Fighting fraud is a fast-paced business, with fraudsters working 24/7 to exploit weaknesses in merchants’ systems. The ‘good guys’ need to innovate at the same rate and, most importantly, increase the agility and speed of development in software engineering, write Risk Ident’s Felix Eckhardt and Piet Mahler
Fraudsters do not operate within boundaries, nor are they the masked criminals depicted in stock imagery across the world’s media.
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They are, however, professional entities, operating with one sole aim: to extract as much money from transactions as possible, in as quick a time as possible. To beat them, we also need to work towards a single goal and match their freedom to innovate.
Anti-fraud technology should be 100% customer-centric, and a quick time-tomarket delivery ensures this approach is more effective, not only in markets that fraudsters target across e-commerce, telecoms and financial services, but in new, emerging markets around the world.
To serve these markets with industry-leading fraud prevention, software engineering requires bright minds that bring the right mindset and the right skillset to further develop innovative products for the industry.
Fortunately, engineers are problem solvers – their curiosity is something you cannot teach, but which is invaluable to anyone working with data and a constantly evolving foe.
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Across the tech development community today, many businesses are inspired by the Spotify model of development, but apply their own version of it to suit their targets.
We are big fans also of the OKR mindset – objectives and key results – inspired by Intel and Google. In combination, this approach to team structure enables developers to forge a shared commitment to achieving specific goals, which studies have shown help improve productivity and performance.
Simply speaking, it creates an ambitious yet achievable objective, and data scientists and software engineers have complete freedom in how they achieve their targets.
Defining these targets in our industry is based on the experience and knowledge of fraud experts, combined with regular, honest, unfiltered feedback from merchants on the challenges they are facing.
It is not always fraud
Fear of fraud can be just as damaging as fraud itself. False positives, where a retailer’s anti-fraud setup incorrectly determines that a genuine purchase attempt is fraud, costs the industry millions in lost revenue each year.
It is possible to accurately identify the genuine fraudsters through a number of
techniques, including device identification and by linking and analysing multiple data
points. The scale of this makes it next to impossible for fraud managers on their own, but incorporating machine-learning into software helps them analyse and refine their fraud cases so that defences get stronger over time. It is this approach that is key to staying one step ahead of the fraudsters.
Think like a fraudster
Benedict Cumberbatch is an inspiration to so many fans around the world, including us. It is not hard to see why, when you consider his world-famous on-screen portrayal of Sherlock Holmes.
The English detective employs a mnemonic memory technique, known as his Mind Palace, where he stores each detail of every case, linking and carefully analysing them to deduce the true story behind the crime.
Visualising the potential steps of the criminal is crucial here. Fraud, for example, is never just a single transaction. Fraudsters do not simply target one victim, succeed, and then retire. So, there is a whole journey here to identify their process and beat them to it with a trusted, reliable defence.
They are organised, even attending their own secret ‘conferences’ to share best practice, and they have a growing information pool at their fingertips with the advent of the dark web and repeated large-scale data breaches, such as those recently highlighted by Ticketmaster and British Airways.
If you are to understand and trace the actions of the perpetrator, you need to step into their shoes and think like them: what is their goal? what is their skillset? what is their mindset, and what identifiers might they hide or accidently leave behind?
Apply this approach to fraud and you have a far more effective defence. We are in a technology arms race, and knowing what the enemy is doing gives you an advantage in each battle. It is equally important to learn from your mistakes – also a Sherlock Holmes trait.
The future of prevention
Fraudsters’ tactics change over time, as they aim to get through defences before they are noticed. But the industry in which we operate is also changing, fast. Consider the last time you interacted with a loan provider, an online retailer, or even your cellphone company – it is likely you did so online.
Our embrace of digital has pressured companies to deliver products and services faster than ever. Whether providing digital or physical goods, there is now an expectation for a quick and seamless payment experience, followed by near-instant dispatch of goods. This has significantly narrowed the window in which merchants can confirm whether a transaction may be fraudulent, preventing valuable goods coming into the hands of fraudsters. Too many delays and you risk the customer leaving and never coming back. It is a balancing act.
So, we need to enable quicker fraud decisions and improve response times for active payments processing. Fraud managers cannot do so alone, but are a key part of the equation. Machine learning alone cannot function without the same specialist knowledge of the company fraud officer(s) either. But combine the two, and you have a winning formula.
