Bank fee analysis remains a topic that is mundane yet extremely vital for treasurers. Essentially, bank fee analysis involves taking a meticulous look at your current and future fee structure from the relationships you have with banks and attempting to mitigate fees where possible. Traditionally, it was executed manually by generating reports and forecasting from an “x-over-x” amount of time. What this provided was holistic visibility over just how much money you are spending and will spend in the future on bank fees. Seemingly redundant, the fees can snowball into hefty sums if left unchecked or if the process of analysis was stagnant.
You can imagine how the traditional vanilla process was not the most inviting from an operational point of view. Valuable man-hours and the absence of technological precision were potential impediments, and important ones at that. As technological innovation continues to sprout, treasurers can ditch the quill and ink, and allow modern technology to act as a figurative sidekick.
Current bank fee landscape
As mentioned, companies that practice manual bank fee analysis are likely haemorrhaging time and money. According to a recent FIS market report titled “Bank Account Management Report: Complex, Manual Processes Leading to Increased Payment Fraud Risks and High Costs”, 200 corporate treasurers were surveyed across the globe. In this survey, 64% do not use any automation tool to analyze statements and 52% are not receiving an analysis file at all. Some 37% of fees for services are managed externally.
Corporations that don’t utilize emerging technology can be over-charged with no real way to analyze fees, compare banks and gain visibility. Current treasury systems that incorporate bank relationship software, like BELLIN tm5, have intuitive bank fee analysis integrations that are constantly evolving and utilizing such technology. What future innovations are brewing in the pipeline regarding treasury technology?
The emergence of new advanced analytical technologies, via a centralized platform such as machine learning, artificial intelligence (AI), and automation, would allow corporations to offer even better pricing efficiency and reduce potential spending. The increase in competitiveness will reduce the need for traditional bank fee analysis methods, yet still allow banks and treasurers to increase revenue from the customer lifecycle.
Machine learning allows for opportunities in analyzing large volumes of context-driven data sourced from multiple parts of the organization, such as sales and marketing, or external indicators from government sources. Large volumes of data were a burden in the past from the sheer amount of time needed to dissect it. With machine learning, those large volumes of data have become valuable input which provides further precision when treasurers are looking to mitigate extraneous banking fees or improve their products.
Another way that machine learning is advancing treasury services is regarding pricing strategies. By using that large volume of raw data, treasurers can price down their services to a granular level and machine learning can be used to establish company-wide benchmarks that give relationship managers the means to identify opportunities for new products, or areas where customers are being charged above the benchmark.
Fundamentally, machine learning yields industry-transparency, which in turn, sparks competition. Traditionally, a market oozing with competition cultivates two fundamental concepts that are especially fruitful for consumers: the yearning for companies to improve products while undercutting the price of their market competitors. What machine learning provides is a means for treasurers to gain market data to introduce new banking products, greatly minimize their banking fees, and offer those products at competitive rates. A financial win-win for providers and consumers if you will.
On the corporate side, bank fee analysis tools making use of AI can quickly identify exceptions based on historical volumes and fees charged per service. Additionally, this type of analysis can be performed within a few minutes as opposed to hours of manual effort investigating bank fee reports.
A centralized platform that utilizes machine learning offers a host of benefits including:
- Mitigating payment fraud
- Improved organizational and operational efficiency
- Transparency over workflows
- User tracking
- FBAR and compliance reporting
- Autonomously analyze and compare expected bank fees
- Real time tracking of market trends
- Month-over-month and year-over-year analysis
Taking a closer look at bank fee analysis, it becomes apparent that manual processes not only cost treasurers a great deal of time, but the precision and availability of data leaves much to be desired. Current technological solutions have remedied those past issues and companies are diligent in spearheading future innovation. With machine learning, AI and automation, a centralized platform that provides bank fee analysis is going to have an even larger role within treasury and accounting departments.
About the authors
Marcin Cichon is one of BELLIN’s Treasury Consultants in North America and is responsible for implementation and consulting projects of multiple multinational corporates – focusing on cash management and payments implementations. He holds a degree in Finance from the University of British Columbia (UBC) and is a Certified Treasury Professional (CTP). Ron Jaradat, content marketing specialist at BELLIN, holds a BA in Journalism with Public Relations collateral from California State University Northridge. Ron joined BELLIN in August 2018 and his areas of focus are content development, web analytics, and search engine optimization. Ron’s past positions allowed him to gain experience within multiple industries like gaming, automotive, tech, and investor relations.