Strengthening bank fraud detection has become imperative in the digital age, as cybercriminals employ increasingly sophisticated methods to exploit financial systems and consumer trust.
In this case study we shall consider a bank, which was using rules-based anti-fraud processes and later changed the processes to strengthen the anti-fraud program and controls.
A bank was operating in Europe and Asia. The management of the bank was dissatisfied with the current rules-based anti-fraud processes.
The bank was prone to cyber-attacks from cybercriminals on desktops, systems, mobile applications, and delivery channels, particularly the bank, therefore, required an effective solution to decrease rates of fraud incidents and significantly improve the fraud response time.
Strengthening Bank Fraud Detection
Social Engineering Fraud
The bank also sought a solution that would seamlessly integrate with strict state Personal Data Laws and Localization requirements that do not permit the processing of bank data outside of its national territory.
Fraudsters manipulate victims to divulge their credentials either through phishing, vishing, or malware or gain direct access to desktops and mobile devices via techniques of remote desktop protocols. Financial institutions need detailed data access scrutiny and user profiling to ensure that fraudsters are not given access to the data and data sources.
Compliance with State Data Protection Legislation
State data protection laws are strict, prohibiting the processing of customers’ 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 around 500,000 unique users on the bank’s mobile application and processing over 80 million transactions each month, our solution was able to detect around 71% of instances of fraud against strict requirements about how much traffic we could refuse. The outcomes included anomalies and suspicious behaviors indicative of potential fraud.
Some key takeaways:
Fraudsters were frequently restoring factory settings before the fraud occurred (difference between total space and free space on a device).
Most fraud attempts came from the iOS platform.
Fraudulent transactions were statistically of higher value.
Although the data is based on a sample of data for a proof of concept (PoC) demonstration, our solution had the potential to have transaction rejection around 0.028%.
Our advanced solution can 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.
Final Thoughts
In this case study, a bank operating across Europe and Asia, previously relying on a rules-based anti-fraud process, recognized its vulnerabilities to sophisticated cyber-attacks, particularly social engineering fraud. With the aim to bolster security, decrease fraud rates, and improve response time, the bank sought a solution adhering strictly to stringent personal data laws that prohibited external data processing.
Implementing a pilot solution on their mobile application with approximately 500,000 unique users, it was observed that the solution could detect 71% of fraud instances, pinpointing patterns like fraudsters often resetting devices and a higher frequency of fraud from the iOS platform. Despite being a proof of concept, this advanced method, utilizing digital fingerprinting and behavioral biometrics powered by machine learning, proved promising with a minuscule transaction rejection rate, showcasing a potential transformative approach in the domain of financial security.
