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AI Case Study

OCBC bank reduces number of false positive financial transaction alerts by 35% with machine learning

OCBC bank of Singapore analyses transaction activity to identify unusual payments that might be financial crime. In a pilot OCBC was able to reduce the number of false positives by 35% by using machine learning. They were also able to classify transaction alerts into 48 unique risk clusters allowing the compliance team to better prioritise based on risk.


Financial Services


Project Overview

"By embedding the fintech firm's technology into the existing system, around 4,200 alerts have been grouped into 48 unique risk clusters for the compliance team to sieve.'

Reported Results

"The early results showed that the company's technology was able to reduce the number of alerts that did not require further review - by 35 per cent.

The technology was also better at categorising flagged transactions by their risk levels, which vastly improved the accuracy rate of identifying suspicious transactions."


Machine learning





"PwC report...estimated global money laundering transactions to be equivalent to 2 to 5 per cent of global GDP, or roughly US$1 trillion (S$1.36 trillion) to US$2 trillion annually."

At OCBC "'existing transaction monitoring system is a rule-based one, which makes scanning risks very fixed and means they are handled on a first in, first out basis.'"



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