For Usable Insights, You Need Both Information and the Right Analytical Engine

“It’s all about the information!”

Chances are you’ve heard this before. If you are a Ben Kingsley or Robert Redford fan you may recognize the line from Sneakers (released in 1992). Yes, 1992. Before the World Wide Web!  (Remember, Netscape didn’t launch the first commercially successful Web browser until 1993).

Actually it’s always been about the information, or at least the right information – what’s needed to make an informed decision, not just an intuitive one. In many ways the information, the data, has always been there; it’s just that until recently, it wasn’t readily accessible in a timely manner.

Today we may not realize how much data is available to us through technology, like the mobile device in your pocket – at 12GB an iPhone 6S is 2,000 times bigger than the 6MB programs IBM developed to monitor the Apollo spacecrafts’ environmental data. (Which demonstrates the reality of Moore’s Law, but that’s another story).  Yet because it’s so easy to create and store large amounts of data today, far too often we’re drowning in data and experiencing information overload.

Drowning in Data

Chances are you’re reading this in between deleting that last email, before your next Tweet, because the conference call you are on has someone repeating the information you provided yesterday. Bernard Marr, a contributor to Forbes, notes “that more data has been created in the past two years than in the entire previous history of the human race”.  Marr’s piece has at least 19 other eye-opening facts about how much data is becoming available to us, but the one that struck me the most was this one:

#20   “At the moment less than 0.5% of all data is ever analyzed and used – just imagine the potential here”

0.5%! Imagine the opportunities missed. Just within the financial industry, the possibilities are limitless. For example, what if the transaction patterns of a customer indicated they were buying more and more auto parts as well as making more payments to their local garage (or mechanic). Combined with a recent increase in automatic payroll deposits, might that indicate this customer would be a good prospect for a 0.9% new car financing offer?

Or imagine the crises which could be avoided. Think back to February 2016 and the Bangladesh Bank heist where thieves managed to arrange the transfer of $81 million to the Rizal Commercial Banking Corporation in the Philippines. While it’s reasonable to expect existing controls might have detected the theft, it turns out that a “printer error” alerted bank staff in time to forestall an even larger theft, up to $1 billion. The SWIFT interface at the bank is configured to print out a record each time a funds transfer is executed, but on the morning of February 5 the print tray was empty. It took until the next day to get the printer restarted.

The New York Federal Reserve Bank had sent queries to the Bank questioning the transfer. What alerted them? A typo. Funds to be sent to the Shalika Foundation were addressed to the “Shalika fandation.” The full implications of this are covered in WIRED Magazine.

Analytics, Spotting Problems Before They Become Problems

Consider the difference if the bank had the toolset able to flag the anomaly of a misspelled beneficiary in time to generate alerts and hold up the transfers for additional verification. The system was programmed to generate alerts as print-outs. It’s only a small step to have alerts like this sent as an SMS text, or email to the bank’s compliance team, which may have attracted notice sooner.

To best extract value from the business data available to you requires two things: An engine and a network. The engine should be like the one in OpenText™ Analytics, designed to perform the data-driven analysis needed.

With the OpenText™ Analytics Suite, financial institutions can not only derive data-driven insights to offer value-added solutions to clients, they can also better manage the risk of fraudulent payment instructions, based on insights derived from a client’s payment behavior. For example, with the Bangladesh Bank, analytics might have flagged some of the fraudulent transfers, to Rizal Bank in the Philippines,by correlating the fact that the Rizal accounts were only opened in May 2015, contained only $500 each, and had not been previous beneficiaries.

Business Network: Delivering Data to Analytical Engines

But the other equally important tool is the network. As trains need tracks, an analytical tools engine needs data (as well as the network to deliver it).   Today more and more of this data needed to extract value comes from outside the enterprise. The Open Text™ Business Network is one way thousands of organizations exchange the data needed to manage their business, and provide the fuel for their analytical engines.

For example, suppose a bank wanted to offer their customers the ability to generate ad-hoc reporting through their banking portal. With payment, collection, and reporting data flows delivered through the Open Text Business Network Managed Services, the underlying data would be available for the bank’s analytical engine.

Obviously much of the data involved in the examples I’ve provided would be sensitive, confidential, and in need of robust information security controls to keep it safe. That will be the subject of my next post.

Mark Mixter

Mark is a Solution Consultant at Open Text and a relentless client advocate with 18+ years experience designing, implementing, and managing Integration Solutions for the Financial Services Industry. His expertise is in the area of: Corporate to Bank Connectivity and Integration, Global Treasury and Cash Management, Global Product Management, and Global Project Management.

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