The state of digital fraud prevention in financial institutions
Even before the upheaval caused by the COVID-19 pandemic, financial fraud has been a significant thorn in the side of businesses and individuals alike.
Globally, fraud accounted for losses of more than $5 trillion, a 2019 study has found.
Implementing advanced anti-fraud mechanisms will become even more critical at a time when banks and other financial institutions embark on digital transformation programmes to meet the increasingly sophisticated demands of today’s connected customers.
Today, people apply for loans via online channels or purchase goods with Internet or mobile payments instead of visiting a physical shop.
But the convenience does not mean it is without risk. Fraudsters have access to sophisticated tools and technologies to perpetrate the likes of identity theft, digital payments fraud, and other attacks.
It is no longer good enough for businesses and consumers to be aware of the potential for compromise, they must start playing a more active role in defending themselves.
Using the technology and analytics to address all types of fraud becomes an increasing need, allowing for more sophisticated detection and investigation methods, reduced costs, and increased efficiencies.
Sophisticated analytics techniques give an organisation a significant advantage in the quest to manage and control fraud losses in real-time, to reduce the number of false positives and to enhance overall investigation.
An example of this is network analytics that enable a company to uncover organised fraud rings that might otherwise take years to identify.
Embracing technology
Data and analytics are therefore key to combat the surge in financial-related crimes.
Artificial intelligence (AI), and specifically machine learning, can provide financial institutions with automated algorithms that incorporate a cross-channel view of customer behaviour, help to spot complex fraud trends, and reduce false positives in parallel.
In the world of digital operations there are thousands of data points collected every second describing every single step of a customer.
Information about devices, geolocation of users, and even behavioural biometrics are playing the role of additional fuel for analytics. Analytics is perfect way to approach that huge amount of data.
To find different types of fraud and suspicious activities you need to connect the dots and analyse variety of data types together (not only structured data, but also text, or maybe even voice, image and video data).
This is where SAS anti-fraud technology takes a unique, with Hybrid Analytics approach to fraud detection and prevention.
It features a powerful fraud analytics engine uses multiple techniques, including advanced analytics with embedded AI and machine learning, to uncover more suspicious activity.
Now this approach, has even a new fresh and trendy name of “Composite AI” introduced in the recent Gartner Hype Cycle for Artificial Intelligence.
Composite AI can combine several techniques, including business rules encoding expert knowledge, anomaly detection, predictive models, network analytics, natural language processing (NLP) and even deep learning, audio processing or computer vision.
All of that combined together in order to detect fraud with greater accuracy.
A changing world
The move towards digital shows no signs of stopping.
This seems like a very good time for banks and financial institutions to take action to protect themselves and their customers from variety of fraud modus operandi in the digital space, including payments, application and identity fraud.
Fraud detection requires a comprehensive approach to match data points with activities to find what is abnormal.
Fraudsters have developed sophisticated tactics, so it is essential to stay on top of these changing approaches of gaming the system.
In order to keep up with fraudsters’ changing approaches, the modern fraud detection and prevention technology chosen should be able to discover complex fraud patterns in data in real-time.
It should use sophisticated machine learning models to better manage false positives and detect network relationships to see a holistic view of the activity of fraudsters and criminals.
Applying Hybrid Analytics or Composite AI approach has proven to be far more accurate and effective than approaches based only on rules – and is becoming a silver bullet to combat fraud in the digital world.
Click here for more information on SAS for Fraud, AML and Security Intelligence.
Marcin Nadolny, Head of EMEA Banking & Insurance Fraud at SAS
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