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Companies could save USD 12 bln with adaptive behavioural analytics

Friday 22 July 2016 07:41 CET | News

A study published by Oakhall and Featurespace has estimated that financial services companies could save USD 12 bln annually by employing adaptive behavioural analytics software.

By employing adaptive behavioural analytics software to both identify actual fraudulent transactions, and reduce the number of ‘genuine transactions declined, as well as reducing the costs associated with managing blocked customers, the industry could reduce the USD 31 bln total annual cost of card fraud by over USD 12 billion annually.

Genuine transactions declined, also known as false positives, are legitimate transactions that have been incorrectly blocked by existing fraud prevention systems, which result in lost revenue and additional management costs to the card issuer.

Working with banks and cards issuers, Featurespace demonstrated a 25% reduction in the incidence of undetected fraud and, simultaneously, a 70% reduction in genuine transactions declined, as well as a subsequent reduction in call centre costs of 50%.

Oakhall applied these results to industry data to estimate the implied savings for the industry at USD 12.2 bln, comprising USD 4.1 bln reduced fraud and USD 8.1 bln reduction of fraud management costs and lost revenue.


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Keywords: adaptive behavioural analytics, machine learning, fraud management, false positives, transactions, Oakhall, Featurespace
Categories: Securing Transactions | Digital Identity, Security & Online Fraud
Countries: World
This article is part of category

Securing Transactions






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