Banks are taking a knife to a gunfight without fraud analytics

By Mark Crichton, Head of Product at Outseer

Fraud is a global business, and it’s booming. The number of online fraud attacks continues to rise and shows no signs of slowing down. A PwC crime survey found that in 2021, global fraud losses topped $42bn. In H1 of 2022, Outseer data shows that APP fraud – where consumers are conned into willingly sending money to fraudsters, such as romance and crypto scams – made up 75% of all fraudulent banking payments.

With these figures climbing year on year – fraudsters are currently winning the war. So, how should the banking industry respond? Educating customers certainly plays its part but can’t be solely relied upon. Fraud analytics is the only way to proactively prevent fraud at the source effectively.

What is fraud analytics?

Fraud analytics refers to the process of leveraging data mining, machine learning, and applied data science to understand, detect, and prevent fraudulent transactions. Fraud prevention systems rely on analytics to identify the tell-tale signs of fraud before it can harm businesses or customers. Generally speaking, fraud analytics involves banks gathering data from a large, high-quality dataset and mining it to establish baseline patterns that can be used to predict and stop future fraud attacks.

Analytics helps to cut fraud off at the source and is a proactive measure to prevent consumers losing out. But it’s only as strong as the sum of its parts. More data means better insights. Shared data spanning thousands of companies, sectors, and geographies net the best results. With the power of machine learning and artificial intelligence, automation can immediately take corrective action when these threats are detected.

The benefits of fraud analytics

Whilst the benefits to consumers of companies deploying fraud analytics are clear – fewer fraudulent transactions – it also has a lot of benefits for banks:

• Reduced costs:Many banks deploy fraud analytics to help monitor their systems and build threat models to prevent instances of fraud within their businesses. Leveraging services that use artificial intelligence eliminates countless hours of manual research during an investigation. Fraud analytics allow organisations to be proactive in defeating scams. By staying ahead of attacks, banks avoid embarrassing and costly data breaches that jeopardise their customers and intellectual property.
• Protecting a brand:With brand abuse attacks at the forefront of fraud trends, banks have a lot more to lose than just their revenue. Fraudsters impersonate brands by exploiting their brand identity and consumer trust to trick their prey into handing over payment details. Brand abuse scams not only steal payment details from their victims, they also quickly tarnish the brands they impersonate. Even when a bank has no idea it’s being impersonated, customers often hold that brand accountable for the deception. Spoofed domain names, fake social media accounts, phony brand smartphone apps, and phishing sites can all be detected and removed before they can cause serious financial or reputational harm.
• Protecting customers:Even when banks do everything right, it still might not be enough to stop fraud. Between data breaches and dark web marketplaces, customers’ stolen data could be beyond control. Armed with compromised payment details or login credentials, fraudsters can sign on to customer accounts, change security settings, make purchases, and view sensitive data. Artificial intelligence combined with modern data science and shared transaction data can understand the context behind a user login or transaction. This separates friend from foe by comparing activity to typical user behaviour and challenging transactions that are suspicious.

Putting Fraud Analytics into Action

For most companies, putting fraud analytics into action is easier said than done. Defining a risk tolerance and putting it into practice takes time and effort. Having a dedicated fraud prevention team requires a high level of resource. Monitoring for evolving indicators of compromise and brand impersonation must be continuous to be effective.

Without these guardrails in place, it’s impossible to take the fight to fraudsters. With attacks increasingly yearly and customers bearing the brunt, banks must act now with the latest technologies like AI and machine learning to combat this. Without analytics, banks are taking a knife to a gunfight, and we’ll continue to see fraudsters wreak havoc on banks and their customers.