AI Fraud Detection for Banking and Finance
AI as a tool for fraud detection has already been adopted by banks and financial institutes – just not in the form it has evolved to today. The in-use technique is called “anomaly detection”, a process which enables the automation of cybersecurity features like anti-fraud and anti-money laundering.
Now, more than a decade later, further advancements in AI have created the opportunity for even greater involvement of AI when it comes to fraud detection – leading to easier compliance for banking and finance, and more security for customers.
Machine learning is a key component of a functioning AI system. It can be of great benefit to banks, analysing data across multiple channels to detect occurrences of fraud across a range of transactions and applications.
The data from this analysis can be automated using intelligent automation solutions, but it also has the capacity to be forwarded to a human element like a support team to make an informed decision.
Fraud Prevention and the Role of AI
As well as detecting possible instances of fraud, AI is also an incredible asset for preventing fraud from occurring in the first place. Similar process functioning off lower-level services are in use, but revolutionising these with a full adoption of AI into your financial business model can further increase your cybersecurity and prevent fraud.
The main way in which these developing AI technologies can impact your fraud prevention is through a further application of the “deep learning” principle, which trains a neural network to recognise behavioural patterns – potentially preventing fraud by warning customers or restricting unexpected inputs automatically.
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