A Finextra Research Impact Study in association with Aerospike.
With increased financing options at point-of-sale, card-not-present transactions, and contactless payments, comes a resultant surge in fraudulent transactions and financial crime. This increase in digital fraud has been catalysed by the recent Covid-19 pandemic-induced shift to online banking and commerce.
Now more than ever, financial institutions must implement payments authentication processes to prevent the long-term risks associated with fraud, including slimming margins and reputational damage.
One way financial players can stay ahead is to analyse all available historical and real-time data, and apply artificial intelligence (AI) and machine learning (ML) tools – which encompass a range of algorithmic approaches that derive from statistical methods such as regressions and neural networks – to decipher legitimate transactions from the illegitimate.
There are, however, five further business benefits to understanding customer risk profiles. Actionable insights derived from fraud profile analysis can help banks visualise each customer, not as a collection of disassociated data points, but as a mosaic, made up of different characteristics that merge to provide a comprehensive view. This can lead to complex, holistic, and predictive analysis of customers’ behaviour – generating consistent and tailored services.
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