Credit Risk & Fraud Detection with DevOps and Cloud-Native Approaches
Puneet Pahuja · The Review of Contemporary Scientific and Academic Studies · 2025
Conventional systems to detect and prevent fraud in financial services are frequently insufficient in that credit risk is constantly evolving and financial crime is increasingly sophisticated.Enterprises can overcome these challenges by leveraging modern technologies such as DevOps and Cloud-native.Such technologies bring scalability, real-timeliness and agility to the forefront of the solutions.The convergence between DevOps principles and Cloud-native architecture will facilitate the detection of both financial fraud and credit risk (this paper).With DevOps backing, financial apps can be developed at a faster rate.Thanks to DevOps, Credit Risk & Fraud Detection System could quickly integrate changes and updates without compromission the app security and the app stability.By continually updating their algorithms with the most recent risk indicators and fraud patterns, banks and other financial services providers can more accurately assess a customer's credit history, and capture incidents of fraud.Banking institutions could build highly performant, cheap and massively scalable solutions in credit risk and fraud detection with the help of serverless computing, microservices and containerization.The cloud can manage large data sets in real-time with the help of artificial intelligence (AI) algorithms and machine learning models, to uncover potential fraud tendencies and understand credit risk.When integrated with Cloud-native technology, DevOps makes it possible for data to be processed in real time, enhances algorithms which can detect credit risk and fraud and additionally, has the potential to deal with huge data sets.Sophisticated analytics require cloud storage and computing.The DevOps-style is marked by fast deployment and use.Banks can develop holistic systems for fraud detection, identifying all potentially fraudulent patterns across different facets of financial transactions, by using cloud offerings such as data lakes and real-time processing pipelines to tap into huge volumes of both real time and historical data.It further explores how adoption of these technologies falls within the broader trend of digital transformation taking place within the financial services industry, with firms increasingly shifting to cloud-based platforms to improve customer service and drive operational efficiency.