Fraud detection is a substantial challenge. This is due to the fact that fraudulent transactions only can ever represent a very small fraction of financial activity, which makes finding them equivalent to a needle in a haystack. Using rules-based systems to detect fraud is very difficult, as it’s a phenomenal challenge to create a rule that encompasses every anomalous transaction. Fraud detection instead relies on an understanding of what’s “normal” and being able to detect deviations from standard activity.
To combat this, machine learning (ML) systems have long been recognised as a key technology for fraud prevention; they can process a large quantity of data very quickly, and identify the typical qualities of fraudulent and non-fraudulent transactions. By their very design, ML models are intended to discern patterns in data sets and spot outliers and anomalies.
In addition, ML models are adaptable, and so can swiftly respond to sophisticated organised crime, whose methods often change quickly. Through using anomaly detection techniques, AI models are well positioned to observe and respond to changing patterns which indicate fraud. For all these reasons, it should be no surprise that there’s been an underlying trend within financial institutions, auditors, and governments towards adopting ML as part of their fraud detection infrastructure.
The last year has seen this trend markedly accelerate, culminating in Amazon’s fraud detection platform recently being made generally available. One can look at the upsurge in fraud detection technology as an inevitability, it’s actually the case that rapid recent developments have been spurred in no small part by the challenges created by the Covid-19 pandemic.
An uptick in fraud, a reduction in capacity
Between 2020 and 2024, losses from digital money fraud are expected to increase by 130 percent. It’s anticipated that the amount of fraudulent transactions could reach $10 billion by 2024. The pandemic saw this trend markedly accelerate, with its first phase from March-May seeing a 6 percent rise in digital fraud against businesses. Fraudsters have worked to exploit the sudden transition businesses and employees made earlier this year, with all the business and communication disruption that came with it.
At the same time, many of the teams responsible for monitoring fraud had to rapidly switch to remote working earlier this year, and many others were placed on furlough. This means that while there was an uptick in fraud – which would test teams even in normal business conditions – anti-fraud teams found themselves at low manpower and having to operate in an unfamiliar environment.
This made the pandemic a perfect time for many organizations to accelerate the implementation of AI platforms for fraud detection. The sheer potential AI has for fraud detection processes meant that greater uptake of AI models was inevitable, but the pandemic has accelerated this trend by creating a short-term impetus for companies to automate and adopt AI.
Challenges facing AI for fraud detection
Deploying and scaling AI among anti-fraud teams does throw up some novel challenges, which technologists and teams have to thoroughly consider. Beyond the mere technical challenge of deploying AI models among teams, AI also throws up a range of regulatory, compliance, and ethical problems.