Articles

Extraction: The Next Step in Intelligent Document Processing  

By Luciano Lera Bossi, Alejandro Nava and Parsa Morsal  

intelligent document processing extraction

One of the starting points of digital transformation, especially for financial institutions, is intelligent document processing or IDP. We previously explored classification as the first step in IDP. In this article, we will explore the next crucial step, extraction.   

What is document extraction?  

Document data extraction is a process of extracting data from structured or unstructured documents and converting them into usable data. It is also called intelligent data capture. With the rapid progress in document imaging technology such as the incorporation of natural language processing (NLP) and optical character recognition (OCR) as well as advanced analytics, we can now enable IT systems to understand the data that was thus far only on paper.  

Powered by machine learning models and NLP, IDP systems can now bring the benefits of AI to document processing. The intelligence and detail offered by IDP systems today can be used for many functions including compliance and fraud. The level of granularity, accuracy, and speed offered by IDP systems today, can hugely impact the scale of digital transformation for your organization.  

Document processing and the banking industry  

The financial industry is no stranger to the benefits of IDP. In a recent study of 200 banks in the US, it was found that 66% of the respondents eliminated the need for manual processes for a typically labor-intensive industry thanks to IDP; and 87% cited accuracy and extraction as the key reasons for incorporating IDP into their systems. Given the emphasis on accuracy and extraction, it is important to understand how intelligent document processing coupled with OCR and NLP can give desired results.

OCR vs IDP

OCR as a document processing technology has been around the longest. OCR is used to extract handwritten or typed text in documents which can then be converted into data. While OCR has been synonymous with data extraction for many years, it is not without its challenges. Without intelligent processing of the data in understanding what the data is for, OCR may give inaccurate results. There can be errors in detecting a text block in an image (error in word detection), there may be errors in interpreting words correctly if there are differences in text alignment or spacing (error in word segmentation) or there may be errors in identifying a character bound in a character image (error in character recognition).   

However, when combined with intelligent processes such as NLP and machine learning analytics, time-intensive processing tasks can be sped up with minimal errors. The biggest differentiator between OCR and IDP is that IDP can also handle documents that may be structured, semi-structured, or unstructured.

Structured Documents  

Structured documents generally focus on collecting information in a precise format, guiding the person who is filling them with precise areas where each piece of data needs to be entered.  These come in a fixed form and are generally called forms. Examples of structured documents include tax forms and credit reports.   

Semi-structured Documents  

Semi-structured documents are documents that do not follow a strict format the way structured forms do and are not bound to specified data fields.  These don’t have a fixed form but follow a common enough format.  They may contain paragraphs as well. Example of semi-structured documents could be employment contracts or gift letters.  

Unstructured Documents  

Unstructured documents are documents in which the information isn’t organized according to a clear, structured model. These files are all easily comprehensible by human beings, yet much more difficult for a robot. Examples of unstructured documents include mortgage commitment statements and municipal tax forms.   

Textual and Visual data extraction in IDP  

The two main aspects that efficient IDP solutions tackle are textual data extraction and visual data extraction. In textual data extraction, the entity extraction technique is applied to recognize text in a document. This is a machine learning approach where the software is exposed to thousands of documents and the machine “learns” to identify information and segregate it based on certain semantic parameters. Entity extraction can involve a variety of tools and techniques including neural networks to visual layout understanding. By using entity extraction methods, you can avoid going down the template route and thereby use the software on various kinds of documents.   

In visual data extraction, IDP solutions can be designed to understand elements such as signatures, tables, checkboxes, logos, etc. Visual data extraction is more complex than textual data extraction as it involves detecting, analyzing, and extracting information from the regions with visual elements accurately while also denoising content that is not relevant. Using machine learning, advanced visual extraction models can also understand the structural relationship of the visual data and its relevance.   

Choosing the right IDP solution that can handle both text and visual elements accurately across varying document types will ensure that there is no need for your back office to comb through documents once again.   

Explore Kapti, our intelligent document processing software to find out how the power of machine learning and automated document workflows can transform your organization’s document processing experience.  

  

Articles

4 Ways to Avoid A Failed Automation Journey 

by Saurabh Bhatia 

4 Ways to Avoid A Failed Automation Journey

In recent years there has been a growing desire among financial institutions to automate processes as they move upstream to meet customer requirements. Before important decisions like loans and underwriting decisions are even made, financial institutions are deploying automation at the start of the process to provide a seamless digital-first experience.  

Research shows that automation is one of the fastest and most efficient ways for financial institutions to acquire, enhance and deliver information, reduce costs and save manual labor. Then why is it that most automation initiatives fail?  

4 Ways to Avoid A Failed Automation Journey 

A survey by EY states that 30-50% of initial RPA projects failed to realize their expected returns. The diagnosis suggests some challenges the enterprises typically face in their automation journey which potentially cause the RPA failure. Here we explore 4 ways in which enterprises can avoid automation journey failures: 

1. Give importance to change management and training 

2. Create automation champions within the organization 

3. Use the right tools to understand what needs to be automated 

4. Keep employee experience at the core of the automation vision 

Let’s dive in! 

Give importance to change management and training 

Automation cannot be successful without proper implementation of change management. Even if automation is a technical matter, it relies heavily on human relations. Intelligent Process Automation (IPA) is different than your typical IT project such as ERP/CRM etc. 

These projects require engagement from business users because, in essence, the role of the very same individual will evolve with the adoption of automation. Organizations that have been successful in their automation journey have put a real emphasis on the training and mobility of their teams when implementing automated processes. One of the most common examples of this is the changes in the target user interface. Any changes to the UI interface of an RPA application or system will most likely halt the automation which can put projects in a critical condition. Therefore, some organizations ask their automation core team to regularly check for changes in the ecosystem of the dependencies (in this case the UI interface) to avoid any failures.  

Create automation champions within the organization  

Automation is here to stay and is evolving rapidly. However, many organizations still see their automation initiatives as a project and not as a journey. Automation today has moved away from being just a technology project and is more about data transparency and technology capability. Most organizations don’t approach automation with the rigor it requires, assuming the business workforce need only attend a few training courses and that they can, without the support of IT, generate enough extensive automation to scale a program. 

IT is a critical partner throughout the transformation process. Their role is to ensure that the system is scalable, reliable, secure, and performs well. To make automation scalable, organizations should have a core team who are experts in the automation space. Successful organizations use such a core team to articulate the business value of automation well and get buy-in from key stakeholders within their organization. The core team articulates the need, advantages, and roll-out plan to each stakeholder before moving ahead with automation. 

Behaviours Driving Intelligent Automation Success & Failure

Use the right tools to understand what needs to be automated

Not all business processes are considered fit for automation. Choosing the wrong pilot process without understanding the needs can become one of the major reasons for failed automation initiatives. The success of any automation program strongly depends on a deep understanding of how processes will get handled on-ground.

Organizations are moving towards discovery tools such as process mining, task mining, and task capture which identify automation and improvement potential in end-to-end business processes and unleash the true value of automation.

You can use “The Three Rs of Automation Discovery”, to guide your approach towards successful discovery and automation implementation.

Keep employee experience at the core of the automation vision

In recent years we have realized that automation is not about replacing human beings but helping them be more efficient and using them for strategic work rather than manual. Automation projects require even more engagement from these individuals (users) to ensure the stability of the automation program.

It is important that organizations have full management support to communicate and reassure their teams that automation is more about reducing boring/repetitive tasks so that they can focus on more interesting and fulfilling work. Think about a contact center agent in a bank who struggles daily with clients because they had to go through 15 or 16 applications to do simple resolutions. They will be better off with an easier interface where they can focus more on the clients rather than swivel chair operations.  Automation can help these contact centers to reshape the experience not only of the customer but also of the team members, which will also lead to better talent retention rate.

The Bottomline

There is no question that Intelligent automation has demonstrated exceptional results, from predicting behavior to streamlining operations. But its implementation needs to be thoughtful, effective, and pragmatic to ensure its long-term success. There are other factors that can cause a project to fail including the lack of testing and not following the best development practices. However, these 4 reasons are primary and should be taken into consideration before starting your automation journey.

Blanc Labs has proven experience working with organizations to identify and implement intelligent automation solutions. We take a holistic approach, helping organizations build the necessary foundation and setting them up for long-term success in a hyper-automation environment.

Book a demo or discovery session with Blanc Labs to discover the impact of our Intelligent Automation solution.

Articles

Customer Centricity As The Essence Of Digital Transformation

by Abhijit Chakravarty

Digital Transformation may well be one of the most overused terms among the C-Suite, business consultants, and the industry overall. It is an axiom that increasingly falls in the category of being painfully cliché, but the fact of the matter is that the term isn’t about digital technology. It’s less to do with technology and more about organizational transformation and change management with human capital at its core.

The fourth industrial revolution is all things Meta with its blurring lines between the real and digital worlds. Characterized by augmented reality (AR) and virtual reality (VR), it has created a fundamental shift in the way we will conduct business. The power now is truly in the hands of the new-age customer, who decides when, where, and how they will transact.

While the “customer is king” moniker has always been around, thanks to new research, we are now able to see exactly which factors in a digital transformation correlate with being a financially successful company, and why indeed, companies should care about digital transformation.

Source: Harvard Business Review

A recent study by Deloitte found that more digitally mature companies see higher gains in customer satisfaction, gross margin as well as long-term gains. With customers at the center of the digital transformation, more digital maturity can lead to better net revenue and net profit margin, as seen in the graphic below:

Why is this? The answer is that digitally mature companies can add value for their customers. Deloitte identified seven digital pivots, listed below, which successful organizations made, with customers at their center. As a result, they were more agile when it came to responding to customer demands (products and services), improving customer relations (being culturally relevant and bringing diversity and inclusion), or enhancing customer experience (seamless transition between mobile and in-person experience and 24/7 support). These successful enterprises were also able to adapt their business model and boost innovation.

Pivot 1: Flexible, secure infrastructure

Pivot 2: Data Mastery

Pivot 3: Digitally savvy, open talent networks

Pivot 4: Ecosystem engagement

Pivot 5: Intelligent workflows

Pivot 6: Unified Customer Experience

Pivot 7: Business model adaptability

The business and financial impact of a digital transformation centered around the customer is not lost on the C-suite either. Companies with higher digital maturity see benefits across cost reduction, increased sales, and better customer lifetime value. According to this report by KPMG, as many as 67% of CEOs agree that the agility provided by digital transformation is “the new currency of business; if we’re too slow, we will be bankrupt.”

Digital transformation’s real purpose is about empowering the server (business) and the served (customer). In coming years, we will see businesses re-imagining, growing, and transforming themselves by placing the digitally empowered demi-gods, a.k.a the customer, at the epicenter. The alternative, unfortunately, is worse.

At Blanc Labs, we understand that every organization’s needs are different. This is why we offer advisory and consulting services to understand your unique issues and get you started with your digital transformation journey. Book a discovery call with us to learn more.

Articles

Challenges in Digital Lending

Digital lending has evolved over the last ten years and the pandemic has only exacerbated the need for intuitive, enjoyable, dynamic, and accessible lending systems for both lenders and borrowers. In the age of Apple and Amazon, borrowers demand a seamless experience that does not involve speaking to a human being or filling out paper forms.

While traditional banks and monoline lenders are overhauling their systems to address these challenges, FinTech companies are making use of the gaps left by the big banks.

Challenges in Digital Lending

The Era of Digital Lending

According to the Canada Banker’s Association, 49% of Canadians do their banking digitally, but more importantly, 75% of Canadians intend to maintain the digital banking habits they picked up during the pandemic. Compared to five years ago, FinTech companies in the US today account for more than 38% of the personal loan space. Traditional banks, on the other hand, saw a loss of 12% of the personal loan space during the same period.

Why is this? The reason is a lack of simplicity, speed, and accessibility.

Leading FinTechs today can provide multiple quotes within minutes and fund loans within a matter of day. Apart from speed, online lenders today offer a seamless, fully digital experience as well as advanced features including security and risk assessment that does not involve a long-drawn-out credit check process.

U.S. Digital Lending Platform Market Size

Digital Lending Challenges Faced by Lenders Today

Creating a fully automated, digital-first lending solution can be a complex process, especially if you don’t have the right tools and automation in place.

Automated self-service, compliance, fraud and cyber-security, document processing are just some of the many challenges that traditional financial institutions must overcome to match the experience offered by FinTechs today.

Here is our point of view on some of these key challenges:

1. Complicated and Slow Loan Origination Process

Can you think of living in a time before same-day delivery or tap payments? Neither can borrowers.

Companies that don’t offer instantaneous loan decisions run the risk of losing their customer to a FinTech that is faster and can provide quick decisions. Relying on antiquated loan origination processes that require filling multiple paper forms, visiting the bank in person, and taking days to evaluate risks and make funding decisions, will spell trouble. One study shows that 42% of respondents abandoned their applications because the process was too long and complicated, and 62% said they were unsatisfied with the digital experience, due to “too many touchpoints” and “the necessity of going to a physical location.”

Nimble FinTechs on the other hand are assessing credit risk and offering funds at the speed of light. One study by Smarter Loans found that 53% of respondents received their funds a mere 24 hours after applying for it, “suggesting that same-day-funding is becoming a standard in the industry.”

Thankfully, banks can implement digital native loan origination platforms that can automate the end to end loan origination process;  or they can address bottlenecks in it like underwriting, which will make the process more streamlined for both borrowers and lenders.

2. Partial measures instead of end-to-end solutions

Delivering an end-to-end solution is a mammoth task that needs significant resources and time.   The world of lending is full of large projects that took twelve to eighteen months to deliver value and this is not going to disrupt the FinTech community. Success for this is now measured in mere months.  Moreover, automating one part of the customer journey and ignoring the rest can cause more complications in the long run and can re-introduce manual intervention. Instead of a piecemeal approach, consider a unified lending solution with a modular structure that addresses all steps from information collection to underwriting, servicing, and reporting.  It is important to get the end state vision right first and then you can make incremental changes building towards your best customer journey if you elect to do things in recommended phases.

3. Document Intake and Data Storage

While traditional banks and brokers have been busy processing loans through paper-based or hardwired systems, FinTech companies are using automated processes to process loans faster and more transparently. Intelligent Document Processing (IDP) can automate the document intake process and apply AI (artificial intelligence) plus ML (machine learning) to extract data with more accuracy while converting unstructured data into structured data, making the data more useable. This can save you money and reduce human data entry error.  In most cases, this data can be prepopulated into origination and adjudication engines to drive faster straight-through processing and time to decisioning the loan.

Also visit: www.kapti.io

Productivity loss due to manual document management

4. Data Silos and Lack of Personalisation

During a digital transformation project, it is important to design the system in a way that the multiple components within that system can speak to each other and convey relevant data. If for some reason this does not happen, then it simply consumes more time—time that can be used for strategizing and planning. With so many systems in  silos, banks may not get a true 360-degree view of their business with a customer, therefore making it difficult to create personalized offers and recommendations for that customer. Ideally, banks should have a unified, transparent view of deposits, loans, and personal accounts if they want to keep the customer engaged and cross-sell new lending products over time to grow their share-of-wallet.

5. Regulations

Borrowers that come through the digital lending channel hand over a lot of sensitive information and so it makes good sense that lenders hardwire a regulatory compliance framework into their platform. Luckily, there are experts that can help business leaders stay on top of banking regulations and data privacy laws. The regulatory environment is rapidly changing and new industry driven changes around Open Banking are emerging as well. These dynamics are going to redefine and further embolden  FinTechs, drawing clear lines around how the participants in the banking ecosystem will work together.  Full assurance on regulatory reporting and compliance with industry standards is table stakes in any solution that meets this demand.

A Unified Lending Solution for Lenders

Blanc Labs provides a three-stage approach for digital lending which includes:

– Consulting assessment of your needs

– Streamlining processes and platforms

– Intelligent documentation processing (IDP)

Book a demo or discovery session with Blanc Labs to discover the impact of our digital lending solutions.

Webinars

Delivering a world-class teledermatology solution in partnership with MedX and SmileCDR

In 2021, MedX Health Corp (MedX) engaged Blanc Labs to upgrade the SIAscope from PC-based hardware to a browser-based, multilingual interface that could also host patient data in a secure cloud. In collaboration with MedX stakeholders and SmileCDR, Blanc Labs’ multi-disciplinary team of designers, engineers, and product specialists created a solution within just twelve months.

In this webinar, Blanc Labs CIO, Dariush Zomorrodi, speaks about Blanc Labs’ partnership with MedX and Smile CDR in delivering an efficient and effective teledermatology solution to a global market.

Interested in hearing how we can accelerate your digital transformation?