In the era of digital finance, artificial intelligence (AI) and deep learning (DL) technologies have revolutionized the way financial institutions assess risks, detect fraud, and make credit decisions. However, as financial systems grow more sophisticated, the role of computer security and data engineering becomes crucial in ensuring the integrity and safety of AI-driven risk models. This paper explores the use of deep learning in financial risk modeling, specifically in credit scoring and fraud detection, while also addressing security concerns and the importance of robust data engineering frameworks. We analyze different models, algorithms, and methodologies employed in these domains, highlighting their effectiveness, advantages, and limitations. Additionally, we discuss cybersecurity challenges, data governance, and the ethical implications of AI in finance, along with future trends in AI-driven risk modeling