In BI and Analytics

The financial services industry has undergone sweeping change in the past decades owing to different waves of regulatory, customer technology, competition and market challenges.It is no secret that today, banks and other financial institutions must evolve continually to provide the best customer experience to users and remain competitive in the saturated financial sector.

Moreover, with massive counter competition from virtual banking solutions, banks are under immense pressure to boost efficiency and optimize their present resources. The scarcity of skilled resources, a sudden surge in personnel costs, and the need to improve process efficiencies triggered by the decade old global financial crisis is yet to abate. While many today remain focused on the implications of the digital revolution and its impact on customer expectations and behaviour, new opportunities are also emerging. One such technological innovation- Robotic Process Automation or RPA as it is widely marked, is changing the way the banks, insurers and capital market firms execute their basic processes.

RPA adoption is steadily gaining grounds and financial institutions are adopting it with more planning to follow suit. According to recent reports, banks are estimated to disburse nearly $270 billion yearly, just on compliance operations. Almost more than 10% of a bank’s operating cost is attributed to compliance costs.

Let’s start with some quick definitions. Robotic process automation is a suite of technology options used across multiple industries to automate business processes. RPA software involves “software robots”, empowered with decision-making abilities with knowledge-based programming to handle mundane repetitive tasks traditionally handled by employees. That said, there are no actual robots involved in the way one might see in the manufacturing or heavy machinery industry.

 

Shift transition to Intelligent Automation (IA)

It’s clear that RPA has found favourable grounds in the financial industry. As the financial institutions are moving past crucial experimental stages and realized benefits from the RPA investments, they are extending its application across various functions within the enterprise. A vast number of companies are exploring the reality of advanced AI-enabled RPA platforms to make automation more cross-functional and cognitive. Combining RPA with analytics, AI and virtual agents, financial services can be automated more than just back-office processes.

RPA’s benefits for financial services are manifold. For one thing, the technology enables vast reductions in security breaches caused by human error. The personal data breaches across industries were caused by human error such as sending data to the wrong recipient or in response to phishing attacks. Taking people out of the process can positively boost security while helping people to do tasks that create more value for customers.

RPA can help financial institutions to drive efficient growth by executing pre-programmed rules across a range of unstructured, semi-structured and structured data. Today, this intelligent automation has given processes the power to learn from prior decisions and data patterns to make decisions by themselves- freeing you up to focus on the important work of driving your business forward.

Using the automated systems paves the way for data privacy controls and fraud detection. This is critical for banks and financial service providers as data security and compliance are essential for their profitability. In this manner, the digital workforce reinforces governance and enables financial service providers to function more effectively and profitably.

And, the potential benefits don’t stop there. Success in today’s complex global financial markets requires an unparalleled level of accuracy, speed, and cost efficiency beyond what a human workforce can deliver. That’s precisely where RPA and artificial intelligence-driven cognitive automation can transform their businesses.

 

Key Use Cases for Automation in Finance Industry

Because of its non-invasion nature, implementation of RPA services needs minimal upfront investment and can be introduced into your organization’s existing IT structure without making any drastic changes into the underlying system. Let’s have a look at a few use cases where RPA has been deployed in areas including risk controls, risk monitoring and reporting. 

Customer onboarding: During the customer onboarding process, combining data from various internal and external sources is a big task. Bots can be used to collect, retrieve and manage data from regulatory bodies to speed up the onboarding process. Banks use RPA solutions to gather documents for KYC details and validation processes as it helps them to quickly identify prospects with any suspicious records and reject their application. 

Suspicious activity alert investigation: The transaction monitoring system generates a huge amount of alerts to identify suspicious activities. Although most of the work involved in resolving the alerts are standardized and repetitive in nature, this is an ideal scenario for RPA solutions integrated with cognitive capabilities. By automating judgement based activities, RPA can speed up the issue resolution process and enhance overall fraud management within the banks. 

Limit breach management: Risk officers review, approve or reject the counterparty exposure limit breaches. This process involves manual collection of data from disparate sources, followed by manual analysis of data sets to make decisions. This process becomes time-consuming and error-prone. Using the cognitive RPA solutions to automate data collection helps in faster analysis of data and boosts accurate decision-making. 

Internal and external reporting: The external and internal regulatory reports like delinquency reports, daily liquidity coverage reports, and much more require teams to spend significant time on gathering data and consolidating it. Sometimes, this lengthy and tedious process impacts the quality and accuracy of these reports. Using RPA for data assimilation will help business teams immensely in focusing on analysis and review of reports. 

Reconciliation: The reconciliation activities are bit standardized and business users test the data using specific rules. Deployment of RPA solutions helps in automating the data gathering process, carrying out business rule checks for faster resolution of issues. Quicker reconciliation in turn leads to faster management of information reporting and on-time reviews that can help discover errors and inconsistencies. 

The Future of Workforce is Here to Stay

A new world of workforce is upon us and it is the reality. As we embark on this transformation journey, there will be a lot more significant changes in the way teams are structured, roles are defined and the type of capabilities and skills unfold to execute and manage it all. It is thus crucial for organizations to get ahead of the workplace, and talent implications, which has been embarked by automation with robust change management, and people’s strategies that reduce the negative disruptions and ensure the best possible outcomes for the financial organizations. 

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