This is a guest post by Seth Ruden, from ACI Worldwide.
Think about the last time you got a fraud decline. Where were you? In the grocery store? Buying airline tickets? On holiday? Shopping in the same place you’ve been a dozen times, but across the border? How frustrating was that, what did it do to your perspective, your mood, your confidence in your financial institution? This can be embarrassing and inconvenient, stressful and alarming for the consumer. There are few things that can be more disruptive in our day to day lives then the lack of access to your funds, or the care taken by your financial institution after a fraud occurs. According to ACI Worldwide’s Global Consumer Fraud Survey: 20% of people may decide this is too much and move along to another financial institution.
So, how can we harness payments data and manipulate it to determine if a transaction is legitimate or not, and more accurately and reduce customer churn?
Here’s a guide to some examples of how advanced analytics that can be developed to underpin a successful approach to fraud detection. This should allow you to start separating the hype around artificial intelligence from machine learning, neural and regression models and the rule-based logic we all have come to embrace in the practice of making sausage in a fraud shop.
Enter behavioral profiling, the capacity for one’s own behavior to be able to be influencing fraud detection strategy. The merchants we typically visit, the fuel pumps where we get gas, common travel destinations, these are all a part of the behavioral profiling technology that we’ve been using for years. Questions arise: How often do we buy from retailers who sell women’s ready to wear clothes? What’s the potential that someone will spend their holiday in Belize? Is this an unusual amount that someone is taking out at this ATM and is it the first time they are using this specific terminal on the other side of town? The technology to evaluate these scenarios has been around for a while now, it’s mature and widely accepted as “table stakes” feature functionality to reduce fraud risk and maintain a good customer experience.
Behavioral profiling is also useful in models, whether it be a regression model (expanding on the example above at the ATM), neural scoring models or going into rule-based machine learning path. Adaptive Machine Learning will typically leverage multiple data sources, using up-to-date variables to provide the greatest possible timeliness in both legitimate and fraud transactions. This integration of many data points and risk indicators may include: recent fraud trends, legitimate spending patterns including non-monetary transaction elements like the addition of beneficiaries or changes to demographics, end-point device intelligence, resident malware indicators, authentication results and 3rd party or internal scoring models. Any risk indicator can be ingested into this model of models.
So, while all these elements are assembled together into a larger complex regression model, there is one additional element that can be integrated into the model that enhances it with statistical properties: the risk-weighting of the various signals and data elements inside of the regression model. This allows for the model to accurately assign an optimized predictive capability to these data elements which will then in turn accurately calculate the relative risk of the data element. This process produces reliable and repeatable decisioning logic, identifies insights into the legitimacy of the transaction and provides value relative to historical relationships and trends, aligning the model logic to inbound transactions, recognizing patterns and delivering the value of advanced analytics in fraud detection.
Pattern recognition in supervised machine learning (where the machine is provided example inputs, perhaps exemplified in the data elements described above) is not a new science, we’ve been delivering these models for years. What is new is the hype around this process and the introduction of Artificial Intelligence (AI) concepts in the fraud detection space.
Here’s the deal with unsupervised AI: It’s not ready for primetime, performing under the legacy supervised machine learning analytics applications that are deployed presently in the smarter financial institutions and processors. It’s simply not fast enough, smart enough or cheap enough to be implemented at scale, so when you hear people say AI and fraud in the same sentence, you might be smelling actual fraud.
AI is suggested, expected and advertised to be the technology holy grail that will reduce human supervision and oversight, minimizing the manual analytical work load and constant strategy maintenance that is the backbone of any fraud analytics team. The end goal of AI is to get the computer to minimize the amount of work from humans and transition it to the machine, ultimately to realize a lift in efficiency and accuracy in fraud detection. The computer can indeed do things faster than humans, but the human will always know better about the reasons behind the signals and for this reason, while the goal is admirable, its unlikely to be fully realized.
Utilizing the existing supervised machine learning strategies is presently delivering the best probability of aligning the stars of a high detection rate AND a positive customer experience. This is again, table stakes for financial institutions as the culture of fraud detection moves further toward the best customer experience metrics. Because I shouldn’t get a decline when I am going back to Arizona for Christmas again or having my favourite meal at that one Poutinerie in Montreal with the best smoked meats. But if you’re my bank and you fail to detect more than a transaction in a country I’ve never been to, I’ll be upset over it.
ACI Worldwide is a sponsor of the Interac Risk and Cybercrime conference, happening April 25 and 26 in Toronto. Click here to find the full list of topics and speakers: Interac Risk and Cybercrime Conference 2018.