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The Complete Guide to Intelligent Document Processing

Complete guide to IDP_Blanc Labs
Illustration by Storyset

Intelligent document processing (IDP) helps companies manage documents more efficiently and digitizes unstructured data from multiple sources. 

IDP is part of modern digital transformation, which is changing how businesses operate. Artificial intelligence (AI) is one of the key drivers of digital transformation. AI makes business processes more efficient, reduces costs, and improves customer experiences. According to an IBM study conducted in 2022, AI helped: 

  • 54% of businesses reduce costs with efficiency 
  • 53% of businesses improve their IT or network performance 
  • 48% of businesses improve customer experience 

 

IDP offers similar benefits. It encompasses multiple technologies, including AI, machine learning (ML), natural language processing (NLP), robotic process automation (RPA), and optical character recognition (OCR) to automate your document processing workflow. 

That’s barely scratching the surface of what IDP has to offer a modern business. This guide explains the meaning of IDP and how modern businesses can use it to their advantage. 

What is Intelligent Document Processing? 

Intelligent document processing is a technology that allows businesses to digitize unstructured data from multiple sources of documents. For example, your business may need to manage unstructured data from online survey forms, word files, PDFs, and similar document types. 

Imagine manually scanning through each of these documents to extract information, convert them into digital documents, and organize the data. You’ll end up wasting resources and time on a mundane task. 

Fortunately, IDP can automate the entire process. Automating the documentation workflow enables you to free up your team’s time for more value-adding tasks. Moreover, you also spend less on handling and routing these documents and errors your team might commit during the process. 

Here are five technologies IDP uses to automate your documentation workflow: 

Robotic Process Automation (RPA) 

RPA is integral to IDP — it’s often even confused with IDP, but they’re technically different. RPA is a technology used in building software robots that automate tasks that otherwise require human effort. 

For example, RPA can help populate data from a document into your ERP without involving any humans. This translates to greater efficiency because RPA is faster and doesn’t need coffee breaks. 

However, IDP goes a step further. IDP combines the power of RPA and AI. RPA is a rules-based technology that can’t make data-driven decisions. On the other hand, AI can perform more complex tasks.

Artificial Intelligence (AI) 

IDP uses AI technologies like machine learning and natural language processing for data extraction, document classification, and claims processing. For example, AI reads and labels information on documents and can accurately route documents without requiring manual effort. 

If you’re in banking, AI can help automatically classify mortgage documents, tax records, and pay stubs. IDP can also automate claims processing using AI. For example, it can find the relevant customer for a specific claim and then route it to the appropriate department. 

Machine Learning (ML) 

ML is a branch of AI that allows an algorithm to learn as it processes more data. Over time, ML helps IDP extract data from documents more accurately. 

For example, the ML algorithm uses pre-processed documentation to collect information, including names, amounts, and dates. It stores the data and further analyzes it. This way, it can process future documents more accurately. 

Natural Language Processing (NLP) 

NLP is a branch of AI that allows computers to understand text and speech, just like humans. IDP relies on NLP to understand data faster. It does so using sentiment analysis and tags language elements like named entities to derive context. 

As you can imagine, NLP plays a critical role in understanding the contents of a document and data extraction. 

Optical Character Recognition (OCR) 

OCR is one of the key technologies used for processing handwritten or scanned documents. With OCR, IDP can copy text from a document image — text that a computer can’t directly copy into its system. OCR converts the information in the document image into editable text, which allows for processing and storing this information. 

IDP Vs. OCR 

Traditional OCR has limited capabilities. For example, it can only read templatized documents formatted using specific rules. OCR is also limited to just extracting text and can’t derive any context by itself, which means it can’t make any decisions for you. 

As a result, OCR fails to process unstructured or handwritten documents, rendering it less valuable for modern businesses and limiting scalability. 

On the other hand, IDP combines OCR with other AI-based technologies and RPA for extraction, context, and execution. 

A traditional bank check is a classic use case of IDP. Suppose you’re a multinational financial organization that processes hundreds of checks daily. You receive checks from different banks that use a different format. Each issuer has different handwriting. No two checks look the same, so OCR can’t process these checks accurately. 

However, IDP can process these checks far more accurately. IDP uses OCR to convert handwritten and scanned data into text. Then, it uses NLP to derive context about the text extracted from the scanned check. The RPA takes over and executes an action based on a preconfigured set of rules. Over time, the ML algorithm gets better at processing checks. 

Read more: IDP vs RPA vs OCR

How Does IDP Work? 

IDP uses a five-step process for document processing:    

  1. Document pre-processing
  2. Data Capture 
  3. Document Classification 
  4. Document Extraction 
  5. Document Verification 
  6. Integration 

Document Pre-processing

Before we begin processing documents, they must be cleaned up for ‘noise’ so that they become machine readable. The quality of pre-processing often determines the accuracy of the final result. Reducing noise may include splitting sentences into words, lower-casing words (e.g., the word Bank and bank mean the same but are represented as two separate words in certain document processing models), removing stop words like ‘a’, ‘an’, ‘the’, etc. It may also involve improving image quality for better readability.

Data Capture 

Data capture (or ingestion) is the first step, where you input the document into the process. OCR and the ML algorithm are key technologies used for data capture in IDP. 

OCR is available on many commonly used tools like Microsoft Office. This means you don’t necessarily need an IDP system to use OCR, but you do need OCR to use IDP. OCR captures data from the document, whether it’s an image or digital document, and sends it to the IDP system for extraction. 

Document Classification 

We briefly discussed NLP in the previous section on data extraction. However, NLP has an even bigger role to play when classifying documents. 

IDP systems use NLP, OCR, and long-term short memory (LTSM) to analyze and classify data. NLP and transformer models (first described in a 2017 paper from Google) establish a relationship between words in a sentence and assign weightage to each word to interpret the meaning. 

Practically, IDP systems classify documents and extract data simultaneously. The system typically takes less than a few seconds to classify documents and extract data. 

Document Classification _Intelligent Document Processing_Blanc Labs
Document Classification

Document Extraction 

Document data extraction involves converting the captured data into usable data. IDP uses NLP and ML models to understand data and derive context. 

  • Structured: Data stored in an Excel sheet is a great example of structured data. It’s a data set where the system doesn’t need additional context to interpret the data. 
  • Semi-structured: Semi-structured data is where part of the data is structured. Examples include invoices, annual reports, and contracts. 
  • Unstructured: Unorganized data doesn’t follow any specific format and is often received in multiple types, including images. This type of data is the most difficult to process automatically. However, 80% to 90% of data organizations collect is unstructured, making it mission-critical for you to have the tools that allow processing unstructured data. 

 

Structured data is easy to interpret, while interpreting unstructured data requires additional technologies like NLP. 

The extraction process involves two aspects:  

Textual data extraction: Textual data extraction involves identifying text in a particular document. The IDP system uses ML to identify and tabulate the text based on specific semantic parameters. The best IDP systems use entity extraction techniques like convolutional neural networks (CNNs), that allow the IDP system to extract data from documents that don’t follow any specific format. 

Visual data extraction: This is more complex because it involves understanding elements like signatures and logos. The IDP system must detect, understand, and extract information from visual elements while using ML to understand the element’s structural relationship and relevance. 

The best IDP systems offer accurate textual and visual data extraction. You can use them to extract data from multiple document types with great accuracy. 

Intelligent document processing_structured vs unstructured data_Blanc Labs

Document Verification 

IDP verifies and validates document data for accuracy. It ensures that it’s extracting the right data from the document and that the extracted data is accurate. 

KYC verification is an example of document verification. When customers provide an ID and complete the KYC form, you’ll need to verify these details against a database. However, you can eliminate manual effort and validate KYC data automatically using IDP. 

Automated validation is especially helpful when you’re processing documents at scale. For example, a receipt might be mixed up with one of your invoice batches. The IDP system needs to be able to differentiate and disregard this document through validation. 

Validating borrowers by approved vendors is an excellent example of data validation. You can use the IDP system to identify borrowers who have availed loans from an approved lender. You can automatically mark such borrowers during the extraction process without any manual effort.

Integration 

Once the IDP system completes processing the data, it will create a JSON or XML output file containing the compiled data. 

You can also use APIs to migrate this information to a data repository or third-party tools like enterprise resource planning (ERP) or customer relationship management (CRM) systems. If your IDP system doesn’t integrate with your business solutions, we can help you integrate any API-enabled application with your IDP. 

Benefits of Intelligent Document Processing 

Using IDP offers monetary as well as non-monetary benefits 

Minimizes Human Error 

Manually scanning documents and migrating data is prone to human error, especially when processing a high volume of documents. Errors can be expensive—you might upset your customers, disrupt your workflow, or become non-compliant. 

IDP helps nearly eliminate the risk of human error from your processes. As long as the data on the physical documents is accurate, the system will make sure everything that goes into your systems via the IDP is accurate. 

Better Employee Experience 

Automating document processing saves time and effort so your team can focus on more productive tasks. 

Our partner, UiPath, surveyed 4,500 office workers worldwide and found 43% of employees believe automation allows them greater opportunities to focus on more important work. 

The same UiPath survey also reveals that 52% of employees believe automation helped them achieve a better work-life balance. 

Lower Compliance Risk 

IDP helps streamline compliance processes. An IDP system automatically extracts relevant information from documents and classifies them based on predefined criteria, which means fewer errors and easily accessible records you might need for compliance. 

You can configure the IDP system to compile data on a searchable database, which helps simplify audits by making information readily available. The best IDP systems can also detect sensitive information and determine how to treat it based on sensitivity. 

Improves Customer Experience 

Fewer errors, faster turnarounds, and frictionless onboarding can greatly enhance customer experience. 

Automated document processing allows you to serve your customer better in almost all client-facing functions. For example, if a customer submitted KYC forms last week and calls support to ask if KYC verification is complete, you’ll need to sift through a pile of paperwork to provide an answer. 

On the other hand, if you use an IDP system, you can search the database and answer them faster. Customers don’t like being on hold—and when you use an IDP system, they won’t have to. 

Scale Document Processing 

As your business grows, you’ll need to process more documents. Manually processing documents can be resource-intensive. 

Your team will spend a ton of time scanning documents and extracting and transferring data to your internal systems. You’ll need to keep adding more people to the team, which means you’ll essentially be investing money in mundane tasks. 

Automating mundane tasks allows your team to focus on parts of the business that require a human touch. For example, a sales rep can work on selling—the task you hired them for—instead of collecting KYC forms. 

Improves Data Usability 

A large portion of your business’s data is unstructured. Similarly, a good volume of business data is locked behind PDF files, emails, and scanned copies of documents. IDP systems help structure this data, making it usable. 

This means data previously lying dormant can help you make more insightful decisions once you start using an IDP tool. Digital documents are a critical source of information, provided you handle them correctly. As a McKinsey article explains: 

“Incoming mail and other physical documents are an important source of data, but not the only one—many documents that arrive digitally can pose significant challenges if not handled correctly. Emails, for example, may require significant effort to become structured, digital data that can be processed automatically.” 

Top 6 Use Cases for Document Processing 

IDP has many applications in a modern business’s workflow. Most businesses are looking to use automation to improve efficiency and reduce costs, and that’s where IDP can help. 

Estimates on the cost of processing an invoice vary, but it can be as high as $15 to $40 in some cases. The reasons for high costs include fat finger errors, mail costs, and labor, among other things. 

Instead, you can use IDP to process invoices and other documents at scale and at a much lower cost. Here’s a closer look:  

KYC 

If you’re a financial organization, you know how automating your KYC verification process can free up a lot of time and resources. Why make your team work on mundane tasks like KYC verification even though performing them manually can result in a human error? 

You can use IDP to process KYC documents, verify the customer’s identity, and automatically migrate their data to another platform. This ensures your KYC workflow is free from human error and reduces your cost of compliance. According to a McKinsey survey, automated KYC can also improve customer experience by 18%. 

Customer Onboarding

70% of onboarding projects aren’t completed on time. Translation? Cost overruns and unhappy customers. 

Customer onboarding is critical because it sets the tone for your relationship with the customer, and IDP can help streamline a part of the onboarding process. 

You might have to handle multiple types of documents when onboarding a customer, including credit reports and tax returns. You might be able to automate document handling with RPA, but the automation workflow will stop working as soon as you change the format or document type. 

You’ll need AI to handle these changes, and that’s where IDP can help. IDP systems are more robust in handling various document formats and types than RPA, thanks to NLP and ML. Using IDP also helps reduce onboarding costs, but you won’t need to tie up human resources in manual document processing. 

Mortgage Underwriting 

A spike in mortgage demand can overwhelm your team and workflow. In fact, a J.D. Power study revealed that customer satisfaction dropped five points on a 1,000-point scale in 2021 because of a major spike in mortgage origination volume. 

Managing better demand requires streamlining the entire mortgage process, from application to approval. Underwriting is one of the most critical parts, where your team needs to scan through various documents and pull relevant data needed to approve or reject an application. 

A single team member can only process so many applications a day. To scale the underwriting process, you need IDP. An IDP tool extracts applicant data and sends it over to the credit team or your credit evaluation system, streamlining the underwriting process. 

Digital Archiving 

Archiving involves storing data digitally to protect it against data loss and other disasters. Creating a digital archive is critical for modern businesses that rely on data to make data-driven decisions. 

IDP helps archive documents such as financial statements, tax records, survey results, customer data, and more for future use. You should also ensure the archived data and documents are safe from anything that can potentially cause data loss. 

Data Entry 

Data entry is one of the most commonly automated tasks. Automating data entry is easy when you’re receiving structured data from a digital platform. However, entering data from physical documents into a digital tool isn’t all that easy with traditional automation solutions. 

IDP uses OCR and AI to scan physical documents, extract information, and migrate the information to an output file or another system. For example, you can scan invoices and then update the inventory data in your ERP in real time using IDP. 

Intelligent Document Processing with Blanc Labs 

If you need to implement a comprehensive, intelligent document processing system, we can help. Blanc Labs works with financial organizations like banks and credit unions to automate their workflow. We can help you build a customized automation system based on your specific needs and internal workflows to make your document processing seamless. 

Articles

IDP vs. RPA vs. OCR

IDP vs RPA Blanc Labs
Illustration by Storyset

Financial institutions deal with a lot of data from various sources like forms, emails, invoices, PDFs, etc. daily. Processing this unstructured data manually is time-consuming and requires a lot of effort. It can also be prone to errors, which can have costly consequences. This can take away from the time and energy that employees could be spending on more strategic tasks. 

Automation technologies like IDP (Intelligent Document Processing), RPA (Robotic Process Automation), and OCR (Optical Character Recognition) can take manual document processing off your hands.   

If you are wondering which one you should choose among IDP vs. RPA vs. OCR for the digital transformation of your financial institution, you are in the right place. Here we break down each banking automation technology to help you choose the best one.    

What is RPA? 

RPA is a technology that allows organizations to automate repetitive, routine tasks typically performed by humans. These tasks include data entry, document processing, customer service interactions, and back-office functions such as compliance, risk management, and accounting. 

What is possible with RPA, and where does it fall short? 

Banks are known to be heavily regulated, and compliance is a critical part of banking operations. This is where RPA can play a significant role by automating compliance-related tasks, such as KYC (Know Your Customer), AML (Anti Money Laundering), and other regulatory data management. It can also automate data migration, trade execution, data validation, data updates, and perform simple copy/paste functions.  

However, RPA in banking automation also has some limitations. RPA requires a developer or GUI window to operate. Thus, RPA can only be used to automate simple screen-related tasks. It is limited to automating tasks that are highly structured and rule-based and is not suitable for tasks that require human judgment or decision-making.  

Also, the entire automation process can break if there is an update in the user interface of a linked software. It is an outdated technology that relies on OCR and is not built for modern end-to-end integration.   

What is Intelligent Document Processing (IDP)? 

IDP is a next-generation technology designed to tackle the limitations of RPA. It is a system created to process documents just like humans. If you compare IDP with RPA for banking automation, you will find that IDP is the ideal combination of OCR, Artificial Intelligence (AI), machine learning, and natural language processing.  

IDP is independent of strict rule-based approaches. Due to its flexibility, it can reach and process unstructured data not reachable via RPA. IDP tools also reduce the margin for errors by validating the data and informing the team in cases that need human intervention. 

Here are some reasons why banks need Intelligent Document Processing.

How can IDP help in banking automation? 

IDP can be a valuable tool in banking automation in several ways: 

Account Opening 

You can use IDP to automate extracting information from account opening forms and other related documents. It can reduce the effort and time required for manual data entry and improve the accuracy of the data. 

Document Management 

IDP can index, classify and route documents to the appropriate systems or individuals. Thanks to intelligent document processing, financial institutions can manage documents effectively and quickly retrieve the necessary information. 

Compliance 

IDP can help automate extracting information from compliance-related documents for processes such as KYC, AML, and other regulatory requirements. This way, banks can comply with regulations more quickly and efficiently while reducing the risk of non-compliance. 

Loan Processing 

IDP can enable the extraction of information from loan applications and other related documents such as income statements, credit reports, and real estate appraisals. It can help automate the loan review process, making it faster and more accurate. 

Fraud Detection 

IDP can be used to extract information from documents and match it with other sources to detect potential fraud. In this way, banks can reduce the risk of fraud and losses. 

Read more: The Top Use Cases for Banking Automation

IDP vs RPA 

The choice of automation for document processing boils down to IDP vs. RPA. RPA and IDP are two different technologies used in automation but are sometimes confused with the other.   

The main difference between RPA and IDP is that RPA does not have the native intelligence of AI. RPA cannot consume and analyze data on its own. RPA is limited to mimicking repetitive actions performed on computer screens with a mouse and a keyboard. It is helpful for tasks that don’t require high-level decision-making and is largely outdated. It is often said that AI is ‘the brain’ while and RPA is ‘the hands.’ 

On the other hand, IDP takes automation up a notch by automating documents and absorbing and understanding data to extract actionable insights. Thus, IDP can be considered the future of banking automation. 

RPA works better with structured documents (e.g. claim forms, tax forms ) where IDP works better with unstructured documents (e.g. contracts, handwritten notes). Ideally, an organization should use a combination of RPA and IDP to achieve better operational efficiency. 

What is OCR? 

OCR is a technology used in both RPA and IDP, that reads, extracts, and converts data from images and scanned documents into text for electronic automation and importation. When integrated with automation solutions like IDP or RPA, OCR can efficiently process structured data, eliminating the need for manual data entry and thus minimizing errors. OCR technology also enhances image quality to produce more accurate results.  

While OCR is a step towards automation, it is not very effective in processing unstructured data that most banking institutions deal with every day in large volumes.   

IDP vs RPA vs OCR

IDP vs RPA_Blanc Labs

Blanc Labs’ Document Processing Solutions for Banks 

Blanc Labs’ helps financial institutions like banks, and credit unions fast-track their way to digital transformation. We can help you integrate powerful automation technologies into your processes to increase productivity and reduce manual labor and the scope for errors.   

Our team provides a customized combination of machine learning and artificial intelligence for automating complicated tasks like document processing so that you can save your resources and provide faster and better financial services. 

If your financial institution deals with a ton of documents every day, let us help you put your processing on auto. Book a discovery call with us today, and we will create a seamless document processing solution unique to your needs. 

 

Articles

Benefits of Intelligent Document Processing

Benefits of Intelligent Document Processing_Blanc Labs
Illustration by Storypik

If you are reading this, chances are that you are exploring intelligent document processing (IDP) systems for your business. 

Many businesses are curious about this document automation process because, just like you, they may have heard about how it simplifies complex document layouts, captures data and organizes it for a seamless workflow. 

In this article, we will explore:  

  • What Is Intelligent Document Processing? 
  • How Intelligent Document Processing works 
  • Benefits of Intelligent Document Processing 

What Is Intelligent Document Processing?

Intelligent document processing uses Artificial Intelligence (AI), Machine Learning (ML), Optical Character Recognition (OCR), computer vision, and Intelligent Character Recognition (ICR) to automate data extraction from complex semi-structured and unstructured documents. This technology helps you categorize the extract data into a meaningful format that is easier for people or a system to comprehend. A popular use case for intelligent document processing is mortgage origination and decisioning, where applications can run into hundreds of pages.  

Read more on how Intelligent Document Processing helps financial services organizations.

How Does Intelligent Document Processing Work?

IDP uses deep-learning AI technology to scan complex data, extract it and organize it into predefined categories. The best part is that this technology can be trained in up to 190 languages. 

One of the best ways to understand how document processing works is to look into a few use cases first. Here are multiple ways in which IDP helps organizations from various industries to automate their data. 

IDP for Human Resources

One of the popular intelligent document processing use cases is human resources. This paper-intensive industry has already transitioned to data automation by: 

  1. Screening resumes and capturing the right skill sets that match a job description. This has helped HR teams avoid going through resumes manually and narrow down on candidates who are fit to take up the role, hassle-free. 
  2. Keeping all the employee data in one place. Earlier, HR teams were forced to manage hundreds of thousands of files for new and existing employees. Not only was it hard to find specific data quickly, but it was hard to comprehend at times. But with IDP, it is easier to update and extract the employee data when need be. 
  3. Simplifying the employee onboarding process by capturing employee information based on the forms filled in by them. 

IDP for Mortgage Processing

Another data heavy use case you can refer to is mortgage processing. Given how vast the industry is, you can only imagine the number of documents that accumulate at every stage of the mortgage process. Let’s see how IDP simplifies document accumulation and data extraction for this industry: 

  1. IDP is capable of processing high-volume mortgage data and identifying possible risks one may face during the process. With IDP, mortgage officers can identify the cause for rejection and inform approvers about these potential risks. 
  2. Validating documents is time consuming. However, IDP helps you validate the data from documents with a button and offers you insights on whether to move ahead with the mortgage or not. 
  3. You can also audit each mortgage application with IDP and that too without human intervention. It can be time consuming to audit documents for a mortgage even when you have a professional helping you out with the process. With IDP, you can save time and also check the authenticity of each document.

Top 7 Benefits of Intelligent Document Processing

  1. Increases Employee Productivity

One of the benefits of using IDP is its ability to increase employee productivity. Intelligent document processing helps employees free up their time that they spend on doing repetitive tasks such as data entry and record management. Lesser time spent on repetitive tasks helps increase their productivity. 

  1. Helps Reduce Manual Work

IDP is also known for reducing manual work as it enables extraction of data from a document,  an image, sound, or even a video using its AI technology. What’s more, the data extracted is then transformed into text, thereby helping employees reduce manual work on such tasks. It uses Natural Language Processing (NLP) that understands the content and converts that data into text. 

  1. Automates Classification of Documents

Once the data is converted to text, it is classified into different categories, without the need for human intervention. This helps your organization with data collection and streamlining the document management process with minimal errors. 

  1. Enables the Processing of Large Volumes of Documents

Another benefit of using IDP is processing large volumes of documents in one go. While humans take time to extract data from each document, the same isn’t true in the case of IDP. Unlike humans, a document processor can extract data from multiple documents simultaneously. You can tackle a giant database and avoid spending capital on a data entry team.  

  1. Improves Data Accuracy

Humans are prone to make errors when feeding data to a database, especially when there are a large number of documents. This may hamper the overall authenticity of the database and may leave managers questioning its accuracy. 

Thankfully, the same isn’t true in the case of AI and ML-based IDP technology. It is capable of entering the data accurately in the database and retrieving the same data with speed when its users need it on an urgent basis. In short, an automated document processor can extract accurate data and eliminate mistakes with an accuracy of more than 90%. 

  1. Increases User and Customer Satisfaction

Intelligent Document Processing is capable of providing users with optimal responsiveness through the life cycle of work to be performed. With IDP, you can process evidentiary documents, extract the right keywords from a data set in less time. What’s more, the automated process can use keywords to route emails to the right department for faster throughput from start to finish. This helps speed up the response time between receiving an origination request to approving it, from a claim initiation to notification of completion, etc. Documents are no longer the bottleneck. 

  1. Offers Data Security

When documents are managed manually or in disparate data systems, there is always a possibility of a data breach. But with IDP, documents can be kept in a secure, centralized location. This enables businesses to be more compliant with data protection regulations. In other words, you can make sure that the data saved never gets misused by anyone.  

Maximize the Benefits of Intelligent Document Processing

Benefits such as these (and others discussed in the article above), will help you identify various reasons why a data extraction processes like Intelligent Document Processing are essential for your business. Once you decide to opt for intelligent document processing, it’s time to look for the right implementation partner. 

Why Choose Blanc Labs’ Intelligent Document Processing?

Blanc Labs believes in driving improved operational efficiency and better business outcomes.  

Blanc Labs’s IDP solution can: 

  • Automate workflows for document collection, digitization and analysis  
  • Replace manual effort through intelligent data capture 
  • Connect with third party data providers for analysis and insights  
  • Analyze document data, provide status alerts, and flag fraudulent entries  
  • Secure documents in a drop box  
  • Deploy on premises, in the cloud or as a hybrid model  

 

We hope that learning about these benefits will help you arrive at a decision faster and invest in the best document processing solution in the market. 

News

Blanc Labs and Daylight are teaming up to help enterprise teams simplify rules-based complex processes

Blanc Labs, a technology services company specializing in digital transformation for financial services and healthcare is partnering with Daylight to deliver digital solutions focused on simplifying logic and/or rules-based processes. 

Complex processes are elaborate labyrinths of different checks and balances. 88% of senior IT leaders in North American agree that optimizing complex processes is  both very costly and requires significant resources*. Blanc Labs and Daylight strategic partnership will focus on: 

  • Empowering businesses to easily build digital user experiences that replicate the rules of their business processes
  • Digitize and automate paper-based processes
  • Quickly build connections to legacy applications to deliver a modern end-user experience without the need to rip and replace
  • Optimize ROI from investment in automation by putting business technologists in the driver’s seat

 

“We are committed to helping organizations transition into the digital age by leveraging our deep sector expertise, ecosystem partners, and world-class tech talent to reduce risk and achieve strategic objectives,” said Hamid Akbari, CEO of Blanc Labs. “We believe our partnership with Daylight will be a game-changer for companies looking to do more with less”.

The partnership will focus on developing and delivering end-to-end solutions without displacing the existing tech stack or disrupting IT. 

“Both teams are passionate about empowering teams with the ability to build their own solutions and take charge of improving processes and scaling digital transformation initiatives… I’m excited to see the impact we can make together,” said Art Harrison, CGO of Daylight.

*2022 Daylight Market Survey: 100 IT leaders in North America (Enterprise Orgs with 5000-10000 employees)

About Blanc Labs  

Blanc Labs offers technology solutions that help enterprises become ready for the future. Blanc Labs has developed expertise and bespoke solutions in a wide variety of applications, including financial services, healthcare, enterprise productivity, and customer experience, to help companies rapidly deliver on their digital initiatives. Headquartered in Toronto, Blanc Labs serves the Americas through operations in Toronto, New York, Bogota, and Buenos Aires.

For more information, visit www.blanclabs.com 

 About Daylight  

Daylight makes it easy for your business teams to simplify and digitize your current processes through rapid iterations while addressing IT concerns like security, privacy, scalability, and integration. From modernizing legacy systems to improving the flow of data and information between people, processes, and systems, we empower your business users, and IT teams to streamline inefficient processes and free up valuable resources to complete more projects.

For more information, visit daylight.io

 

Articles

Top Use Cases for Banking Automation

Top Use Cases Banking Automation_Blanc Labs
Illustration by Storyset

Did you know that the World Bank uses Banking Automation including robotic process automation (RPA) for many of its functions? That’s how powerful it is. 

Modern businesses rely on automation to reduce costs and improve efficiency, but how can banks use automation? In this article, we explain the most common use cases of banking automation. 

What is Banking Automation? 

Banking automation involves handing over repetitive business processes in financial institutions like banks and credit unions to technologies like robotic process automation (RPA), artificial intelligence (AI), and machine learning (ML). 

Why Banks Need Intelligent Automation

As a banking professional, you know that a good chunk of your daily tasks is repetitive and mundane. Banking automation eliminates the need for manual work, freeing up your time for tasks that require critical thinking. 

Here are seven reasons why banks needs intelligent automation:

  1. Better Customer Experience
  2. Automated KYC
  3. Faster Mortgage Application Processing
  4. Accurate Report Generation
  5. Anti-Money Laundering Prevention
  6. Audits and Compliance
  7. Faster Decision Making

Below we dive deeper into banking automation use cases so you can get a closer look at how automation can transform your workflow.

1. Customer Experience 

A robust Customer Experience has never been more important. As the world moves online, you’ll need to re-engineer your Customer Experience to make it friction free, faster and more efficient. 

Consider a customer’s first experience with the customer onboarding process. If this isn’t a painless experience, you risk turning away a customer in the first interaction. 

Sure, you might need to invest some money to improve the customer experience and make it seamless and efficient, but the potential ROI is excellent. Think about it. Automation will eliminate much of the manual and low-value in-person interaction, saving your sales reps plenty of time to focus on running effective sales campaigns. 

Automating processes reduce the potential for errors, allowing you to onboard customers faster. Automation also reduces costs because it eliminates the manual labor and paperwork associated with customer onboarding. 

Moreover, your customers will be able to use their accounts faster, which improves customer experience. As a McKinsey report explains: 

“Automation and artificial intelligence, already an important part of consumer banking, will penetrate operations far more deeply in the coming years, delivering benefits not only for a bank’s cost structure, but for its customers.”

2. KYC

Most banks perform KYC (Know Your Customer) by manually verifying customer details. The problem? Manual verification can take plenty of hours. 

The KYC process doesn’t end at verification. You must manage KYC documents for a long time to comply with regulatory requirements. Using automation in banking operations can help free up the hours you spend on manual verification. 

Moreover, automation also eliminates the risk of human error. By eliminating room for error, automation ensures improved customer experience, increased quality assurance, and the number of cases processed each month, according to a McKinsey study.

3. Mortgage Application Processing

Mortgage application processing involves plenty of paperwork. Manually checking details on each document is time-consuming and leaves room for error. On the other hand, intelligent document processing (IDP) helps streamline document management. 

Remember those desks full of paperwork? That’s a thing of the past. Modern banks use IDP to manage documents digitally. IDP helps automate the generation of customer risk profiles and mortgage document processing, reducing processing time to a few days. 

An Accenture study found that 47% of customers prefer opening a new bank account online using a computer, while 37% prefer using the bank’s app or website. 

The shifting consumer preferences point to a future where loan requests and processing are online and automated. Now is a great time to prepare your bank for that future.

4. Report Generation

All financial institutions need to generate reports for various purposes. For example, you might need to generate a report to show quarterly performance or transaction reports for a major client. 

RPA can help generate reports automatically. The system can auto-fill details into a report and prepare an error-free report within seconds. An automated system can perform various other operations as well, such as extracting data from internal or external systems and fact-checking the reports.

5. Anti-Money Laundering (AML) Prevention

In Canada, banks need to ensure they are complying with the statutes of the Proceeds of Crime (Money Laundering) and Terrorist Financing Act, 2000. Depending on your location, compliance requirements might include ongoing risk-based assessment, customer due diligence, and educating staff and customers about AML laws. 

A single AML investigation can take 30 minutes or more when assigned to an employee. However, automation can complete the same investigation much faster and minimize errors. 

Using automation ensures you don’t spend too much money on AML investigations and stay compliant, so you don’t have to pay hefty fines.

6. Audits and Compliance

The cost of maintaining compliance can total up to $10,000 on average for large firms according to the Competitive Enterprise Institute. 

Maintaining compliance is expensive but less so than being non-compliant. For example, banks must ensure data accuracy when producing loan facility letters. However, instead of requiring employees to spend time meticulously verifying customer data, you can use intelligent document processing to save time and guarantee data accuracy. 

Banking automation can help you save a good amount of money you currently spend on maintaining compliance. With automation, you can create workflows that satisfy compliance requirements without much manual intervention. These workflows are designed to automatically create audit trails so you can track the effectiveness of automated workflows and have compliance data to show when needed. 

For example, you can set up a system to auto-freeze compromised accounts. Once the account is frozen, RPA can automatically complete the steps in your fraud investigation process. The system also creates an audit trail in the process. 

Unlike humans, RPA can be active 24/7. Using RPA in banking can help ensure the accuracy of compliance processes, ensuring you’re compliant at all times without investing a lot of human resources towards compliance.

7. Decision Making

Managers at financial institutions need to make decisions about marketing, operations, and sales, but relying on raw data or external research doesn’t provide full context. RPA can help compile and analyze internal data to track client spending patterns and preferences. 

AI and RPA-powered automation can help make decisions about timing marketing campaigns, redesigning workflows, and tailor-making products for your target audience. As a result, you improve the campaign’s effectiveness, process efficiency, and customer experience. 

For example, when introducing a mortgage loan product, you can use RPA’s data analysis capabilities to identify location-specific and borrower-specific risks. RPA can compile a summary of the risks on a document, allowing the credit manager to study risk profiles without the associated manual work. 

 

Blanc Labs’ Banking Automation Solutions 

Blanc Labs works with financial organizations like banks, credit unions, and Fintechs to automate their processes. 

We can create tailor-made automation software solutions based on your banks’ needs to minimize manual work and improve process efficiency. Our team can help you automate one or multiple parts of your workflow using technologies like RPA, AI, and ML. 

Book a discovery call with us to see first-hand how automation can transform your bank’s core operations. We’ll create an automation solution specifically for your organization that works in tandem with your current internal systems. 

Articles

The Transformative Power of Banking Automation

Automation for banks
Image credit: vectorjuice on Freepik

McKinsey expects machines to be responsible for up to 10% to 25% of a bank’s functions. The reasons? Banking automation minimizes the need for your team to work on repetitive tasks, allowing them to focus on high-profile and strategic aspects of the business. 

Automation also improves accuracy, which can save you a ton of money — a major reason why 80% of finance leaders have implemented or plan to implement Automation (including Robotic Process Automation). 

Curious about how banking automation can help? We explain everything you need to know about how automating your banking workflow can help reduce costs and improve efficiency. 

What is Banking Automation?

Banking automation involves using software powered by multiple technologies like AI (artificial intelligence) and ML (machine learning) to automate repetitive tasks. Automation has three primary benefits: 

  • Frees up your team’s time for more strategic tasks 
  • Improves process accuracy 
  • Improves the Customer Experience (CX) and the Employee Experience (EX) 

 

For example, you can automate your account opening process. A customer requests a new account via the chatbot on your website. The chatbot provides an application form. The applicant fills out the form, and it’s sent to your RPA robot. The robot performs the basic procedures, including checking the credit score and KYC verification. 

Next, the robot scans the applicant’s documents using OCR (optical character recognition) for data extraction. The robot matches the information in the documents and the application form. It flags any details that don’t match and sends them for manual approval. 

The robot continues to validate uploaded documents using NLP (natural language processing). It finds key data points in the document’s free text, categorizes them, and uses them in the automated process. 

The robot then updates the bank’s backend system to create a new business account, provided the customer’s data meets the bank policy. Once approved, the customer receives an automated welcome email. 

Why Banks Need Banking Automation 

Banks need automation to compete in the modern banking environment. Now, that’s a broad statement, so here are specific reasons why a modern bank needs automation: 

  • Allowing employees to focus on tasks that require a human touch: Most banks were set up long ago. Manual forms and workflows were a foundational pillar for legacy banks, and as a result, employees spend countless hours on things like data entry and account verification. Automation allows employees to “hand over” repetitive tasks to software, freeing up their time for high-profile tasks that require a human touch. 
  • Record management: RPA can generate and check expense records for compliance. It auto-logs all transactions and prepares the necessary financial records to get an overview of your business’s financial performance and position. 
  • Meeting customer expectations: The need for speed is a key driver of a modern customer’s experience. If you’re taking too long for basic operations like opening a bank account, you’ll lose customers fast. Automation can help speed up your processes and help deliver on your customer’s expectations. 
  • Faster customer support: Your customers hate waiting hours to get an answer. Automating your support using RPA helps you respond faster. You can answer customers’ questions at scale using a chatbot. Also, you can use an AI-powered chatbot to answer questions you haven’t added as an FAQ. 

 

These factors make automation more of a necessity than a nice-to-have — you need automation to compete neck-and-neck with other banks. 

How Banking Automation Can Transform Your Bank 

Transforming your bank’s value network with automation offers many benefits in various business aspects, including finance, legal, and customer experience. Here are the benefits of using RPA in banking: 

Banking Automation Leads to Efficiency 

You can improve productivity by up to 80%, especially if you identify the most impactful productivity levers. The efficiency improvement is a result of two factors: 

  • Low manual effort: Employees have more time available once they hand over repetitive tasks to software. They can do more in the same amount of time, helping you scale your operations. 
  • Improved accuracy: Errors are expensive because you spend time and resources on correcting the errors. Fewer errors = improved productivity. 

 

A great example of efficiency is automated document processing. As a banker, you probably spend a good number of hours reading documents and inserting relevant data into your systems, depending on your role at the bank. However, you don’t have to spend all those hours manually entering data if you use intelligent document processing. 

Better Customer Experience 

An average company takes over 12 hours to respond to customer service requests. That’s a recipe for dissatisfied customers, especially if you’re a financial institution. 

Your customers expect their money to be in the hands of a reliable entity, and guess what you communicate when you don’t answer customers for over 12 hours? 

Using RPA to automate your customer support helps minimize response times. In most cases, the chatbot can provide real-time answers to the most commonly asked questions. 

Speed is also critical for other client-side processes. For example, you want to be as fast as possible in opening accounts, processing personal investment requests, or enabling additional services for an account. Automating these processes (while ensuring accuracy) helps improve customer experience. 

Compliance and Risk Reporting 

According to Deloitte, the cost of compliance for retail and corporate banks has increased by over 60% since the pre-financial crisis spending levels. Non-compliance is even more expensive, but automation can help lower your spending on compliance. 

RPA builds compliance into your processes. Automating compliance ensures you’re always meeting regulatory requirements without requiring teams to spend extra time double-checking for compliance. 

Automation also creates an audit trail and automatically generates risk reports that give you added insights. The system can identify and flag suspicious activities so that you can investigate them. 

Reduced Costs

It’s easy to see how banking automation using RPA can reduce costs. Reduced administrative load, saving time on repetitive tasks, and speeding up processes all yield dividends. 

For example, Radius financial group reduced loan processing costs by 70% by using AI to automate their process. 

Banking automation also removes human error, so you’ll spend less on fixing those mistakes. 

Without automation, you’d need to invest a large amount of money in building more teams as you scale. However, automation empowers you to scale faster. You can continue investing in training current teams and save on costs you’d incur to accommodate a larger workforce. 

Automation and Adaptability 

Banking automation helps banks adapt faster to a client’s needs or the business environment. 

For example, the increasing popularity of Fintech is one of the most significant concerns for banks. Fintechs are quickly gaining market share at the expense of legacy banks. Customers appreciate how a fintech offers better, faster services. 

Fintechs aren’t the only factor banks need to consider, though. Your bank might want to integrate banking solutions with a new partner’s ecosystem to offer additional services like tax consulting. Or your bank needs to process offshore transactions faster, especially when the transaction is subject to jurisdictional restrictions on the amount of transfer allowed. 

Adaptability is critical for banks to succeed, and automation can make adapting to changes seamless. Implementing an automation solution will improve your adaptability to changes and allow you to quickly catch up with your modern competitors. 

The Bottom Line 

Over the past five decades, banking has gone from paper-based to almost entirely digital. Next up? Automation. 

Automation makes banking frictionless for both internal and external stakeholders — it’s a win-win. The only problem banks face with automation is the lack of a reliable partner who can guide them through the transformation journey. Book a discovery call with us, and we’ll answer all your banking automation questions. 

Articles

What Is Composable Banking and Why Should I Care?

Composable Banking is a technology and transformation approach that addresses the simple fact that change is constant. To ensure that banks and FI’s can innovate swiftly and maintain the greatest level of control over their product roadmap, they must adopt a modular or “swappable” architecture. The characteristics that define Composable banking follow the MACH principles: Microservices, API First, Cloud Native, Headless.

In a world that is evolving at an ever-increasing pace, it can seem as though the velocity of technology trends is starting to reflect the feverish pace of modern-day news cycles.  It can be hard to keep up with all that is happening across the financial services industry and a lot of the content out there is often decorated in painful consulting speakThat is why we view it as our accountability to sift through the noise and develop an informed opinion on the what, why, and how of emerging trends that our clients and ecosystem partners need to know about.   

A significant shift is underway in terms of how banks and FI’s do transformation work reflecting similar (r)evolutions in other industries like eCommerce and tech platform players like Apple and Google. The term that is being increasingly adopted to encompass a broad cross-section of tech evolution amongst financial institutions is composable banking.  

Composable banking is a technology-enabled approach to delivering financial products and services to customers and ecosystem partnersIt is a banking transformation approach that addresses the simple fact that change is constant. To ensure that banks and FI’s can innovate swiftly and have an agile experience roadmap, they must own a modular “swappable” architecture. This is the only way to deploy new features rapidly and retain control of their destiny. 

Modular banking is not composable banking

First, let us define the characteristics of composable banking by delineating how it is different from the traditional, modular approach offered by E2E core banking systems provided by established SaaS vendors.  They have been using a modular approach to extend the functionality of their core systems, whereby their propriety modules are extensible but are neither flexible nor open.     

Composable banking is a solution approach that prioritizes integration readiness and flexibility, allowing organizations to dramatically improve the speed at which a company can onboard a new partner or design, build, test and deploy a new product.  This is relevant for value-driven business transformations aiming to build differentiating customer experiences while setting up a future-proof, flexible and cost-effective IT landscape.  

What you are Composed of matters 

Composable banking is enabled through the adoption of MACH characteristics to define how an organization approaches developing and supporting technology to enable new customer experiences and improve business operations.  


Microservices


M: Individual pieces of business functionality that are independently developed, deployed, and managed. 

With a microservices architecture, an application is built as independent components that run each application process as a service. 

These services communicate via a well-defined interface using lightweight APIs. Services are built for business capabilities and each service performs a single function. Because they are independently run, each service can be updated, deployed, and scaled to meet the demand for specific functions of an application.


Microservices Enabled Banking Example: 

 Monzo is a UK based neo-bank that has written extensively about their API-first approach and how they scale, secure, and manage over the over 2000 microservices that power their banking experiences. 


API First


A: All functionality is exposed through an API. 

API-first is a product-centric approach to developing APIs. It views the role of APIs as discrete products, rather than integrations subsumed within other systems. 

 Developing and managing microservices in an API-first approach means that APIs become key inputs to determine & define product functionality.  This means that the people developing against your API are your users, and your API needs to be designed with those users in mind. 

An API-first mindset requires adopting product management best practices to ensure the services evolve to meet the needs of users (developers), particularly around the characteristics of flexibility, interoperability and reusability.   


API-First Banking Example: 

Citibank is one such organization that follows an API-first approach in its path to digital transformation and empowers a developer ecosystem for innovation. Citi’s global consumer bank serves 62 million clients in 35 countries and uses APIs to build many of its consumer facing digital products. 


Cloud Native


C: SaaS that leverages the cloud, beyond storage and hosting, including elastic scaling and automatically updating.  

 Cloud-native technologies empower organizations to build and run scalable applications in modern, dynamic environments such as public, private, and hybrid clouds.  They feature containers, service meshes, microservices, immutable infrastructure, and declarative APIs to exemplify this approach. 

 Cloud-native banks leverage core banking systems built in the cloud and for the cloud to enjoy benefits such as scalability, flexibility, availability and elasticity, amongst others. 


Cloud Native Banking Example: 

 In September 2021, JP Morgan Chase announced that it would migrate its retail core banking assets to the Google Cloud Platform and leverage Thought Machine cloud native core banking system. 


Headless


H: Headless architecture enables an organization to evolve from a monolithic approach to service delivery to an ecosystem model.  

Front-end presentation is decoupled from back-end logic and channel, programming language, and is framework agnostic. 

Enables the ability to seamlessly embed secure banking services into a variety of customer touchpoints.   e.g., Apple Pay, ACH money transfers, budgeting and billing platforms.


Headless Architecture Banking Example: 

Using the API’s available through Temenos core banking platform, EQ Bank was able to act as a deposit-taking backend for Wealthsimple to launch high-interest savings account offering to WS clients in Canada. 

 

In the image below, we’ve highlighted some of the intended outcomes FI’s can expect to benefit from as they undertake the development of a digital transformation strategy and embark on a journey that can only be described as iterative and incremental. Spoiler alert: the work is never doneThe goal though is that with investment and dedication it goes faster and gets easier to measure 

What is composable banking

“Almost half of the global financial services organizations are still in a very early or even immature stage of their digital transformation journey.”  

– Juergen Weiss, FI Practice Vice-President at Gartner 

Digital Transformation is the entire journey by which a financial institution seeks to digitize and automate its processes to improve its products and customer experiences and expand into newer, untapped markets with speed and greater operating efficiency.   

For those organizations that are still in the early stages of planning and prioritizing their transformation initiative, the breadth of choice, cost, and complexity can be daunting. Some of the activities required to achieve MACH characteristics require major infrastructure upgrades and the migration will most likely be implemented over a multi-year horizon.  

Status of core banking initiatives

A Composable Approach to Digital Transformation 

Developing a composable transformation approach should allow for multi-threaded initiatives that support the broader objectives. For this reason, we often work with clients to highlight one to two areas of opportunity, whereby organizations can see an immediate ROI in terms of CX and operational efficiency.   

Initiatives like implementing an intelligent document capture platform to reduce manual data entry into a Loan Origination Systems (LOS) or conducting an API Maturity assessment allow FI’s to realize immediate benefits while building transformation capability and creating foundational progress towards a future state that reflects the characteristics of composable banking.  

Curious about how you can develop a composable transformation approach at your organization? Book a workshop with Blanc Labs.

Articles

Top Use Cases of Intelligent Document Processing

Top use cases of intelligent document processing

Currently, most enterprises have a workflow rampant with manual document-heavy processing. 

However, businesses are quickly digitizing their document-processing workflows. 50% of B2B invoices across the globe will be processed without manual intervention according to a Gartner study. The reason? Manual document processing is more expensive than the cost of the documents themselves. 

For example, the average cost of processing a single invoice was $10.89 in 2021. Manual document processing is also prone to human errors like fat finger errors. In a world where 90% of the data is unstructured, you need a tool that can automatically convert unstructured data into structured data to supercharge your productivity. 

This guide explains how you can use intelligent document processing to save your business plenty of money, time, and resources. 

What is Intelligent Document Processing?

Intelligent Document Processing (IDP) is a technology that automatically extracts unstructured data from multiple document sources, including images, online forms, and PDFs. IDP is also known as Cognitive Document Processing (CDP). 

IDP converts this unstructured data into structured data using multiple technologies, including natural language processing (NLP), machine learning (ML), optical character recognition (OCR), and intelligent character recognition (ICR). Together, these technologies make IDP intelligent.

OCR is often used interchangeably with IDP. However, that’s not true. IDP uses OCR as one of the technologies to extract data. 

How Does Intelligent Document Processing Work? 

Here’s how a document is processed using IDP: 

  • Conversion: An IDP platform starts by capturing your document through a scanning device. Once it converts a physical document into a digital one, it starts ingesting data. 
  • Document image processing: The document’s image is processed for optimal OCR and archival. 
  • Reading text using OCR: OCR helps the machine accurately read the scanned document’s text. 
  • Identify language elements with NLP: IDP platforms use NLP to find language elements using methods like feature-based tagging and sentiment analysis. 
  • Machine learning algorithm classifies information: A combination of machine learning and other techniques is used to classify the information in the document. 
  • Extracting elements using AI: IDP uses AI to extract information elements like contact numbers, addresses, and names. 
  • Validation: IDP platforms validate information using third-party databases and lexicons for data validation. Data points are flagged when the platform can’t validate them so someone from the team can review them manually. 

Top Intelligent Data Processing Use Cases in Banking 

Reading and writing financial documents make up a large portion of a bank’s workflow. As a bank, you need to process data fast to offer best-in-class services to your customers without making errors. 

IDP helps banks guarantee accuracy and efficiency to their clients. In addition to data extraction’s key role in a bank’s workflow, banks can also use IDP platforms for fraud detection. 

Here are some of the most common use cases of IDP for banks. 

Mortgage Underwriting 

Customer satisfaction with mortgage originators reduced by five points on a 1,000-point scale in 2021 according to a study by J.D. Power driven by record mortgage origination volume. Banks need to automate their mortgage workflow to scale as the demand grows. After all, customer satisfaction is one of the most significant differentiators in the mortgage industry. 

The mortgage workflow involves collecting various documents. Extracting data from these documents is one of the major factors slowing down the workflow. This is where an IDP tool can help streamline your mortgage workflow. 

An IDP tool helps you speed up the underwriting process with automation. It automatically reads and extracts relevant data and relays it to your bank’s credit evaluation system. 

Claims Processing

The P&C Customer Satisfaction Survey reveals that the claim filing process is the biggest driver of customer satisfaction. 

However, the claims processing workflow can be complex. Claims data comes in various formats—customers might send data as word files, PDFs, and images. Plus, you might receive the data via multiple channels—you might receive it via email, chat, or over a call. 

Unifying this data without manual effort is a massive challenge. Traditionally, banks used OCR to process physical documents. However, the lack of accuracy required manual review. 

An IDP tool is a great alternative to OCR for claims processing. Thanks to technologies like NLP, computer vision, and deep learning, it provides greater accuracy than traditional OCR. 

Customer Onboarding 

Customer onboarding is one of the most resource-intensive processes for a bank. Banks spend an average of $280 to onboard a single client according to Backbase—the cost can add up when you’re onboarding hundreds or thousands of customers every month. 

Many of these expenses go towards processing documents, including the bank’s forms, credit reports, or tax returns. Sure, you can try automating this workflow. However, the automation will break down as soon as a new document type is introduced or you change your form’s template. 

An IDP tool can help tame your customer onboarding costs. Your customers will appreciate a fast onboarding experience, and you’ll save money, increase productivity, and make an excellent first impression. 

Financial Document Analysis 

Banks handle thousands of financial documents every day. From financial statements to tax returns, carefully studying financial documents is critical to a bank’s operations. 

Financial analysis is a cognitively heavy task. Why make your team spend time on mundane tasks like manipulating data when you can use an IDP tool to automate this process and enable your team to concentrate on their more complex deliverables. 

Using an IDP tool helps analysts automatically structure and populate relevant financial data into their system. You’ll do your analyst team a favor by eliminating a lot of their manual work, allowing them to focus on analysis. 

KYC Process Automation 

KYC (Know Your Customer), Re-KYC, and C-KYC are critical for compliance. Banks might need to refer to a customer’s KYC details at various stages during a customer’s journey. 

However, handling hand-written KYC forms is a hassle. Migrating a customer’s KYC data comes with challenges like human error and work overload. Committing errors when underwriting a mortgage or onboarding a customer costs money, but failing to comply with KYC requirements may increase the legal, compliance and regulatory risks.

Using IDP ensures accuracy, so you never have to lose your reputation and pay a fine for failing to comply with KYC norms.  The McKinsey KYC Benchmark Survey found that by increasing end-to-end KYC-process automation by 20%, an organization could enjoy the following positive outcomes: 

  • Increased quality assurance by 13% 
  • Improved customer experience (by reducing customer outreach frequency) by 18% 
  • Increased the number of cases processed per month by 48%

 

The Bottom Line 

Banks process a colossal amountnumber of documents and data each day. Getting new customer data into the system, processing claims, and analyzing financial statements are heavily data-driven tasks that involve dozens of documents from hundreds of customers. 

The probability of committing errors is high. Banks also need a large team just to process documents and structure the data in those documents. 

Banks need an IDP tool to automate this process and remove the risk of error from the process. It also integrates with applications to make migrating the data easier. An IDP tool also validates data and alerts team members in exception cases, when it requires a human to review accuracy. 

It is important to select an IDP tool that offers the right solutions for your industry. Better yet, find a partner who can create a custom IDP solution tailor-made for you. 

Why Choose Blanc Labs Intelligent Document Processing? 

Blanc Labs partners with financial organizations like banks, credit unions, and fintechs to automate operations. 

We can help you create robust automation solutions that minimize manual effort, reduce errors, and improve productivity. Our team helps you use the most advanced technologies including AI and ML to automate complex, resource-heavy processes like document processing. 

Book a discovery call with us if your financial organization deals with plenty of documents daily. We’ll come up with a tailor-made solution to minimize the friction in your document processing workflow. 

 

News

Innovation Is The Key To Digitally Transform Financial Services

In a world where customers are becoming increasingly demanding, we need to find new ways of meeting their needs. The only way to do this is through innovation.

In this Business Ninjas podcast, our CEO, Hamid Akbari, talks about how Blanc Labs helps financial services organizations evolve their technology to meet new customer needs and make the most of market opportunities.

For more information on how Blanc Labs can help with digital transformation, get in touch.

 

 

Articles

Finding the right API Management Platform

APIs are an integral part of today’s digital world. They are used for secure data exchange, integration, and content syndication. As APIs become more ubiquitous in enterprise businesses, it becomes necessary to manage them efficiently.

Blanc Labs API management

Banking and Payments ecosystems are converging with Open Banking and Finance. Whether regulatory or market driven, these digital interactions are happening already – and growing exponentially. Doing APIs and API Management right are central to the growing interdependence and interoperability between Fintechs, Banks and Consumers. Stakeholders are demanding secure access to financial data to drive better customer experiences. A key enabler to that end are the systems that surround APIs.

What is API Lifecycle Management?

API Lifecycle Management is the process of building, controlling, distributing, analyzing, and reusing APIs. It also can include capabilities around intelligent discovery; one pane of glass visible across multiple API gateways and API management systems; bringing to life the visionary end state of monetizing and marketing all these capabilities to external parties to operationalize the concept of “API as a product”. Thus, there are many API Management solutions in the market offering a variety of features. But at the very minimum, an API Manager should allow users to do the following:

Discover APIs

Before you can more effectively govern their lifecycle, you need a simple and configurable tool to find, filter and tag all your API assets into a centralized repository. Simplify complexity and/or get better visibility and facts to position your organization to “open itself up” to the new business realities and opportunities emerging.


Design, build, and Test APIs


The API Management tool should provide everyone, from developers to partners, the ability to create APIs under a unified catalog and test their performance.


Deploy APIs


API Management tools should also allow you to publish APIs on-premises, on the cloud or in a hybrid environment. Additionally, the API Manager may give you a choice between managing the API infrastructure in the tool itself or on your own.


Secure APIs

By providing a central point of control, most API Management tools will ensure that you have full visibility of all your APIs across environments so you can mitigate any vulnerabilities.


Manage APIs


API Management tools should give you a central plane of visibility into APIs, events, and microservices. Most API management tools will allow you to govern APIs across all environments (on-premise, hybrid, cloud) and also allow you to integrate with other infrastructures, including AWS, Azure, and Mulesoft. A good API management tool should also provide multiple predefined policy filters to accelerate policy configuration.


Analyze APIs


An API Manager should give you real time metrics in a unified catalog. By providing data on the business performance or operations across your APIs, you can make better decisions leading to improved business results.


Extend and Reuse APIs


By giving you a single, unified catalog, an API Manager can eliminate duplication and extend the life of APIs through reuse.

The need for API Management

API management centralizes control of your API program—including analytics, access control, monetization, and developer workflows. It provides dependability, flexibility (to adapt to shifting needs), quality, and speed. To achieve these goals, an API Manager should, at the minimum, offer rate limits, access control, and usage policies. 

Essential features of an API Manager tool 

1. API Gateways 

 A gateway is the single entry point for all clients and is the most critical aspect of API management. An API gateway handles all the data routing requests and protocol translations between third-party providers (TPP) and the client. Gateways are equally important when securing API connections by deploying authentication and enforcement protocols.

2. Developer Portal 

 The primary use of the developer portal is to provide a hub, specifically for developers, to access and share API documentation. It is an essential part of streamlining communications between teams. Typically, developer portals are built on content management systems (CMS), allowing developers to explore, read, and test APIs. Other features of a developer portal could include chat forums for the internal and external developer community and FAQs. 

3. API Lifecycle Management 

 As the name suggests, API Lifecycle Management provides an end-to-end view of how to manage APIs. API Lifecycle Management is a means to create a secure ecosystem for building, deploying, testing and monetizing and marketing APIs. 

4. Analytics engine 

The analytics engine identifies usage patterns, analyzes historical data, and creates tests for API performance to detect integration issues and assist in troubleshooting. The information gathered by the analytics engine can be used by business owners and technology teams to optimize their API offerings and improve them over time. 

5. API monetization and marketing 

API management tools can provide a framework for pricing and packaging APIs for partners and developers. Monetizing APIs involves generating revenue and keeping the API operational for consumers. Through usage contracts, you can monetize the microservices behind APIs. An API management tool will offer templatized usage contracts based on predefined metrics, including the number of API calls. This empowers innovative external players to help drive your business in ways you have not dreamed up yet – and still do it securely. 

How successful is your API management?

Now that we know the features of an ideal API management software, how do you evaluate its success for your API efforts? Here are a few ways to track your progress:

Speed
How rapidly can you launch your APIs to meet your business goals? Latency and throughput are ways to measure the speed of deployment. Other areas to measure speed would be onboarding and upgrading APIs.


Flexibility
Flexibility is the breadth of options available to developers when adopting APIs. The greater the flexibility, the higher the cost and effort to manage the API.


Dependability
How available your APIs are to developers. One way to measure dependability is downtime. Quota is another way to restrict how many API calls can be made by a developer within a certain timeframe. Enforcing quotas makes API management more predictable and protects the API from abuse.


Quality
Stable APIs with consistent performance reflect higher quality. It is a way to measure a developer’s satisfaction with the API.


Cost
The above four factors contribute to cost. If your API management software provides a better view of all your APIs, it will reduce duplication and costs. Reuse of APIs is another way that you can save costs.

How are you managing API complexity?

If you are a business leader concerned about how to meet market demand through the creation and deployment of APIs, or you would like to monetize and reuse your existing APIs and reduce costs, then you need structured API management. 

In partnership with Axway, Blanc Labs offers a way to manage your APIs to bring maximum business value. Axway’s API Management Platform enables enterprises to manage and govern their APIs for developing and applying their digital services. 

Book a discovery call with Blanc Labs to learn more. 

Interested in hearing how we can accelerate your digital transformation?