Voice of the Industry

Fighting fraud on all fronts with custom fraud modelling & machine learning

Monday 8 October 2018 08:44 CET | Voice of the industry

Amador Testa of Emailage explains the role of specialised fraud modelling, custom fraud modelling and network intelligence in providing robust, real-time fraud prevention

With access to faster and cheaper computing, fraudsters have shifted their targets to more profitable weaker points in almost every vertical.

It’s wise to expect every part of your process to be continuously and thoroughly examined for potential weaknesses and opportunities. If, and when, weaknesses are found—you can bet word will spread like wildfire on fraudster communities and forums. Of course, any fraud solution you choose should be adaptable for your business case. Examples include your transaction types, customer profiles, and which regions you operate in.

But today’s world requires you to be able to quickly identify and stop risky transactions, while still approving the majority of your good volume. Here is how you can employ custom fraud modelling to do that.

The role of network intelligence

Network intelligence offers a holistic view of the space you operate within. But not all networks are equal. It’s important to differentiate siloed networks against those which rely on crowdsourced data.

Operating in a siloed network will only get you halfway there. Fraudsters rarely only focus on one merchant, industry or vertical. Instead, they hit as many as they can at once.

There are a lot of benefits in knowing what your peers are facing. Intelligence around fraud events allows you to identify risky behaviours faster. Then, you can react before they become a problem. Theres a flip side, too: the same process works with legitimate customers. When you can identify positive behaviours, you can quickly approve those customers.

By unlocking the global intelligence in the network, our decision science team can build fraud models which are tailored to identify the latest fraud patterns across geographical markets, transaction types, card products, and segments. Models are carefully segmented to ensure that they can uniquely capture both local and regional fraud trends and transaction patterns.

Custom fraud modelling

Crowdsourced intelligence lends a wider and more operational view. It also provides a wider variety of elements for mapping custom fraud models. Custom fraud models give you the coverage of industry and peer trends, allowing comparison against your own transactions. Therefore, when choosing a provider, make sure you assess the ability to offer customised models. The next step is adding machine learning capabilities to update those models in a real time.

Machine learning uses data history to create a fraud model that is deployed against current transaction activity to highlight suspicious transactions compared to model data. This approach ensures fraud models are timely and current for optimal fraud detection — enabling you to frequently fine-tune the performance of your fraud model by adjusting rules and parameters.

It’s important to establish feedback loops into the process to ensure that it can monitor model performance over time, and proactively work to refresh the fraud model as fraud patterns change. This provides robust, real-time fraud prevention controls at both ends. You can identify (and stop) relevant trends, patterns and behaviours. As with network intelligence, this process also extends to low-risk transactions and good customers.

Specialised fraud modelling

To face todays threats, you have to take it to the next level: specialised fraud modelling. Specialised fraud modelling is exactly what it sounds like — the process of creating unique models to score your unique transactions.

Here’s how it works: network and industry events are inputs for customised fraud modelling. Then, with this intelligence, you can develop specialised models for your business.

For example, peak spending seasons vary by country. Data has shown that in Europe, card usage may peak in the summer. In the US, the peak season is centered around the months of November and December. You can tune your specialised models to expect higher transaction volume during the typical peak season period for the country or region, which helps control false positive rates

The benefits of this approach are twofold: you are getting the best outside data while utilising it in a way that is tailored to your business.

In conclusion

Fraud is a difficult behaviour to define, and even more difficult to predict. Fraudsters are constantly changing their methods, making patterns that are hard to establish.

Every company has a different procedure for processing transactions, and accordingly, fraud patterns are going to be different for each. This clearly calls for customised strategies for predicting and preventing fraud.

In this day and age, companies need to build fraud solutions that maintain consistent performance over time. The solutions should include multiple types of data, including a variety of consistency checks that can work to verify digital identity.

About Amador Testa

Amador is a fraud prevention expert with extensive experience in leading product management and strategy to combat fraud. He is an industry leader in online fraud, identity theft mitigation and cybercrime investigations.



About Emailage

Founded in 2012 and with offices in Phoenix, London and Sao Paulo, Emailage is a leader in helping companies significantly reduce online fraud. Through key partnerships, proprietary data, and machine-learning technology, Emailage builds a multi-dimensional profile associated with a customer’s email address and renders a predictive risk score.

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Keywords: fraud prevention, machine learning, fraud modelling, Amador Testa, Emailage
Countries: World