More than half of financial institutions plan to use AI to reduce fraud in 2021 and beyond. Here we look at banking’s latest capabilities in fraud detection, including machine learning and predictive analytics.
The use of artificial intelligence (AI) in the fight against fraud isn’t new. The fight, though, just got a whole lot tougher.
The COVID-19 pandemic has driven considerable change in the world of digital transactions, and threat actors have taken note. Fraudulent activity is not only becoming more sophisticated but is increasing in scale and sheer numbers. It follows then that the demand for effective AI solutions to help combat them is greater than ever.
GBG’s latest fraud report, which includes insights from financial institutions (FIs) across key European markets, gives a clear picture of just how significant that demand is.
The report, Smoothing the customer journey and preventing fraud, found that banking institutions see AI and machine learning as fundamental for future-proofing fraud detection.
Banking institutions are only too aware of the downsides of not investing in AI capabilities. Fraud rates hit record levels in 2020, and human analysts have clear limits on how quickly investigations can be completed.
Adapting to changing threats by scaling fraud data without some form of AI is a heavy burden for analysts to bear. Not only that but human error and a rules-alone approach can account for high numbers of false positives, which only serve to negatively impact the customer journey.
It’s clear that AI will be a key player in the future of banking fraud detection - but how?
AI might traditionally be associated with science-fiction, but its most common form is actually machine learning (ML) - far less other-worldly and yet, proven to be immensely effective in the fight against fraud.
When implemented successfully, machine learning helps detect evolving and complex financial crimes, protects businesses from fraud losses and enables frictionless customer experience in the digital onboarding and transaction process.
So how does it work? Machine learning is a teachable system that can automate both front and back-office processes and mitigate issues before they happen. Instead of operating based on unchanging protocols, it can learn from its own analyses. This evolutionary capability is key.
Machine learning systems take into account past transactions and apply these rules to future analyses to detect banking fraud. The more they learn and become familiar with an FI's system and the techniques that threat actors use to crack them, the more effective the AI model becomes.
An investment in machine learning technology increases in value over time - an outcome that benefits more than just fraud departments.
Before machine learning, there was predictive analysis. And while ML might be more adaptive, newer, and have larger degrees of freedom, predictive analysis still has a firm place in the anti-fraud toolkit.
Unlike machine learning, in which algorithms are asked to process supplied data without a predetermined set of rules and regulations, predictive analysis finds patterns and behaviours by taking into consideration both historical as well as existing external data.
This is particularly useful in sifting through large quantities of data to predict the behaviours a customer is likely to exhibit or possible changes in the market, for example. The bottom line: predictive analytics help to understand possible future occurrences by analysing the past.
Ultimately, the best practice is to use both in unison, with machine learning a natural progression of predictive analysis.
Imagine a world in which you could peer into a crystal ball and see fraudulent behaviour long before it actually occurred. That’s the focus of AI’s next iteration - simulation modelling.
Rather than using historical data, simulation modelling looks to the future. Instead of a crystal ball, though, it uses complex and intelligent simulations to allow organisations to explore fraud scenarios that would otherwise be challenging or unattainable, even without access to real data.
Agent-based modelling (ABM) can be used to get a deeper understanding of system behaviours by simulating how regulators, corporations, banks, or investors interact with one another and the impacts of that interaction on them and to financial markets.
Each fraud agent individually assesses its situation and makes decisions based on a set of rules. Depending on the parameters set in place, agents may be capable of evolving through the type of learning and adaptation that would occur in real life, allowing unanticipated behaviours to emerge.
There’s no doubt that simulation modelling has enormous potential within banking anti-fraud departments. The only question is how quickly banking institutions will move to adopt this type of solution.
AI is not a silver bullet, but when layered with other tiers of analysis, including human ones, it becomes a vital component of your financial fraud strategy.
Find out more about how FIs plan to use AI to help smooth the customer journey while mitigating fraud risk in our report.
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