The increasing digitization of financial services has brought both unprecedented opportunities and significant risks. With the proliferation of digital platforms and transactions, fraud and cybersecurity threats have become more sophisticated, necessitating innovative solutions to mitigate these challenges. Machine learning (ML) has emerged as a powerful tool in transforming fraud detection and strengthening cybersecurity measures in digital financial services. By leveraging advanced algorithms and data analytics, ML enables organizations to identify patterns, detect anomalies, and predict potential threats in real-time, significantly enhancing the speed and accuracy of decision-making. In fraud detection, ML models analyze large volumes of transactional data to uncover fraudulent activities that traditional rule-based systems often miss. Techniques such as supervised learning classify known fraud patterns, while unsupervised learning identifies novel fraud scenarios. Moreover, ML-driven systems adapt dynamically to evolving threat landscapes, ensuring robustness and scalability. Similarly, in cybersecurity, ML enhances intrusion detection systems (IDS), malware analysis, and behavioral profiling, enabling financial institutions to proactively address vulnerabilities. Despite its transformative potential, implementing ML in digital financial services presents challenges, including data quality issues, algorithmic biases, and regulatory compliance requirements. Addressing these barriers requires robust data governance frameworks, interdisciplinary collaboration, and adherence to ethical and privacy standards. This article explores the application of machine learning in detecting fraud and improving cybersecurity in digital financial services. It examines key techniques, challenges, and real-world case studies, providing actionable insights for stakeholders seeking to leverage ML for operational resilience and trust-building in the digital economy.