DATA & ANALYTICS

One industry , however , has been one of the earliest adopters of AI . Fraud and ID , operating mostly within the financial sector , has been using machine learning ( ML ) for decades in its pursuit to keep money moving where it should .

Yet , just because it has been an early adopter and long-time user does not mean it will not evolve as AI does . Fraudsters are adapting .

“ For many years , organisations relied on rules-based technology to spot fraudulent activity ,” explains Christen Kirchner , Senior Solutions Expert , Fraud & AML at SAS .

“ However , as fraudsters adapt their methods , for these rules to be effective , they would need to be continuously updated and tuned .”

Fraud & ID detection dynamics AI uses several techniques to spot irregularities or any signs that may show fraudulent activities or fake identities .

The ML used relies on rule-based fraud detection methods and models . These analyse crucial transaction details to identify potential fraudulent activities , collect historical data about past fraud cases , and scrutinise various elements , including the purchase amount , device ID , and e-mail address associated with the transaction .

They also consider whether a VPN is being used to mask the user ’ s actual IP address , as well as the type of browser employed and if it ’ s operating in incognito mode . Additionally , these systems take into account recent failed login attempts to the account in question .

These predictive and adaptive analytics techniques are applied to combine big data sources with real-time monitoring and risk profile analysis to flag suspicious transactions that deviate from established patterns of legitimate user behaviour .

Yet , this model , although having kept our financial system afloat for many years , has its challenges .

“ One of the main challenges is managing and analysing large sets of unstructured data ,” says Ariel Shoham , VP of Risk Product , Mangopay .

“ Although the quantity of data matters in detecting fraud and discovering new patterns , this data must be sorted like wheat from the chaff to increase fraud prevention efficiency . Irrelevant data can lead to inaccurate predictions and may increase the number of false positives .”

The accuracy of these fraud-preventing AI predictions depends heavily on the data being sorted and labelled correctly . Yet , this is a laborious and data -intensive task .

For large enterprises , who can invest the infrastructure into their systems and procedures , this may not be as big of an issue . But in the age of fintech , where small challenger startups are

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