Most people have heard of Watson — IBM’s smart computer that famously beat the quiz show Jeopardy! back in 2011. Fewer know that Watson is only one of tens-of-thousands of IBM solutions distributed across the globe.
Not only do IBM’s computers have the capacity to perform complex tasks like playing a rather intense game of Jeopardy!, they’re also used to cut costs when it comes to running business infrastructures. From insurance to banking, IBM’s zEnterprize mainframe (or System Z) is touted as being the world’s heavyweight champ when it comes to crunching the numbers behind the scenes.
Memeburn spoke with IBM’s Software Group VP Ray Jones about how the global computing company is helping businesses achieve their goals better, in turn, making our planet smarter.
The most important benefit System Z mainframes bring to business is speed. More specifically, speed to solve queries faster.
For instance, when an insurance customer wants to cover his new shiny sports car. Ordinarily, the insurance provider would have to run this query through complex algorithms which could take time. And as we all know time is money.
“If a service takes too long to give its customer an answer, they’ll simply move on, and the business opportunity will be lost,” says Jones.
To improve this, Jones points to a concept called the Single Version of the Truth. This computing management concept essentially refers to centralised data hubs, is highly valued by System Z’s functionality.
“A Single Version of the Truth has a dramatic speed in which it delivers. When it comes to the point of competing with a transaction, the single version of the truth allows queries to be run within less than a minute,” explains Jones. “It boils down to consistent answers at record speed and with low cost.”
Jones uses the example of companies like Swiss Mobiliar (see video below) in Europe that found out that people will pay more to get an accurate insurance quote in time.
Insurance is only one of the areas where this system can improve company operations. A suspected 70% to 80% of the world’s business data originates from IBM’s System Z. That’s a massive bulk of information. More than we can imagine, really.
Jones explains that if so much of the world’s data gets created in real-time, running through System Z, the extent to which one can use that data to analyse and predict events and occurrences all of the sudden gets very real. For example, 35 hours to detect fraud can get reduced to less than a minutes.
Over 90% of the world’s credit and debit cards get processed through IBM’s systems.
“Usually what happens is that most financial institutions no longer rely on their systems to predict fraud,” Jones explains. Once the perpetrator gets their hands on a card, they’re off to the the races. They’re spending the card within a very brief time-span. This is too short a time for the bank to pick up the activity until it’s too late. So instead, banks use algorithms to pick up suspicious behaviour.
Jones shares some of his personal frustrations with fraud:
[Last year] I’ve lost four of my credit cards in the US. We got a call from our bank saying there’s been a suspicion that your identity might have been stolen. ‘We can’t prove it but based upon our suspicion, we’re going to cancel your card and create you a new one.’
In reference to the banks that fall victim in these scenarios, their losses are massive. Jones elaborates:
The banks have to ‘eat’ all the fraudulent payments. In the US, the average cost of one of those incidents is US$600, which includes everything from making the call to the customer, canceling the card, creating a new account and ultimately ‘eat’ the fraudulent amount. So far, my banks have spent US$24 000 on me this year.
“None of this is supposed to be happening,” Jones adds.
With System Z, one can have a program look at customer transactions in real-time, and like that, pick up suspicious behaviour which is then run through different algorithms instead of waiting hours, or days.
In case of scaling operations, Jones explains, “banks can choose to either go digital or opt for the expensive bricks and mortar approach.”
In Africa, FNB’s model is a good example in this regard. The South African banking giant has managed to easily grow beyond its borders by leveraging Africa’s prevalence of mobile phones.
Instead of constructing a new branch in a remote Mozambican town together with a couple of ATMs, the financial giant can instead scale by introducing its mobile app.
Jones concludes that instead of looking to tech to solve your business’ problems, rather seek to understand what questions to ask:
“The challenge is, at the end of the day, that all of these technologies merge. They come together. It’s about how do I, as a business, accomplish my goal? Whether that be to tailor my offerings for growth and/or lower my operating costs.”
“Cloud, in and of itself, doesn’t deliver. Analytics, in and of itself, doesn’t deliver. Mobile, in and of itself, doesn’t deliver,” Jones notes. “It’s about how you put the things together in terms of an end-to-end delivery system to accomplish your goal.”