IDP and RPA In Banking: 10x Boost in the Banking Automation

According to Gartner, 80% of leaders in the financial sector are using an RPA for various purposes.

The combination of IDP and RPA in Banking are two technologies that are used to improve efficiency and reduce costs. RPA involves the use of software robots to automate repetitive, rule-based tasks, while IDP uses machine learning and artificial intelligence to automate the processing of documents. 

In this article, I am gonna show you Robotic process automation (RPA) in the banking industry to drive digital transformation.

What is RPA?

The full form of RPA is Robotic Process Automation. This technology uses Software Bots to easily build, deeply, and automate repetitive, rule-based tasks typically performed by a human.

For instance, with the help of RPA technology, you can automate repetitive tasks such as Document Processing, Form Filling, Data Entry, Copy Paste Work, Customer Service Automation, and many more. Basically, this technology is specially designed and innovative to improve efficiency, improve speed, reduce errors, and lower human costs.

What is Robotic Process Automation in Banking?

Robotic Process Automation (RPA) in banking is the use of software robots or “bots” to automate manual banking work and improve task efficiency without any human interaction. This can include data entry, account reconciliation, customer service interactions, and compliance-related processes. 

By analyzing where human interaction is required and where not, then you can set a rule for the bot on what it is exactly to do. And, after setting this, RPA starts its operation without any active human interaction. This RPA bot executes the process 24*7 without any error, whereas humans fail to do this.

Already many banks are integrated their system with RPA and IDP Software and reduced their manual document processing work and other repetitive work. Some RPA use cases in banking are Credit card applications Processing, Mortgage processing, Back office operations, Anti-money laundering compliance, and 24*7 Customer Service.

Main Challenges Faced by the banking industry

Adopting New Technology 

Banks are under pressure to modernize their systems with the new technology as per the changing the customer expectation and behavior. New technologies, such as mobile banking and peer-to-peer lending, have created new competitors for traditional banks. So, if any Bank does not update its technology, it will lose customers and also its Stock Prices in Stock Market. 

Increasing regulation and compliance costs

 The banking industry is increasingly subject to regulation, which has led to high compliance costs. Banks must comply with a complex and constantly changing set of regulations, such as those related to anti-money laundering (AML) and know-your-customer (KYC) requirements. These are designed to further stabilize the banking system and prevent Fraud and financial crises. This can be costly and time-consuming for banks to implement and maintain.

Slow Economy and Political Instability

Slow economic growth and political uncertainty can affect banks’ ability to lend and generate revenue, as well as their ability to repay their customers. Basically, When the economy is growing slowly, demand for loans and other banking products and services is low. It may result in less profit for the bank and even loss.

Integrate RPA with Intelligent Document Processing (IDP) 

RPA provides the basics needed to automate processes. To further enhance RPA, banks are starting to implement Intelligent Document Processing Automation (IDP).  IDP is a technology that uses AI, Machine Learning, and optical character recognition (OCR) to automatically extract and classify data from various types of documents, such as invoices, receipts, Account Opening Application Forms, bank statements, and many more. By doing so, Banks can fully automate their task and massively grow their Business Operation.

Benefits of RPA in Banking and Finance

  • Robots are highly scalable to handle large volumes.
  • Combination of IDP and RPA Improved accuracy and less error.
  • Increase efficiency and productivity.
  • You don’t need new IT infrastructure for implementing RPA in Banking System.
  • Advanced data management and analysis.
  • With RPA Banks can save time up to 75% on certain operational processes
  • Improved operational visibility and control.

RPA Use Cases in Banking

Automatic Accounts payable 

Account Payment is a very crucial and time-consuming task for the Bank. if it does it manually, so first an employee digitalizes and validates the ID information, then Process it But, with the help of IDP, Software OCR, and RPA can automate these repetitive payment processes by automatically reading the invoices and completing the payments after rectifying errors and validating data.

Customer Service Automation

From setting up Accounts to Fraud Loan Calling, Everyday Banks deal with a huge number of customer queries. Call center executives can’t able to do their work, call centers can become flooded, and When high volumes are inbound Inquiries happen. On the other hand, if a customer waits for long periods, it leads the customer to dissatisfy. To solve this issue, Bank needs RPA and Chatbot to handle this large traffic. RPA can take care of low-priority tasks, while, difficult human decision-making solutions are required at that time, it connects with the Executive.

Smooth KYC Process and Customer Onboarding

Verifying the KYC(Know-Your-Customer) Document and Onboarding the Customer are very difficult and also important things to do for the banks. Veryfit KYC Docomenst for each Account Holder is a very time-consuming process for the banks. 

According to a recent study by Thomson Reuters, conducting KYC compliance and customer due diligence can be significant, banks have to manage the cost which is estimated at $384 million annually.

By Integrating IDP Software like Docextractor and RPA, the whole KYC Process and Customer Onboarding Process is accelerated.

Credit Card Processing

RPA-enabled automation for processing credit card applications is another use case where banks have seen tremendous results. In the past, it would take at least 1-2 weeks for a bank to verify a credit card application. For this slow process, many people cancel their applications by frustration.

RPA software takes only a few hours to scan through credit card applications, all the essential documents of the customer. This Bot easily navigates through multiple systems, validates data, after checking, and decides whether to approve or reject the application.

Loan Processing

Loan processing is labor-intensive for both the customer and the bank. Loan Documents are usually delivered via email in a bundled PDF format. Banks have to check each and every document and through the process which takes time to finally approve the loan.

So, to speed up the process, you can use a combination of IDP and RPA. By Setting rules, RPA bots with AI capabilities are used to intelligently extract data and automate the overall process, It can be reduced by up to 80%.

Fraud and AML Detection 

AML analysts normally spend only 10% of their time on analysis. Most of their effort, close to 75%, goes into data collection and another 15% into data entry and organization.

So, you have understood, how the time-consuming process is. That’s why, to save this time, you need to integrate your system with Automation. Automation of the entire AML investigation process is one of the most prominent examples of RPA in banking.

If you do it manually, it takes between 30 and 40 minutes to investigate a single case depending on the complexity and availability of resources. Therefore, by using RPA, These repetitive and rule-based tasks can be easily automated, reducing over 60% of the time.

Docextrcator for RPA In Banking

Docextractor is used to enable users to collect data from invoices, receipts, and other documents with the power of its AI and ML algorithms. This software is specially designed for the Banks to integrate into their systems and automate all repetitive tasks with RPA and IDP.

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