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How RPA Implementation can Help you Excel in Banking and Financial sector

Featured Insights
March 21, 2023

RPA has dramatically emerged as a dominating technology, particularly in the post-Covid world, and has huge demand in every sector. On the other side, Finance and Banking domain shares a sustainable market and is continuously evolving with digital technology.

It is the right time to leverage RPA technology in these sectors with the use of advanced options such as AI and Hyper Automation at a large scale. In fact, RPA in banking and finance sector is predicted to reach $1.12 billion by 2025. The banking and finance sectors thus have many use cases where RPA can make an impact.

The market share of Banking and Finance in the global market

According to Gartner, the pandemic has accelerated digital transformation imperatives in the banking and finance, pushing them to adapt to the demands of employees and customers by making digital options the future of banking services. Automation has thus become the need of the hour to overcome internal challenges, maximize efficiency and ensure low costs.

In Banking automation is not just limited to auditing and updating information for customers, tasks such as bank account opening and closure, credit and debit card management, suspicious activity investigation, Locking & unlocking accounts, limit breach management, managing transaction charges in a different currency, Loan management, and reconciliation, customer data collation and vendor activation can also be automated, securing seamless operational flow and keeping immediate business initiatives aligned.

Similarly, in Finance automation is beyond auditing and invoice processing. Automation can streamline activities such as payroll payment, financial planning, and analysis, preparing month-end and bank account closure, health claim filing intercompany reporting, currency rate updates, expense reporting, and expense reimbursement.

Use case 1

Suspicious activity investigation.

Data are very sensitive. Suspicious activities such as cyber-attacks, fraudulent transactions, phishing scams, and card skimming are performed by attackers and customer data is at stack. Sometimes employees from the bank itself give access to data to do fraud. Due to the higher number of customers, some people use fake identities to open accounts or to get a loan.

Solution

To investigate these activities technology can help a lot. The first step is to optimize the investigation process and then incorporate technology with each step. By using AI algorithms, one can analyze large amounts of data to identify anomalies and patterns.

Steps to use automation at every stage

Customer complaint

Customers can register complaints via chatbot or web forms, email, or phone.

Once a complaint is registered it gets an autogenerated ticket with details like date time, customer name type of complaint, and other related information that can be captured.

Based on some pre-decided checklist this ticket can be automatically prioritized. After this based on certain conditions automation can classify it as a type of fraud case and take necessary actions or assign it to the responsible person.

Monitor transaction

Once it is identified as a fraud issue, the transaction monitoring process is enabled using machine learning and AI algorithms.

Pattern recognition and anomalies detection: Identify patterns of transactions in the account such as a higher amount transferred to a single account, continuous transactions to one single account, and new accounts to which the amount is transferred other than normal regular accounts. Transaction amount, frequency, and location are the key variables that are being measured. Here Decision Trees, Neural Networks, Logistic Regression, Random Forest algorithms, and rule-based systems are very useful.
Risk Scoring is another method where the algorithm assigns scores to each transaction based on the pattern it recognizes which indicates a higher score means a higher risk of fraud.

Customer reporting

After each activity customer can be notified of the steps taken and what are the next steps, which can be automated emails and messages. If the user needs to do a certain action for related fraud such as submitting documents, filling forms, or locking unlocking of cards that also can be automated.

Automation in the banking sector can be established for auditing and updating information for customers, tasks such as bank account opening and closure, credit and debit card management, Lock unlock accounts, Limit breach management, managing transaction charges in a different currency, Loan management, and reconciliation, Customer data collation and vendor activation also can be possible using trending technologies.

Verification

Verification is very important to detect whether AI ML results are correct or not.

Use case 2

Expense reporting and reimbursement.

Expense-related activities are very common and can be significantly improved. It contains a large set of documents processing.

Document scanning

Processing many documents can be automated through intelligent document processing using AI algorithms such as Optical character recognition.

Document categorization

Further to categorize and classify different documents based on some rules Natural Language Processing can be useful for accurate expense classification.

Reimbursement approval

The approval and rejection process also can be automated which results in faster and more efficient processing. And automatically letting your customer know about the result with the appropriate information via email, SMS notification is a smart way of providing information.

In Finance, automation can be further extended to automate auditing and invoice processing, activities such as Payroll payment, financial planning, and analysis, preparing month-end and bank account closure, health claim filing, intercompany reporting, and currency rate updates are possible through automation.

Policy enforcement

To reduce the risk of fraud decision tree algorithm can be applied and a neural network algorithm and RPA to enforce rules and policy regulations related to expense is important to avoid inaccuracy and breach.

Here are the things which help you to excel in these sectors with RPA

Understanding the sector

The first and most important step is to understand what and how the banking and financial sector works. The focus of both sectors is customer data.

It is always better to first know about what data they process and how much data they process, followed by how much customer involvement and interaction are involved.

Understanding customer needs

Banking and Finance are very customer-centric. One must always think about the service they provide to customers. Understanding customer requirements and expectations will provide us with more insight into how and where we need to focus to implement RPA.

Identify the pain points of the customer first and then find a solution that will lead to high customer satisfaction.

Identification of the right process

A vast set of operations is performed in Banking and financial sector for validating, managing, and auditing purposes. It is a very important step to identify the right process for automation by checking its feasibility based on data volume and steps the process has and the ROI it produced.

A process that solves a problem saves hard work, cost and time is the right process that can be automated.

Identification of the right technologies

RPA is not now just limited to process automation. We have various technologies that we can make use of such as hyper-automation, AI, and Mainframe automation which cover almost all areas in these sectors.

These technologies generate lots of opportunities to leverage automation for different types of use cases.

Streamline and optimize the process

Once you identify the process and its feasibility, you must check the scope to optimize the process and streamline the process if needed before starting any implementation.

This will bring quality, operational efficiency, and scalability and open the door for many automation and remove unnecessary step inefficiencies.

Networking and tech skills

When you are trying to automate a task, you need to build networking connections to understand the pain points, needs, and importance of that activity. At the same time, to solve a problem you should have the tech skill set and problem-solving and analytical skills.

You must Focus on

Improving efficiency and productivity

The goal of RPA is always to give better shape and behavior to the existing process. With the feasibility study and streamlining phase, we must focus on improving efficiency and how to increase productivity.

Cost and Time Reduction

Cost and time are the most expensive things. Hence, it is critical that you calculate how you much you will be spending both before the implementation of the solution. Reduction in cost and time will benefit both customers and operations.

Accuracy and reducing errors.

When you define rules and automat tasks focus should be on avoiding errors that occur during manual processing and providing accuracy in results.

Data is always very crucial, and in the Banking and finance sector it is much more sensitive. So, accuracy plays a very vital role here.

Customer needs and customer satisfaction

The utmost importance should be on customer and customer satisfaction with the service you provide. The end user will not be aware of how streamlined your internal operations are. The only thing they are concerned about is the end results. As such, it is extremely important to properly understand the customer’s needs and improve services that your customers would approve of.

Decision-making on real-time analysis

Real-time data analysis is one of the most important steps leading to smart and future-proof decision-making. Based on real-time data your automation should provide results and insights to make timely decisions and take actions if necessary. For this, knowledge of data analytics and visualization expertise are also critical.

Facilitating digital transformation

Making use of technology and tools in every stream to generate sustainable revenue, and improving efficiency and scalability can be the key that unlocks unprecedented growth. Even if the entire process is not suitable for automation you can automate parts of the process and facilitate digital transformation to future-proof your organization.

Conclusion

Standing today, RPA seems to be the answer to all your organizational & operational challenges. You can combine multiple technologies including Artificial Intelligence and Machine Learning to make decisions, and leverage mainframe automation to automate back-office tasks such as data management and scheduling jobs, system performance monitoring, and data storage maintenance. The robotic operation center becomes the setup organization that provides a consolidated, central point to manage and monitor your automation.

We can leverage automation in every task in some or another way. But the trick is to identify the right use cases. When implemented effectively, RPA can transform how your business operates and grows by transcending its focus to customer satisfaction, problem-solving, and choosing the right technology.