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August 06, 2018

The benefits of using artificial intelligence in payment fraud detection: A case study

Abstract

This paper presents a case study on the use of advanced artificial intelligence (AI) for the detection of payments fraud. The process applies AI within a typical online payment environment to detect fraudulent transactions in real time. The design focuses on an effective supervised learning engine with a data analytics component to support high-performance fraud detection, improving the predictive value of the original data. The design exploits the discriminant properties of customer data by finding hidden patterns.

This feature significantly improves fraud detection rate and performance stability compared with a rulebased solution. The developed solution, based on an advanced AI-based technology and platform increased fraud detection rate from 85 per cent to 90 per cent (in terms of number of transaction records) and to 95 per cent in related amount volume (in terms of transaction value), while the alert rate (the percentage of daily transactions investigated manually) was reduced from 40 per cent to 10 per cent. The solution falls under the category of explainable AI because it can explain the rationale behind the decisions.


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February 01, 2016

Payments Cards & Mobile magazine spoke to Cristina Soviany on how machine learning can be used to derive intelligent actions for the business within the data-rich payment industry

Payments Cards & Mobile - a leading magazine for global payments news, has asked Cristina Soviany - CEO and VP R&D of Features Analytics, to talk about the applications of machine learning and big data to the financial industry. The article is entitled "Behind the Big Data hype" and it appeared in the Jan/Feb 2016 issue. In the article it is discussed how the data rich payment industry can use now machine learning to derive intelligent actions for the business.

In the article section entitled: Does the size matter? (pages 20-22), Soviany answers the question: "When it comes to data, is it really a case of bigger is better, though? How much data is enough?"

For Cristina Soviany, CEO, Features Analytics, a Belgian firm specializing in machine learning technologies, huge quantities of data alone is not enough to build accurate predictive models. Soviany notes that sometimes one can get by quite well with limited or small amounts of data. "The answer lies in the quality of data combined with the ability to enhance it with the right features, or sets of variables, able to detect hidden patterns but also evolve with the data," she says. When Features Analytics builds models to detect payment fraud, having the right type and amount of historical data is important, ideally 12-18 consecutive months of transaction data. The data also needs enough statistical coverage. "In the case of payment fraud solutions, we are used to building models where the fraud class size is 0.005 percent to 1 percent of total transaction numbers," explains Soviany. "If the volumes of data are large enough - more than hundreds of millions of transactions or samples, for example - and if the above percentages hold, then we have enough statistical data," Good data quality enables Features Analytics to apply algorithms to select the best variables, and to design new features to ensure the models learn and evolve. So, it is not always a case of bigger is better. Both quantity and quality of data matter - as well as the underlying modeling technology, which drives insights.

June 10, 2015

The Fraud Practice: Critical capabilities that can enable custom modeling to improve business intelligence

Custom modeling solutions, whether they are built and managed in-house, provided through a third party or through a combination of both, can provide Business Intelligence, or BI, that helps organizations maintain effective risk management with access to rich data to review trends and monitor performance. These data and Business Intelligence features can not only help improve risk detection, but also provide an organization with an overall better understanding of their customers and business, with benefits to marketing, user experience teams and other groups outside of risk management.

March 18, 2015

The Fraud Practice: With custom modeling, deployment is just as critical as design

Even if a custom model was accurate at predicting fraud nearly 100 percent of the time, it is of no benefit unless it can be applied to transactions, meaning the transactional and customer data can be fed to the models and the results can be interpreted to decide the course of action for each order, all in real- or near real-time. This is why being able to effectively and efficiently deploy custom models is just as, if not more, important than model creation and design.

December 04, 2014

Features Analytics participates together with The Fraud Practice in the "Enabling Custom Modeling & Analytics for the Modern eCommerce Merchant" US webinar

Even if a custom model was accurate at predicting fraud nearly 100 percent of the time, it is of no benefit unless it can be applied to transactions, meaning the transactional and customer data can be fed to the models and the results can be interpreted to decide the course of action for each order, all in real- or near real-time. This is why being able to effectively and efficiently deploy custom models is just as, if not more, important than model creation and design.
2014