Articles

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. 

 

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. 

Interested in hearing how we can accelerate your digital transformation?