June | 2016
In my last blog , I spoke about the magnitude of damage P2P fraud can cause to the organization and the need to address the problem with a different mindset with a more data driven approach.
Let me list out some common risk factors in the payments space that we see today:
As is conceivable, some of these could be due to human errors and others with an intent to deceive. While traditional approaches help in uncovering some gaps, they suffer from some inherent shortcomings as discussed in one of our earlier posts . Some of these shortcomings are high false positives, inability to uncover newer anomalies and recognize patterns in large datasets, not learning from feedback.
We have seen significant upside potential through use of data analytics and machine learning in fraud detection. Since anomalies are rare and considerably different from each other, pre-defining what we are looking for turns out to be infeasible. We approach this by letting data define the boundaries of normal behavior and flag the ones that fall out/ look abnormal. Additionally, by scoring the red flags, we can set priorities for the investigation team, channelizing their efforts to higher order tasks.
Below are some of the outcomes from our implementations in Procure to Pay:
These outcomes demonstrate the potential of Artificial Intelligence (AI) in elevating the P2P function to one of strategic importance and hence, in enabling the organization to achieve competitive advantage over its peers.
© 2021 Wipro Limited |
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© 2021 Wipro Limited |
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