Robotic Process Automation is garnering a lot of attention in the Financial Services industry. RPA is changing the nature of the workforce in this industry and proving to be a major means of cost-saving and operational efficiency.
Let us take a look at what this digital labor is being applied to and why it is gaining popularity beyond mere process automation.
Typical financial companies are banks, insurance companies, and credit card companies, where document processing is the backbone of the business. Millions of forms and applications, certificates, documents are filled, checked, forwarded, compared, verified, and processed as part of the business. Data from one form is gathered in one place and entered into another form, and certain decisions are made based on it. Data is regularly stored, retrieved, and audited. All of it is labor-intensive. These are highly repetitive tasks but which demand great accuracy in order to avoid risks.
This is an ideal scenario for RPA technology, which uses software bots with rules-based processing capability. Organizations can deploy a rule-based virtual workforce and connect it to the company’s systems. This digital workforce will act in the exact same way as the existing human users and do the same tasks. You can automate the entire process across the front and back office as well as the support teams.
RPA can carry out data entry faster and more accurately than humans and work 24 by 7 without a break, increasing work cycles during off-work hours. Organizations can expect 50-60% cost savings by deploying RPA. This not only increases operational efficiency but also leads to many benefits.
The finance industry has a lot of regulatory compliance and legal requirements. The risk percentage is high and so is the return. Therefore, the accuracy of data is crucial in this industry. Data management processes must be more robust and not prone to errors. Data entry must be accurate all the time. This is where RPA’s accuracy comes into the picture. RPA technology eliminates human error and is exceedingly accurate and well suited for data management in finance. RPA will not miss or vary steps or add errors in data. Of course, for this, your workflow must be set up clearly with precision. With RPA you can be sure that these rules are applied 100% of the time as it adheres to the control framework.
In an industry that spans banking, mortgages, loans, and investments, a system that enhances compliance is very useful. Once accuracy is taken care of, it is much easier to meet compliance needs and reduce risks. Auditors will see the value in the RPA framework following the prescribed steps repeatedly with no errors or bias, creating a level of confidence. In addition, unlike human processes, bots can keep an audit trail of all the work that the RPA does. RPA may reduce costs enough to eliminate outsourcing, and many an auditor will be happy to have data on their premise, unlike in the case of outsourcing the work to someone. Up-to-date, industry-specific requirements can be built into the RPA.
Compliance processes, once automated and standardized, will carry out a managed set of steps and collect the data in the same system and easily identify anomalies and inconsistencies. Consistent operation of an RPA will end up saving non-compliance fines and penalties.
Using RPA, business face less risk in their data, and transactions. Since risk is reduced/eliminated, RPA services can help companies follow an ‘increased risk/increased reward” model in certain areas of growth.
All financial institutions are highly regulated, face frequent audits, and need robust operations for accuracy and risk management. RPA reduces human error significantly, thereby enabling institutions to accomplish audits with lesser costs and in lesser time and with greater accuracy and confidence. Updated regulations can be managed easily by making changes in the framework.
RPA can be a stepping stone to applying Artificial Intelligence to this area. RPA and AI can help find patterns and insights from data. Adding AI as a next step, banks will be able to make things very customer-centric. If an AI system recognizes the customer, it can bring up connected information like spending patterns and recent transactions, and pitch for selling a suitable credit card to the customer.
As another example, with the help of machine learning, lawyers can parse thousands of deals in a short while, a task that used to take them many days earlier.
Surveillance and fraud detection, which was done through audits and data sampling, can take the AI approach. With AI, you can analyze really huge volumes of business data to detect fraud patterns. A machine learning-based model uses an algorithmic rules-based approach for surveillance. As such, machine learning techniques are way ahead of human-based fraud detection.
RPA seems ideally suited for the financial sector, going beyond mere labor-saving, due to its precise data management and repeated workflow processes, its adherence to compliance rules and its ability to mitigate risk.
RPA drives efficiency and growth by carrying out pre-programmed rules on all range of structured and unstructured data in a financial institution. Going further, we can say that intelligent automation helps processes learn from data patterns, and prior decisions to make better decisions on their own - freeing precious human capital to focus on taking the business forward.