How we decreased fraud rates for a major bank operating in Europe & Asia
We significantly reduced a bank’s instances of social engineering fraud on their mobile banking app.
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We significantly reduced a bank’s instances of social engineering fraud on their mobile banking app.
Industry:
BanksProduct type:
Enterpriseof fraud attempts detected in the sample mobile app traffic
response time, no negative impact on UX and loading speed
recorded false-positives, once processed by our logic set-up
A bank operating in Europe and Asia was dissatisfied with its current rules-based anti-fraud setup. As a major bank providing financial services, it is prone to attacks from cybercriminals on desktop and mobile channels, particularly social engineering fraud. The bank, therefore, required an effective solution to decrease fraud rates with a response time of no more than 500 milliseconds.
The bank also sought a solution that would seamlessly integrate with strict state Personal Data Law and Localization requirements that do not permit the processing of bank data outside of its national territory.
Industry: Banks
Fraud type:
Fraudsters manipulate victims to divulge their credentials either through phishing, vishing or SMiShing (to install malware) or even gain direct access to their desktop and mobile devices via remote desktop protocols. For this reason, financial institutions require detailed profiling to recognize if they are dealing with a regular user or fraudsters.
State data protection laws are very strict, prohibiting the processing of customers’ personal details outside of the national territory. The bank required that we could effectively deal with fraud while being fully compliant with national law.
Using an initial portion of 500,000 unique users on the bank’s mobile app (Android and iOS) and processing over 80 mln transactions on a monthly basis, our solution was able to detect 71% of instances of fraud against strict requirements about how much traffic we could refuse. Our results included anomalies and suspicious behaviors indicative of potential fraud. Some key takeaways:
Although the data is based on a sample of data for a proof of concept (PoC) demonstration, our solution had the potential to limit transaction rejection rates to 0.028% (against a strict bank benchmark of less than 1%). Our advanced solution is able to effortlessly collect over 5,000 pieces of digital fingerprinting data and scan behavioral biometrics, all backed up by advanced machine learning (ML) models on both mobile and desktop platforms.
Take advantage of a ready-to-take or custom machine learning models for data of all sizes.
Use in action over 100 types of risks we detect on websites & mobile apps.
Use an intuitive user panel, create rules, automize and customize fraud detection.
After analyzing data in real time and quietly in the background of each user browser session, we can confirm (with median response time of 500ms) if a user was genuine or a potential fraudster. In this case, we were able to block numerous fraud attempts, many of which were of high value, originating from only a handful of users connecting through numerous IP addresses. All fraud pattern data is provided in real time and can be analyzed in our easy-to-use control panel, providing the customer with valuable business insights.
Our advanced solution allows for seamless integration with the EU’s GDPR, eliminating the risk of fines through non-compliance with implementation. As the GDPR is one of the world’s toughest privacy and security laws, we could adapt Nethone’s tools to work within state law and easily fulfill the bank’s requirement despite processing data on servers within the EU. We are, therefore, able to adhere to GDPR standards within the EU and adjust to national law requirements around the world.
Additionally, the bank appreciated how clearly collected data was presented and how easily they can navigate through our API responses.
of fraud attempts detected in the sample mobile app traffic
response time, no negative impact on UX and loading speed
recorded false-positives, once processed by our logic set-up
Industry: Banks
Fraud type:
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