A research report published by the Alan Turing Institute, estimates that up to 5% of total GDP is lost to fraud and corruption, costing the global economy about $2.6 trillion each year. This means that if fraud and corruption were a country, it would be the 8th largest country in the world by GDP, bigger than the economies of Italy, Canada and South Korea.
Alongside the incredible amount lost to fraud each year, the types of fraud and methods deployed to exploit it are continually evolving. It is clear that professional fraudsters are organised and intelligent and should be treated as such. They are able to rapidly devise new techniques and methods to commit fraud when their existing methods become obsolete.
That is why it is crucial that the technologies we deploy allow our financial services and insurance organisations to stay one-step ahead, proactively identifying and stopping fraud.
Rule-based systems have been the cornerstone of our fraud defences for the last 30 years. Now newer predictive analysis techniques such as supervised and semi-supervised machine learning and anomaly detection have proven critical in improving the efficiency of our fraud defences and reacting to changing fraud techniques.
So, what do these terms mean and how can they help identify potential fraud?
Anomaly detection is a type of unsupervised machine-learning technique used to identify patterns within data such as outliers, exceptions and peculiarities that deviate from the normal behaviour expected of the data set.
Unsupervised learning at its core means the algorithm is not given any information to label or score the training data prior to its deployment and must self-discover any patterns or trends.
Putting this in a fraud context means that, as fraudsters develop new techniques to get around your anti-fraud strategies, your defences can proactively identify, evolve and respond to emerging threat vectors.
Predictive analytics and machine learning technologies have come a long way since they were originally introduced to the world of financial services and insurance. As pioneers of this still developing technology we have seen and been part of this journey.
Anomaly detection is one such development. It is a fairly well-known technology but is predominantly established in the transactional space. It represents a significant opportunity for the financial services and insurance sectors to both protect their business and customers from losses to fraud, whilst also using it a source of competitive advantage to drive forecasting and pricing decisions.
To work effectively & efficiently, anomaly detection typically requires a significant amount of data. That is why even though traditional rules-based systems are sometimes considered a little old-school by today’s standards, they still provide an essential role in improving the accuracy and effectiveness of machine-learning solutions.
And supervised machine-learning, whilst undoubtedly powerful, is inherently reactive to a degree. It requires the targeted outcome to have already occurred and relies on this data in order to build and train the model.
As time elapses those characteristics that originally made up the target outcome will likely change. As a result, the model will require recalibration based on the data of the updated characteristics of the targeted outcome.
Anomaly detection transforms a business’ ability to become proactive and respond to changes in customer behaviour or fraud MOs in real-time and therefore improves the decisions they can make as a result.
We believe that anomaly detection is the perfect complement to existing rules-based predictive analytics solutions to help provide an uplift through the identification of outliers to predictive models.
Anomaly detection represents the next evolution to achieving a truly proactive approach to fraud detection and prevention. The solutions currently making up the cornerstone of our fraud defences will not be made obsolete by this newer technology, just enhanced. They will still have a major role to play as part of an organisation’s multi-layered defence against fraud. Only this time the results are in real-time and so are the responses.
Find out how Anomaly Detection can help enhance your existing fraud defences.