizations using IDP for check processing have seen an approximate 90-95%
reduction in cycle time. Some banking use cases include loan processing signature validation,
KYC, cheque leaf authentication for rebates, and extracting amount, date, and customer
account information from scanned cheque documents for swift realization, classification, and
extraction of information. All these use cases converge on quicker turnaround, increased
customer delight, experience, and risk aversion scenarios which are high focus areas for
bankers.
In financial services, regulatory needs are evolving into prominent consumers of IDP solutions
as there is a need for processing a substantial chunk of financial statements to ascertain the
credibility of business entities in doing business in a country. Financial spreading, as the use
case is called, is dominant in this space. In the trade finance sector, classification and extraction
of information from documents (e.g., Bill of lading, cover letters, invoices, letters of credit) are
critical for faster and efficient realization of goods purchased and payments.
Consumers of IDP's output could be analytical visualizations, cognitive learning models, or
other downstream applications where extracted data are projected to augment existing insights.
The true strength of IDP's capability emerges when it is bludgeoned with workflow
orchestration and other hyper-automation capabilities.
Next 3-5 years' perspective
Digitization will play a pivotal role in the modernization of enterprises, who have lion's share
of reports and business work products in their legacy systems. Most require seamless
handshake and integration into modern business applications built on SOA and microservices
architecture, as applicable. This is where organizations could face challenges in the coming
years, owing to a lack of knowledge base due to SME roll-over and retirement factors.
Enterprises will look to circumvent those issues by digitizing the legacy system's information
delivery layer by extracting data from reports and dashboards so that downstream integration
can be done with relative ease. This is a massive opportunity in the legacy modernization space
for IDP to come to the customers' rescue in the future.
Next, organizations will look for vendors who can seamlessly provide the brain behind IDP
without close coupling with the user interface. i.e., IDP services must be exposed in a manner
that can effortlessly intertwine with the customers' digitization journey.
Consequently, with the growing number of use cases and the emergence of customer-specific
contexts, IDP platforms ought to operate with greater flexibility, agility, and interoperability.
In addition, the platform is expected to work "faceless" and must readily align with customers'
ongoing digitization channels and their supporting technical landscape. It implies that the IDP
SERVICE tenants will have to operate at atomic levels so that the consumption side can
assimilate the services depending on customer specifics. As the pace picks up, enterprises will
look at IDP-obsessed DIGITIZATION channels as one of the mainstream source systems to
provide depth to "insights generation." This genre is comparable to how the "data analytics
theme," decades ago, looked at various ERPs and transaction/native systems (OLTP) for
congregating and warehousing enterprise data. As IDP platforms possess an innate capability
to process structured, semi-structured and unstructured data, over the next 3-5 years, they will
be anchoring as a central repository for organizations' broader content requirements.
Businesses will look at the IDP ecosystem as one of the crucial sources of data factory for their
INSIGHTs-SEEKING endeavors.
Conclusion
The last 6 to 7 years have seen tremendous growth in the IDP capabilities; however, they should
leapfrog beyond OCRs, as the industry is already overwhelmed with high heterogeneity at the
data origination points. This requires significant investment in R&D, which is vital for
unstructured data crunching. As complementary to IDP, cognitive capabilities driven by ML,
DL, NLP, computer vision, and RPA will be front runners in taking digitization to the next
level. Nevertheless, the science behind such capabilities must be grounded and humbled by
regular human intervention to ensure such sophisticated automation becomes effective and
fruitful. The application of such cutting-edge capabilities has to be devised carefully to
maximize ROIs.
The beauty of digitization is that it starts with COGNITIVE capabilities to engineer data
sources. These sources are taken through various stages to feed into the very capability to emit
wonderful insights, thus pioneering a true end-to-end story.
In conclusion, IDP solutions come as a blessing, as they address the human fatigue linked to
pruning/filtering a large pile of "data rich matter" which was once meant to go to e-wasteyard
