I continue to be surprised by the lack of analysis and focus on operational efficiency when it comes to understanding all aspects of fraud prevention. Organisations spend a lot of time on improvements and removing ambiguity within their teams. It is a constant, ongoing activity, across all businesses. The same needs to be true with fraud, and I believe removing team member ambiguity needs to be a focus for any organisation which wants to optimise stopping fraud.

Over a significant time spent in the fraud industry, I have seen the tools in the market place improve dramatically. Fraud prevention technology is extremely advanced.  Yet, the combination of man and machine is still creating false declines, to the tune of $118bn/year in the USA (Javelin Research), as reported in 2016. Whereas the actual picture was an overall fraud loss of $9bn, nearly thirteen times less.  Doesn’t the process look broken?

If we break down the process, there are typically three operational options that organisations follow to stop fraud: 

• Hybrid – This tends to be the typical operational option in the market place, with the vendor helping to generate the fraud strategy and by doing modelling. Model/rules sets are created and implemented by the vendor, and yet the client is left with the consequences. The downside to this model is very simple. Who is accountable?  It is also time consuming (not real-time enough) and more expensive.
• Self-Service – This is where a client is trained to operate the fraud tools. To execute this well, the tools must be truly self-service, so the client is not reliant on the vendor having to implement or build models/rules. The downside to this option is ensuring that the client is use the technology fully. To do this well, vendor management programmes are required to be performed by the client. However, the client is in control, has full accountability and can react immediately to fraud problems.
• Out-Sourced –In this outcome-based option, it means that the vendor is fully responsible for defining and implementing the fraud strategy for a client, based around clear key performance indicators. As the vendor has built the technology, then they understand its capabilities, and potential imitations, better than anyone.  The perceived downside to this option is that the client is not in control. The vendor is fully accountable. However, if the client sets the key performance indicators for which the vendor must operate –isn’t the client in control? 

Selecting the correct operational option

All the above operational models can be made to work. My team, who have been helping clients with their fraud management strategies over many years, prefer a blend of a self-service and out-sourced approach. This is because accountability is very clear. In the case of self-service, the best anti-fraud tools have been designed to be self-service based on the principle that anyone could use them and that they offer zero ambiguity.

As payment fraud is a global problem, an out-sourced service allows organisations to benefit from a worldwide view of fraud across their vendor’s international client base. This sort of service can be technically distributed via the cloud, hence avoiding major upfront IT costs.

Why zero ambiguity is key

As well as focusing on the tools used, it is important, when reviewing your fraud prevention management, that the operational processes are designed for zero ambiguity. There is no point in paying for the best tools, if they are not being deployed in the most operationally efficient manner.  

Our research into behavioral economics shows that as humans, certain biases (decision fatigue, inattentional blindness, and herd instinct) impact our ability to operate perfectly. For example, expecting fraud teams to work through thousands of pieces of data and be spot on every time, is simply not practical.

Machines need to be allowed to lead some stages of the tedious, repetitive processes – releasing human creativity. As a result, I believe that there is a role for both humans and machines in the world of business.

Instead of man versus machine, I believe the approach should be man AND machine. A.I. will release human creativity and that freedom will, in turn, allow humans to realise the potential of the technology and its application.

Using best-of-breed machine learning technology, many businesses have significantly reduced the amount of time it takes to analyse data and have provided increased accuracy in fraud detection and a reduction in false positive rates, meaning less declines and more transactions – greater acceptance.

However, the role of the human, interpreting, analysing and understanding is still key to the process – that is why there needs to be zero ambiguity.