Optimize and Automate your Financial and Accounting Process at Ease
Intelligent Process Automation is an approach that combines RPA, Business Process Mining and Orchestration, Machine Learning, and Natural Language Processing in one solution to achieve better business outcomes compared to using an RPA Platform only.
Asset management values RPA for its inestimable contribution to business development. Robotic process automation facilitates labor-intensive processes like fund and estate administration, reporting and analysis, client onboarding, compliance and risk management, and so much more. The business environment is continuously changing, and in this changing environment, the financial industry is under great pressure to cut costs. And provide enhanced services to the customer while maintaining their competitive edge. The customer today wants easy and quick access to services, great personalization, and value for money. The financial institutions have to meet these while maintaining their costs. And all of this can be achieved through Robotic Process Automation. Robotic Process Automation involves bots; bots that are deployed to mimic the day-to-day, routine tasks that are performed using some business rule and can be easily automated. A large number of financial institutions have been opting for RPA to meet these needs. Many of the research reports suggest that hundreds of financial institutions have successfully employed software ‘bots’. Amongst the study participants, 32% of companies consider themselves as ‘RPA leaders’, meaning they’ve employed automation virtually across every function within their enterprises.
Business Process Mining helps to analyze and optimize processes based on event logs generated by systems.
Business Process Management is an essential tool for orchestrating, controlling, monitoring, and continually improving complex processes.
Machine Learning means systems can learn via handling variations that are not anticipated upfront. These systems are trained on the go by assimilating lessons from the data and decisions backed by algorithms. Examples include processing image-based POs, identifying molecules from an image, and triggering a relevant process.
Natural Language Processing uses statistical methods and learning algorithms to analyze text and unstructured information to understand the meaning, sentiment, and intent. A sample use case is in customer service, where a customer raises a support ticket in the form of free text, which is then analyzed to determine the next step, and a process is subsequently triggered.
From The Company
We at Lab RPA provide you a complete F&A(FINANCIAL AND ACCOUNTING SERVICES) process Automator, the best automation solution which includes a step by step solution from making the business logic to orchestrate the software robots which possess the key features –
- Providing an enhanced customer experience
- Reducing churn
- Improving customer satisfaction quotient
- Engendering customer loyalty and trust
- Enhancing data quality
- Ensuring KYC regulations are met
- Stabilizing industry best practices
- 24/7 availability.
A full-fledged operational automated framework to manage all of your Financial and Accounting processes and operations.
Key Challenges And Characteristics
The concept comes with its own automation challenges though. These are natural challenges, maybe every automation system cannot overcome them. Some of the most common financial service process automation challenges are multiple integrations, dynamic product information, and frequent changes to the configuration. Multiple integrations refer to the various third-party system integrations that work on both ends of the system. Integrating all these integrations and having them work in sync with the newly induced automation process could be quite a challenge, which makes automation difficult.
There're some Key Concerns
Let's discuss them briefly!
Risk of manual intervention during reconciliation
Implementation Time & Cost
Manual data entry: Traditional claims processing is heavily reliant on manual data entry, making the process inconsistent and full of errors.
Disparate mediums: The disparate medium through which information is processed and collected (like images, emails, papers) creates a lack of accountability and ownership.
Legacy systems: Lack of integration of legacy systems with newer solutions hamper productivity.
Data retrieval: Data retrieval poses a huge challenge when multiple software, processes, systems, and applications are used.
Regulation and compliance: Change in laws and regulation can severely impact the claims processing, posing a serious challenge for companies to remain compliant when operating in different states and countries.
• Lack of time commitment from local team
• Lack of leadership buy-in
• Lack of IT support
• Lack of support from Analytics/Data function
• Lack of support from HR
• Unclear responsibilities
• Company lacking a clear RPA strategy
• Choosing a process that changes frequently.
• Choosing a process with insignificant business impact.
• Choosing a process where errors are disproportionately costly.
• Choosing a process that involves higher-level cognitive tasks.
• Choosing a process where better custom solutions exist.
• Pursuing in-house RPA development with in-house teams that do not have enough capacity
• Choosing a solution that requires intensive programming.
• Not relying on RPA marketplaces and other readily available tools.
• Choosing a solution that did not demonstrate scalability.
• Not building for scalability
• Not taking maintenance needs into account
• Not securing RPA privileged credentials
The Key Characterstics
Let's discuss them briefly!
Automatic Report Generation
A regular requirement of a bank is to generate compliance reports of fraudulent activities in the form of suspicious activity report or SAR which have to be checked by the compliance officers and read manually and fill in the details in the SAR which is an extremely cumbersome task and takes a lot of time. But, if we implement RPA with natural language generation capability, this entire process can be quickly completed in record time where it can read through the process and extract the required information for filling in SAR which leads to a reduction in operational cost and also saves time.
Customer onboarding is a long and tedious process primarily because many documents are required for manual verification. This whole process can easily be automated by using RPA tools to extract the data from KYC using OCR, which can then be matched with the data provided by the customer. If no discrepancies are encountered, then it can automatically enter the data into the customer management portal. This not only removes the chances of error but also saves time and effort put in by the employees.
KYC and Anti-Money Laundering
Both these processes are very data-intensive, which makes them suitable for RPA, ranging from activities of catching suspicious banking transactions or automating manual processes. We can quickly implement RPA, which saves both time and cost as compared to the traditional solutions provided.
By implementation of RPA, the process of account opening has become much more straightforward, quick, and accurate. Automation directly eliminates errors that may exist between the core banking system and new account opening request, thus enhancing the data quality of the system.
The process of mortgage lending is extremely time consuming and thus making it a perfect choice for automation. It allows for the automation of various tasks that are crucial in the mortgage lending process including loan initiation, document processing, quality control, etc. This helps in faster completion of the process leading to enhanced customer satisfaction. Another benefit of this is that it unburdens the employees from doing manual tasks, thus helping them to focus on essential tasks.
Loan Processing has always been considered a very tedious process, even though banks have automated it to some extent, but further automation will bring down the processing to a record 10-15 minute process. This will lead to increased customer satisfaction and reduced workload on employees.
There is a large volume of common customer queries making it difficult for the staff to respond to them with low turnaround time. RPA tools allow them to automate such mundane rule-based tasks to effectively respond to respond to queries in real-time, thereby reducing the turnaround time.
And so on....
Simplified New Business On-boarding
Sometimes companies grow faster than they can manage. Robots can abet growth with minimal growing pains: manual inter-departmental data movement from new clients being on-boarded can be reduced by at least 50% – within weeks.
RPA bots are scalable and can be called forward to manage high volumes of data, and answer to a massive influx of queries in record times. Gone are the days when businesses had to bear significant labor costs when demand/workload spikes. In today’s time, with insightful guidance and a reliable RPA service provider, you can automate F&A processes in a matter of few weeks.
EfficiencyFinance and Accounting involve long-strings of number and repetitive, rule-based transactional processes. Upon successful implementation of RPA, financial institutions can accelerate these transactions, whilst enjoying increased efficiency and reliability of data with minimal errors.
Competitive AdvantageSince the corporate culture is dynamic and ever-so changing, the importance of having a competitive edge can’t be stressed enough. A slight variation in costs, or innovation dictates whether the company would benefit from lasting success or struggle to keep its operation running. RPA automation in Finance & Accounting directly translates to these subtle yet essential advantages, thanks to low integration costs, higher accuracy, and easy scalability.
Innovation in DataThe financial and accounting industry can benefit from RPA implementation as it offers deeper insights into business operations via a smart amalgamation of the legacy and new data. The peculiar combination of data in one system purveys better reporting and insights for business growth.
Address Compliance IssuesHumans can be dead-serious in regards to work, but part of being a human is making mistakes. Numbers are essential to Finance, and even if a single digit goes wrong- an entire system could go haywire. That’s not the case with RPA bots. Bots run according to a set of established rules, deliver higher levels of quality, and substantiates financial success.
One of the crucial factors in achieving high ROI is in understanding what can be achieved through RPA and selecting the right processes to automate. Automating a small process with a time-saving of few minutes per process can yield better savings over a year. For instance, RPA driven cost rationalization in horizontal processes such as HR, finance and accounting, procurement, contact center process are much faster than industry-specific processes such as claims processing.
The biggest hurdle in value realization is the complexity of processes. Process complexity can increase with stringent regulatory requirements, geographies, and numbers of exceptions that can potentially stretch development and user-acceptance testing time. While you can expect to reach break-even in six months for simple processes, it may take you as long as two years for complex ones. Cost savings can vary from 10% to 50%, once again determined by the complexity of processes.
Confidential processes where there is a high risk in involving humans merit your scrutiny for robotic process automation as well.
When there is a potential to save $2 trillion in global workforce costs through automating 45% of work activities (according to PwC), no wonder enterprises are drawn to invest in robotic process automation (RPA). RPA is believed to demonstrate value realization faster than many other automation initiatives.
RPA takes just about 25% of the time required for a business process workflow solution and 16% of the time taken for enterprise application integration to demonstrate significant value. One of the immediate perceivable benefits of RPA is cost reduction by employee optimization — freeing them from routine tasks to do jobs that demand high cognitive skills.
Calculating ROI requires a detailed analysis of parameters unique to your organization. Some of the pre-requisites to achieve significant early ROI are:
- Identifying suitable tasks for automation
- Carving out appropriate processes
- Choosing the right systems
A typical ROI realization process takes from 6 to 9 months