Intelligent Automation for Financial Services Industry

Intelligent Automation for Financial Services Industry

Intelligent Automation (IA) is a powerful AI/ML capability 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.

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.

Reconciliation Automation

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.

Delivering Deeper and Faster Insights

Whether or not IA has a positive impact on the financial services sector depends on

Final Thoughts

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.

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