You don’t have data management challenges — you have data automation opportunities!

Last week, I met with a customer who lamented a banking data problem and expressed frustration that his data management team reported some data management challenges for banking. It reminded me how Albert Einstein famously said, “If I had an hour to solve a problem, I’d spend 55 minutes thinking about the problem and 5 minutes thinking about solutions.”

What do common financial data management problems look like?

Exponential growth: More personal, customer, and financial data exists today than ever before, and it’s growing constantly. As reported by Inside Big Data, experts estimate the size of the digital universe is doubling every two years (at least), which has created roughly a 50-fold growth from 2010 to 2020. Human- and machine-generated data is experiencing an overall 10x faster growth rate than traditional business data, and machine data is increasing even more rapidly, at 50x the growth rate.

Storage: Big data challenges for banks start with the way information is stored — in dozens of disparate systems across a typical bank. Customer data, account data, transaction data, and market data come in different forms that make it extremely difficult to consolidate into a unified operating picture.

Outdated infrastructure: Legacy and mainframe systems have proven extremely difficult to shed. Much of customer and account data is located in decades-old systems that make it difficult to provide modern services demanded by customers, such as instant account opening and same-day loan approvals.

Data synchronization: Synchronizing banking data frequently is critical to maintaining strong risk controls within your organization, but a proliferation of bad data creates problems down the line.

Unstructured information: ID cards, birth certificates, articles of incorporation, contracts and emails all contain relevant data required for decision-making, but are all unstructured documents. Getting data out of such sources requires tedious and time-consuming manual work.

Mistakes: Data management challenges are also rooted in simple human errors. People make mistakes when entering financial data which are very difficult to identify. If you do decide to go forward with that tedious and time-consuming data-entry work, mistakes are likely. Many different people within your back office are part of customer-lifecycle management and it’s easy to fumble entries and make mistakes.

Consider an ideal financial services data management solution

Imagine our friend the bank executive could stanch the exponential growth of data, rip out all outdated systems in the bank, unify and structure all data, and eliminate all user mistakes. None of us will live to see this utopian world — but just imagine it!  But even if we could accomplish it, there would still be two big issues:

  1. There are tons of third-party data that we can’t control.
  2. Clean data is just clean data — it doesn’t actually solve the problems your customers care most about: instant account opening, same-day loan approvals, fraud protection, and not having to fill out hundreds of forms yet again.

Therefore, I would tell Mr. Customer that it seems obvious to me that these actually aren’t big data challenges for banks… these are automation opportunities merely masquerading as data management challenges! See answers through the lens of opportunity:

How to handle mass amounts of data exponentially growing on an annual basis?

Automate ingestion of this data and build machine learning classifiers that can quickly parse and sort through it.

How to unify data across dozens of disparate systems?

Automate data migration through APIs or RPA depending on whether it’s an ongoing need to move data, or a one-time job.

How to get rid of a mainframe system that no one born within the past 50 years knows how to maintain?

— Automate solutions that make them obsolete and they will quickly fade out of existence.

How to structure unstructured data?

Leverage machine learning information extraction models to pull out key information and plug it into a data operating model.

How to avoid data-entry mistakes?  

Reduce dependency on people through automation: Move people from the center of the process to its edges by automating manual processes, so they only need to tune and check the software robots.

And how to leverage this data to make an impact your customers will actually care about?

Build end-to-end automated business processes that ingest data, clean it, move it between systems, and orchestrate the entire Account Opening, Loan Approval, and AML processes within your organization.

Reframe the question & discover Intelligent Automation solutions for banking

If you think traditionally, you may see this as a data management challenge. So you’ll spend years trying to unscrew a bank that spent decades getting screwed up! In other words, you’ll have nothing to show for many lost months besides grey hairs and an expanding waistline.

But if you expand your thinking toward finding a big data automation opportunity, you can save millions of dollars in costs and make a major impact on the people that actually matter: your customers.

Check out these real-world examples and results!

And with the right Intelligent Automation solution you can do it all in 6–12 months, just in time for your next promotion!

In both the short and long term, we argue it’s faster, simpler and more cost-efficient to choose a full process automation solution. WorkFusion’s  Intelligent Automation Cloud includes all the technology components needed for automation in a single unified platform.

If you’d like more information or a demonstration, please contact us.

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