Banking
Banks are taking a knife to a gunfight without fraud analytics
Published by Jessica Weisman-Pitts
Posted on October 19, 2022
4 min readLast updated: February 3, 2026
Global Banking & Finance Review®
Company
Published by Jessica Weisman-Pitts
Posted on October 19, 2022
4 min readLast updated: February 3, 2026
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:
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.
Fraud analytics is the use of data mining, machine learning, and data science to detect and prevent fraudulent transactions in banking.
APP fraud, or Authorized Push Payment fraud, occurs when consumers are tricked into willingly sending money to fraudsters.
Machine learning is a branch of artificial intelligence that enables systems to learn and improve from experience without being explicitly programmed.
Data mining is the process of analyzing large datasets to discover patterns, correlations, and insights that can inform decision-making.
Proactive fraud prevention involves using analytics and technology to identify and mitigate potential fraud risks before they occur.
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