Articles

10 Tips to Successfully Implement RPA in Finance

Automation has taken the finance industry by storm, and for all good reasons. Banking automation technologies like Robotic Process Automation (RPA) come with the promise of streamlining processes and increasing efficiency.

According to Forbes, RPA has the potential to transform the way finance functions, from reducing manual errors to freeing up valuable resources. To help you maximize the benefits of RPA in finance, we’ve put together a list of 10 tips for successful implementation. But first, let’s take a step back and explore what RPA is.

What is Robotic Process Automation (RPA)?

RPA is the use of software robots to automate routine and repetitive tasks like document processing, freeing up employees to focus on strategic activities that can lead to better customer service or innovations that could meet customer expectations.

The bots are programmed to follow specific rules and procedures to complete a task, just like an employee. They can interact with various software applications and systems, such as spreadsheets and databases, to collect and process data. The bots can also make decisions, trigger responses, and communicate with other systems and software.

When a task is triggered, the bot performs it automatically, eradicating the scope of errors that may be produced through manual processes. The process is monitored and managed by a central control system, allowing adjustments and updates to be made as needed.

Think of RPA as a digital workforce, working tirelessly in the background to complete tasks that would otherwise take hours to complete. With RPA in finance, your financial institution can increase efficiency, reduce costs, and improve the accuracy of its processes.

The Use of RPA in Banking Automation

There are many ways in which RPA can be used in banking automation. Here are some of them:

Customer Service Automation

RPA is capable of automating routine customer service tasks such as account opening, balance inquiries, and transaction processing, allowing bank employees to focus on more complex customer needs.

Loan Processing

RPA in banking and finance can streamline the loan processing workflow by automating repetitive tasks such as document collection and verification, credit score analysis, and loan decision-making.

Fraud Detection and Prevention

In 2022 alone, banks and financial institutions lost $500K to fraud. Technologies like RPA can analyze vast amounts of data to detect and prevent fraud in real-time, improving the accuracy and speed of fraud detection. It may also report any suspected fraud to the banking authorities as soon as it is discovered.

KYC and AML Compliance

Verification and compliance document processing can eat up a major share of your institution’s resources. RPA can automate the process of collecting, verifying, and analyzing customer information, helping banks to comply with KYC (Know Your Customer) and AML (Anti-Money Laundering) regulations.

Back Office Operations

RPA can automate various back-office tasks such as data entry, reconciliation, report generation, monthly closing, and management reports, freeing up employees to focus on more strategic initiatives.

Payment Processing

Manual data entry in payment processing can lead to manual errors and longer processing time. RPA can automate the payment processing workflow, including payment initiation, authorization, and settlement, reducing the risk of errors and improving efficiency.

Risk Management

Banks and financial institutions are constantly at risk from various sources. RPA in finance can help institutions identify, assess, and manage risks by automating data collection, analysis, and reporting, improving the accuracy and speed of risk assessments.

Internal Compliance Monitoring

RPA can automate the process of monitoring and reporting on compliance with internal policies and regulations, reducing the risk of non-compliance and improving overall compliance management.

The potential of RPA in banking and finance is unlimited. When combined with Intelligent Automation technologies, RPA can leverage Artificial Intelligence (AI) and Machine Learning (ML) to provide more intelligent process automation. While the technology in itself is efficient, financial institutions must know how to implement it for maximized benefit.

10 Tips for Successfully Implementing RPA in Finance

According to the Deloitte Global RPA survey, 53% of organizations who took part in the survey have already begun their RPA implementation. The number is expected to rise to 72% over the next year. Entering the RPA race can be quick, but managing and scaling it is a different ball game. Before getting started with automation initiatives, it is important to consider the following tips.

Start Small

While RPA might seem useful to rejuvenate all of your systems and processing, it is important to consider the business impact and start small. Beginning with a smaller project, for example, a single process or department can help build momentum and demonstrate the benefits of RPA. It also allows the organization to gain experience and develop a better understanding of the technology before scaling up.

Define Clear Goals

The key to successful RPA implementation rests in your goals. Decide what you want to achieve with RPA in consultation with your IT department. Having a clear understanding of the goals and objectives of the RPA implementation will ensure that resources are allocated appropriately and that the project stays on track. Aim for specific, measurable, achievable, relevant, and time-bound (SMART) goals.

Involve Stakeholders

Engaging stakeholders, such as the management, finance employees, and IT, can help build better design and smoother change management for the RPA implementation. It also ensures that the RPA system is well-integrated across departments and addresses the needs and concerns of all stakeholders.

Assess Processes

When a financial institution leverages too many bots to automate processes, it results in a pile of data. They may be tempted to use ML on the data and create a chatbot to make customer queries easier. However, it can lead to a poorly planned ML project. Thus, a thorough assessment of the processes is critical to ensure that the RPA implementation does not get sidetracked. This assessment should include an analysis of the tasks, inputs, outputs, and stakeholders involved in the process.

Choose the Right Tools

Selecting the right RPA tools and technology is critical to the success of the implementation. Decide the mix of automation technologies your institution requires before reaching out to a vendor. Factors to consider include the cost, scalability, and ease of use , as well as its ability to integrate with other systems and applications.

Define Roles and Responsibilities

Clearly defining the roles and responsibilities of the RPA team, including project governance, testers, and users, is important to ensure that everyone knows what is expected of them. Remember that automation is a gradual process, and your employees will still need to interfere if it is not properly automated.

Ensure Data Security

Protecting sensitive data and customer information is a key concern in finance. RPA needs to be implemented in such a way that data security remains unaffected. Discuss with your vendor about strong security measures to ensure that data is protected and that the confidentiality of customer information is maintained.

Plan for Scalability

There are thousands of processes in banks and financial institutions that can use automation. Thus, the RPA implementation should be planned with scalability in mind so that the technology and processes can be scaled up as needed. This helps to ensure that the RPA can be combined with AI and ML technologies as necessary and the implementation remains relevant in the long term.

Monitor and Evaluate Performance

Continuously monitoring and evaluating the performance of the RPA bots is critical to ensure that they are operating effectively and efficiently. Also, do not forget to keep HR in the loop so that employees are informed and trained about the changing processes and how to use them in a timely manner. Regular evaluations should be conducted to identify areas for improvement and to make adjustments as needed.

Foster a Culture of Innovation

Encouraging a culture of innovation and experimentation can help to ensure that the organization is prepared for the future. Consult your IT department and automate your entire development lifecycle to protect your bots from disappearing after a major update. Invest in a center of excellence that can create business cases, measure ROI and cost optimization and track progress against goals.

Implementation Automation with Blanc Labs

At Blanc Labs, we understand that every financial and banking institution has unique needs and challenges when it comes to RPA implementation. That’s why we offer tailor-made RPA solutions to ensure seamless implementation for our clients.

Our RPA systems are designed to be flexible and scalable, allowing our clients to start small and grow as needed. We provide end-to-end support, from process assessment and design to implementation and ongoing assistance, to ensure that our clients realize the full benefits of RPA.

Booking a free consultation with us is the first step toward successful RPA implementation. Our team of experts work closely with each client to understand their specific requirements and goals to build a customized RPA solution to meet their needs.

Articles

The Complete Guide to Intelligent Document Processing

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:

Document pre-processing
Data Capture
Document Classification
Document Extraction
Document Verification
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 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.

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.”

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.