Automating fraud management is the game changer for 2021
Using technology to analyze payment data, automate workflows and make decisions - including around fraud detection - is becoming quicker and more efficient. At the same time, artificial analysis and machine learning offers greater flexibility and control, allowing banks, merchants, and payment providers to launch new business models quickly.
Experts estimate that banks are deploying AI-driven systems in record numbers, with more than $217 billion spent on AI applications for use cases like fraud prevention and risk assessment.
Fraud detection is an ideal use case for machine learning and artificial intelligence (AI). But as we know, there is more to fighting fraud than technology. The significance of understanding the operational process in combating fraud is fast becoming critical. Historically, many work streams were manual and decentralized, which limited the ability of fraudsters to scale up fraudulent activity. However, as many modern business processes have become centralized, with more and more powerful technology, the ability to scale up fraudulent activity has become a more compelling prize. That fact has become all too evident by the large-scale data breaches that we see around the world today.
My team and I believe that thecurrent rise in fraudis inextricably linked to highly scalable technical process failures. Solving financial crime is not just about products; highly trained people and well-defined processes are also critical. With the increased shift towards digital payments, which has only been accelerated by the COVID-19 pandemic, manual data analysis and threat detection methods have failed to stop the fraudsters. As a result, multiple industries are looking to automation tools to manage, enhance and improve their anti-fraud operations.
Historically, some fraud detection processes that required a mix of ML and human subject matter expertise (SME) have been very difficult to automate and replicate. However, new tools are entering the market to remove the manual effort from fraud detection processes, which had previously required 24/7 management.
Where large scale operations exist, such as with the Internet of Things (IOT) and scalable cloud services, there is a real possibility that security intrusions and threats can go unnoticed. Forward-thinking businesses are now using a set of AI/ML tools known as anomaly detectors. These tools utilize unsupervised learning methods to understand and identify normal activity, creating alerts or processes to follow when abnormal activity is detected.
In this way, whole networks can be effectively monitored and protected, as each individual node will have its own behavior "footprint," which can be automatically monitored both separately, and as part of the whole system, in real time. This tools can be extremely powerful when a web service is targeted by cybercriminals, for example, detecting the abnormal behavior and stopping it before damage occurs.
Extremely detailed information these tools reveal about data sets can also be used in other, more positive ways. Patterns that flag and identify problems can also be used to identify similarities among customers and their spending patterns for marketing campaigns and other engagement activities.
With fraud on the rise, and a more traditional approach to creating more rules and models leading to more alerts and higher incident rates, automation of the fraud management process will be paramount to successful fraud detection in 2021.
Many rules-based fraud detection solutions require nearly constant monitoring to ensure effective fraud detection. Even with ML doing some of the heavy lifting, there is still a large amount of monitoring needed to manage fraud risks. This, coupled with the rise in electronic payments, means it is becoming far too time-consuming to manually discover fraud patterns and write effective rules, or create ML models to combat fraud.
Automating this process is not simple. There are several manual processes, including models and rules performance monitoring, fraud pattern discovery, model and rule generation and fraud alert management processes, to automate. However, the reward for doing so is compelling. Organizations save money by detecting and stopping fraud sooner, while SMEs can focus on extremely difficult fraud cases and perform more valuable services.
Keep in mind that developing automation tools as products brings its own set of problems. One of the biggest issues my team faced was engineering ways to reduce or entirely remove user interactions, while still making our product perform how the user wanted it to and giving them choices when they were required.
But the trend is clear: Automated AI/ML based fraud systems are quickly becoming the standard in the fight against fraud.