Intelligent Automation (IA) is a powerful combination of machine learning (ML) and artificial intelligence (AI) capabilities with process automation. Intelligent Automation can be called the descendant of robotic process automation (RPA), which is limited to rule-based functions. Intelligent Automation for financial services will drive business innovation by taking over mundane tasks and relieving the mental capacities of employees to focus on better work worthy of their effort. Fastidiousness is the name of the game in Financial Services, and IA, in coordination with data analytics, can chew through mountains of unstructured data, which the industry has in abundance, and provide actionable insights at scale. Precise implementation of IA can help in boosting employee productivity, increase employee satisfaction, reduce operational costs and errors, assist intelligent decisions and improve customer experience.
Intelligent Automation for financial services holds excellent potential for growth which is reflected in Gartner’s 2021 survey numbers which say 49% of banking and investment companies and 44% of insurance companies in the US intend to increase their investment in IA in FY 2022. 80% of financial companies have implemented or are in the process of implementing RPA into their systems. The next logical and technological step for these companies would be Intelligent Automation.
Although IA and AI are often used interchangeably, AI is a broader umbrella. Artificial Intelligence tries to emulate the cognitive functions of the human brain to replace humans from the loop and make decisions on their own without intervention.
Intelligent Automation plays the role of “Sherlock” to the mere “Watson” human mind and fortifies the decision-making process that’s pivotal to the success of financial services.
Essentially, by keeping humans in the loop, IA overcomes the shortcomings of AI. Hence, IA is also befitting of titles such as Assistive intelligence, Cognitive Augmentation, Intelligence Amplification, and Machine Amplified Intelligence.
4 Reasons Why Intelligent Automation is a Perfect Match for Financial Services
1. Helps you process tonnes of unstructured data
To succeed in financial services, you need to make accurate sense of data in the quickest possible time, identify trends, avoid errors in calculations and forecasts, provide immediate assistance to customer queries and facilitate thousands of transactions simultaneously. IA empowers you to do all of these. IA can also sift through large sets of unstructured data and organize them. Using IA, financial services can provide their customers with succinct data to help them identify an emerging pattern that otherwise might have gone unnoticed.
2. Impact of bad customer experience
Most US customers who decide to switch to competitors blame it on poor customer service offered by the banks. Increased operational costs and deputing employees to perform mundane tasks inadvertently make customer experience take the backseat. IA can help improve customer experience hugely.
3. Increased Operational Costs
A recent survey showed that about 33% of the community banks in the US spent over 10% of their budget on compliance regulations. The same report also states that only 1.8% of those banks expect their compliance costs to come down by 5% in the coming years. Rising operational expenses, along with compliance regulation fines, can cost a lot and drag down the performance of a financial institution. Intelligent Automation can help on this front to a great extent.
4. Online Banking Surge
The pandemic has catalyzed the way several industries operate. As many as one-third of banking customers worldwide depend on online banking services. MasterCard reported a 40% jump in contactless transactions in a quarter in 2021. There are multiple use cases for IA regarding online banking applications.
Implementation Use cases for Intelligent Automation in the Finance Industry
Automated Detection of Money Laundering
Automated scanning of transactions, comparison with several sets of counterparty data from internal/external sources, and identifying potentially fraudulent transactions are among the top three use cases of IA in the fintech, insurance, and banking sectors. Each year, fraudulent transactions cost financial services companies billions. Manual audits of invoices to detect fraudulent activities are similar to finding a needle in a haystack; it’s both laborious and inefficient. IA with Machine Learning algorithms is a far more effective and cheaper alternative. American Express has reaped rich dividends by implementing IA for fraud detection. With IA, the credit card giant has seen an improvement of 6% in the accuracy of fraud detection and 50x faster processing of transactions compared to their previous CPU-based system. BNY Mellon has an IA framework that has improved its fraud detection capability by over 20%.
Enhanced Financial Planning and Analysis
Successful Financial Planning and Analysis (FP&A) is integral to a financial services organization’s health. A report from McKinsey indicates that almost 60% of FP&A activities can be fully automated. A few years ago, a survey of several CFOs of financial institutions showed that 78% thought that MS Excel skills are crucial for diligent FP&A. Now that number has come down to 5%, which indicates that a majority of business leaders want their business to adopt automation wherever possible and reap the benefits it has to provide in planning, budgeting, management, performance reporting, forecasting, and modeling.
Another labor and time-intensive process in financial institutions is reconciling data across multiple systems and ledgers. Manual handling is prone to mistakes and inefficient. Tallying data, formatting it, and analyzing it might take weeks. It would be too late if the finance team finds something wrong. Using rules and patterns, ML can provide the ability to identify a large number of these reconciliations, understand the problem, and in some cases, correct the problem or flag it for human intervention so that staffers do not have through every data but just those selected ones. A system driven by IA should be able to perform reconciliation, consolidation, reporting, and closure of processes.
IA helps Automate and close books faster.
During the closing stages of a quarter, the anxiety of closing books and tallying records is enough to send the blood pressure of the Finance team into a spiral. The complexity of doing this is majorly attributed to the many systems from which the data is gathered. So, Intelligent Automation for financial services helps in Automating core transactions, workflows, and processes that will address this inefficient work style, and ensure that the correct entries are posted the first time. This alleviates the need for a high degree of manual intervention during closing. A great example is an ML-enabled anomaly detection system. It will identify potentially suspicious/anomalous transactions and automatically correct the codes or push them up a queue for review before the entries are posted.
For teams dealing with multiple disconnected systems, the tools available to automate the closure of books more efficiently can be classified into two categories: cloud-based systems and digital technologies.
With cloud-based systems, a significant advantage is the relative simplicity of deployment versus on-premise software. Cloud makes it simple to quickly scale up and add more services whenever needed. Updates are much easier to deploy and manage. Security is also a solid central point of cloud-based systems, enabling enterprises to use already developed expertise rather than create it from scratch.
Delivering Deeper and Faster Insights
Whether or not IA has a positive impact on the financial services sector depends on
1. Its ability to meet the surge in demand for data-based insights.
2. Its capabilities are organizing vast volumes of data and presenting it as user-friendly (reporting).
3. Handle a large volume of data that is becoming increasingly complex to interpret.
4. Ability to enhance visibility and tracking of processes in the industry.
5. Capturing real-time data to analyze velocity, deployment, and creating a loop for customer feedback-based decision-making and course correction.
A global CFO survey shows that 26% of organizations said that their primary criteria for implementing IA were to achieve enhanced decision-making support.
As the industry learns to embrace technology further, tasks such as manual data collection, consolidation, verification, and formatting will eventually become obsolete. In the current scenario, these cumbersome tasks are hugely time-consuming, leaving the employees of Finacial Service organizations little time for analysis. As these routine tasks become more automated, finance teams can intensely focus on value-added activities, such as risk assessments, scenario planning, predictive modeling, and performance.
Payoda has an unmatched intelligent automation delivery experience to assist with the transformation of processes while optimizing people’s performance and potential. Our specialists will provide you with a free strategic consultation on how to revolutionize your financial services organizations operate internally and improve customer interactions.