“It would be rude and ungrateful for me to ask for more than I have now.”
Every Monday morning we fire five questions at a senior sector executive. Today we’re joined by Dr Mark Goldspink, CEO of The ai Corporation, a payments security specialist.
Mark – What’s the Biggest Challenge for Your Clients?
The biggest challenge is marrying up the rapidly changing digital world with end of life back office systems. We liken the challenge to having a ‘Bentley’ digital front office, driven by a ‘tractor’ back office system.
Essentially large global businesses are having to force the new and old worlds together, which is creating a considerable amount of business transformation for organisations to remain competitive. A great example of this change is in the retail sector, where self-service points of sales are now standard.
The number of digital payments being made across the globe is increasing dramatically, by default this means that back office processes (such as fraud management) need to be constantly refined. This is fully supported by the fact that fraud has reached the highest levels on record, affecting more organisations than ever.
The scale of the problem was revealed in last year’s PWC Global Economic Crime and Fraud Survey, where nearly half (49 percent) of the 7,228 businesses across 123 territories that were interviewed reported that they had experienced fraud and economic crime over a two-year period.
Today, fraud management consists of several manual processes. Models and rules performance monitoring, fraud pattern discovery and fraud alert management to name a few. While these manual processes may be manageable at first, as the number of payment types and channels increase, it can rapidly become untenable to add more and more staff to manage and monitor those processes. Managing fraud can become very expensive, which is why efficient management processes are so important.
Technology that Excites You Most?
Machine learning has always excited me.
Today machine learning is enhancing many of the things we do. From helping us to determine a good deal on a car, to aiding more behind the scenes processes – such as payment fraud detection within banks.
However, while machine learning is being used extensively, it is not being used efficiently or, indeed, to its full potential. In many automation projects, the machine learning component usually only constitutes a small portion of the overall process, which is then completed manually by rapidly expanding support teams.
There is no doubt that machine learning provides additional value to many in the enterprise, but many organisations do not yet realise that by continuing in this manner, profits will eventually plateau or even run at a loss as the cost of running the system outweighs the gains.
There is an enormous amount of effort that goes into managing machine learning systems, often by very large teams. In some cases, teams are working round the clock to ensure a system is up to date by attempting to maximise a system’s effectiveness by using the latest data trend changes. This work is often repetitive and frequently requires input from several teams, before a process is complete and can be used in a live environment. This slows the process down, leading to potential loss of profits, both through lost sales and operational losses.
As stated previously, fraud has reached the highest levels on record, affecting more organisations than ever in 2019.
I believe there are 2 main reasons for this issue:
1: Fraudsters are now highly organised, agile and very innovative. Due to different business practices means that fraudsters can quickly exploit process loopholes.
2: Currently there are still too many manual processes, leaving which leaves process loopholes.
The thing I am proud about is our team and how we have been trying to explain that it is not the technology that is at fault but the process loopholes that need to be addressed. As a result, our team of ML experts and data scientists designed AutopilotML which replaces the end to end manual processes involved in fraud management. This drastically reduces the amount of repetitive and expensive work our users were being tasked with doing, allowing these teams to focus on more valuable activities.
As an ex-research scientist ‘failure’ is not something I recognise.
I have never been someone who has not had to work hard and learn from my mistakes. However, I am lucky my failures are things that I could overcome and recover from quickly.
My biggest failure [was] when I was younger and that was learning that practice really does make perfect.
One of my favourite quotes is from the golfer, Jerry Barber. Someone watching him play said, “Gee, you are a lucky player.” “Yes, I know,” he replied. “And the harder I practice, the luckier I get.”
In Another Life I’d Be…
It is difficult for me to answer this question as I have been ridiculously lucky. It would be rude and ungrateful for me to ask for more than I have now.