Whilst most businesses don’t earn revenue by processing data, they do spend a large amount of their hard earned revenue in manually processing data, validating it and ultimately performing manual tasks that don’t scale.

But at what point does this manual involvement become a burden of cost upon your business? And really how much manual involvement should be required?

Take payment fraud for example. According to the 2015 Merchant Risk Council (MRC) Global Fraud Survey, merchants typically manually review 10-15% of online orders. Other reports suggest that as many as 26% of eCommerce orders are manually reviewed.

Whilst these figures understandably vary between verticals and online merchants increasingly feel that they have fraud itself under control, the Cybersource 2016 UK eCommerce Fraud Report reveals that reducing manual review has become their greatest fraud challenge. Leaving an order ‘sitting’ in the queue for hours or even days results in poor customer experience, potentially causing order cancellations and affecting your key metrics including customer lifetime value and net promoter score.

Even if reviews take place post-transaction as is common in the on-demand space, long manual review queues and an imbalance between customers accepted and declined after manual review are red flags for high operational costs and inefficiency.

Assessing The Cost

Today, surprisingly the cost of manual review is often overlooked. By tracking the number of manual reviews your business conducts each month, alongside the average manual review time and combined with the cost of labour, you can work out the total cost of manual review.

There is an abundance of advice available online on optimising the efficiency of the manual review process, from ensuring analysts and customer service agents can review orders efficiently by making sure they have one screen where they can access all available information on a customer, to prioritizing orders based on time-sensitivity or customer status.

However, whilst these basic operational improvements and investing in improving review workflow tools can reduce the time and money spent internally on manual review, they address the symptoms rather than the cause of the problem and therefore largely miss the point.

Although it only takes an average of 5.6 minutes to review a suspicious transaction (MRC Global Fraud Survey, 2015), time spent on manual reviews remains one of the greatest concerns to eCommerce merchants, especially in regards to fraud.

Most often, the problem is not the time taken to review each order, but rather that too many orders are being reviewed. The biggest gain in time and money saved can be made by cutting down the number of transactions sent through manual review in the first place.

Manual review is most effective as a last resort and you should only have to manually review orders that are difficult to decide on. The goal, therefore, is to have as close to a 50:50 split as possible between manually allowed and declined transactions. If your business has a 1% rate of chargeback fraud (and assuming you do not automatically decline any orders), you should not be reviewing more than 2% of your transactions. However, merchants often report allowing the vast majority of manually reviewed orders, a sign that operational resources are spent unnecessarily and genuine customers are forced to wait in the queue. The underlying cause of this is that your fraud detection and decisioning tool is not performing as well as it should.

Where Rules Fall Short

Achieving the desired 50:50 split is a challenging task when relying on a rules-based scoring method. An effective rules based system takes time and expertise to build and does not scale well, as constant rule changes are needed to deal with peak seasons and evolving patterns of fraud. We often hear how growth through new channels and markets is also held back by fraud risk and the time it takes to find the right rules to beat fraudsters.

What you need is a smarter scoring method, one that makes the right decisions in real time and evolves without the need for constant rule changes. This is one of the reasons why an increasing number of merchants are recognising the benefits of machine learning in decisioning. While machine learning has until recently only been available to large corporations with the budgets to hire teams of data scientists, cutting-edge fraud prevention solutions have now made this technology available to most merchants.

Machine learning models have the capability to learn from similar companies across their network. This means that even if you are processing a relatively small number of transactions, you can take full advantage of the large data sets for your vertical to improve decision accuracy. These models can also be programmed to provide a fraud score for each user from the moment they sign up and updates with each interaction they make with your platform. This score is simply the percentage probability of a customer with given attributes being fraudulent, based on the historical data of both your own customers and those across the wider network. With this knowledge, decision thresholds (allow/review/prevent) may be set to minimise the number of transactions that are unnecessarily reviewed. For some businesses, especially those that see a high volume of low value transactions, scores generated by machine learning models might even be accurate enough to skip manual review completely.

However, for most merchants, manual review still has an important part to play in the fraud prevention process. Manual review can confirm and correct decisions machine learning models fine-tune their decisions and detect changing patterns of fraud. Machine Learning models learn continuously from each decision fed back to them, making them better prepared to deal with similar cases in the future, and automatically bringing you ever closer to the optimal 50:50 split between orders accepted and declined after manual review.

Like this article? Subscribe to our weekly newsletter to never miss out!

Previous post

Big Data Is Revolutionizing The Way We Develop Life-Saving Medicine

Next post

How Deep Learning is Personalizing the Internet