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4 Use Cases for Midsize Banks Looking to Implement a Modern Data Management Solution

March 25, 2021

Data has a better idea 1 - 4 Use Cases for Midsize Banks Looking to Implement a Modern Data Management Solution

The finance sector is, in large part, very welcoming of big data solutions. This holds especially true for big banks, who have the budget and the infrastructure to accommodate the most cutting-edge data management software. But the investment may not be as easy to make for midsize banks, who typically have more modest coffers and serve smaller, more localized markets. Does it make sense for these banks to spend on cloud-based data management solutions? Of what use will these be given the way midsize banks operate?

Below are four practical use cases that may serve to answer these questions. These scenarios demonstrate how midsize banks benefit from a more solid data foundation, as well as data management strategies that are up to the times.

Use Case 1: Navigating Through Cumbersome Data Silos

Many midsize banks rely on legacy data management systems that may have worked best around a decade ago. Unfortunately, those systems often inherit data silos that hamper the bank’s data management capabilities.

A data silo refers to the restriction of data to only one group or department in the organization. There are several reasons as to why these data silos occur. For one, departments may not see eye to eye about their standard for maintaining quality data. For another, different business units may also be using different applications for their data. One group may be managing their data in a less technologically sound manner than another, which creates some cumbersome discrepancies.

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Regardless, these discrepancies can take big bites out of midsize banks’ bottom line. It takes time to gather and consolidate banking data from all of these disparate sources. That also means it takes longer to achieve a comprehensive view of the organization’s financial situation. This, in turn, may cause costly delays in the bank leadership’s decision-making.

A modern data management solution will address such problems commonly associated with data silos. It will allow midsize banks to consolidate their data on one platform and get a 360-degree view of where they stand in near-real time. Departments can adhere to a single source of truth, as well as a uniform standard for managing banking data, and be in sync. For midsize banks, that added agility with data will count for a lot.

Use Case 2: Streamlining an Increasingly Wide and Diverse Pool of Financial Data

Midsize banks may not have the same reach or the same powerful infrastructure as their larger counterparts. But big banks and midsize banks alike need to clear the challenge of handling increasingly large and complex pools of financial data. That’s a problem that a modernized data management solution can tackle for both of them.

If you work in a midsize bank, you may notice that even your customers from earlier generations are seeking more hybridized banking experiences. They may be leaning just as heavily on digital banking services as they do on face-to-face banking. As a result, their data profiles may not be as simple as they were before, and will definitely not be as simple to handle.

Your bank will need to meet challenges like these by onboarding more flexible and more robust data solutions. These will make the process of handling such a large and complex pool of data less burdensome on you. It will also make the prospect of growth a less frightening one for your midsize bank.

Use Case 3: Improving Enterprise-Wide Reporting on Risk and Treasury

Another key application of modernizing data management solutions for midsize banks is increased mastery over treasury and risk management. A more up-to-date data solution can make a midsize bank even better at balancing their sheets. The software can help staff manage data on cash flow, accounts payable, accounts receivable, and other matters pertaining to treasury.

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A more up-to-date data management solution can also help a midsize bank adopt less passive, more proactive approaches to risk management. It will be easier to cull data on assets, liabilities, and equities, as well as to identify imminent financial risks to any of these. If this software is being used by your own midsized institution, it can help you flag potential credit risks or liquidity risks to your bank’s assets.

Use Case 4: Meeting Goals for Financial, Regulatory, and Risk Management Reporting

The last use case for a modern data management solution pertains to meeting the standard for financial reporting, especially to regulators. It must be noted that demands from regulators are never static. Regulatory bodies that govern midsize banks will likely become increasingly stringent about the latter’s data management capabilities, especially for issues like anti-money laundering (AML). They may also issue directives that are a response to the times, for example regarding the US’s Coronavirus Aid, Relief, and Economic Security Act (CARES). In this case, midsize banks will have to consolidate and report data on any heightened lending activities as a result of the COVID-19 pandemic. They will also have to report the risk management protocols they’ve implemented to mitigate risks brought about by this activity.

An upgraded data management solution will help your own institution stay in the good graces of your regulators. It will kickstart succinct, accurate, and timely financial reports of a standard that’s easy to replicate. Today’s solutions come with appealing templates that staff can simply “plug-and-play,” or customize for the occasion. All in all, the bank will be able to compile inclusive financial reports, in a quick and efficient manner, thanks to its new solution.

Final Words: The Promise of Big Data to Midsize Banks

In summary, midsize banks have quite a lot to gain from modernizing their current data management strategies. But this isn’t the kind of change that has to be overwhelming. A bank can adopt a data management solution that’s of the right scale, tailored to its own needs. This will allow it to operate with the same efficiency—and profit with the same consistency—as a bigger bank. At the same time, it can keep the size of operations and the homegrown appeal that’s unique to a midsize bank.

If you are a decision maker in a midsize bank, hopefully you’ve come away with a better understanding of how such solutions benefit you. Consider modernizing your own data management capabilities, and be better equipped to respond to the big data revolution in banking.


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