Implementation of a Security Risks Model in Mobile Money Transactions Using a PIN Attempt Monitoring Algorithm
Mobile money platforms have revolutionized financial inclusion across developing economies, yet they remain highly vulnerable to sophisticated, automated credential-stuffing and unauthorized access attacks. Traditional static PIN authentication mechanisms often fail to balance robust security with user experience, leaving systems exposed to distributed brute-force attempts. This paper presents the design and implementation of an adaptive Security Risks Model powered by a PIN Attempt Monitoring Algorithm (PAMA) to mitigate fraudulent access in real-time mobile money transactions. The proposed system utilizes a state-machine architecture that continuously ingests transaction metadata, device telemetry, and temporal patterns to calculate an instantaneous risk score. Unlike rigid, standard threshold-based lockouts, the PAMA system introduces dynamic, exponential back-off delays and contextual step-up authentication triggers based on the calculated risk profile. Experimental simulation data demonstrates that the algorithm successfully mitigates up to 94% of automated brute-force vectors while maintaining zero friction for legitimate users under normal operating parameters. The architecture proves that pairing deterministic algorithmic monitoring with risk-scoring structures can significantly harden mobile financial systems against emerging fraud vectors without sacrificing system performance.