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.
Steps to use automation at every stage
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.
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.
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.
Use case 2
Expense reporting and reimbursement.
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.
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
You must Focus on
Improving efficiency and productivity
Cost and Time Reduction
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.