BFSI (Banking, Financial Services and Insurance) organizations, being driven by evolving financial and regulatory environments, build software systems that grow over time due to business growth, business ecosystem integration or tactical adjustments. However, this leads to significant technology-led debt built into the software systems over time. While technical debt can be easily detected and rectified at the software engineering level, architecture debt having a long-term impact on software agility, scalability and resilience may be hard to even detect, given the inherent design principles and culture aspects that drive architecture decisions. In this paper, we discuss specialized techniques using HyperAutomation—“automation for automation”—to continuously detect, mitigate and manage architecture debt over time. Architecture debt tends to accumulate across business, application, database, technology and tool chains, and infrastructure layers. Detection of such debt typically needs stochastic or heuristic methods in addition to impact analysis of such debt in a complex technology ecosystem. Hence, use of AI and associated machine-learning algorithms including deep learning, that continuously learn system behavior related to accumulated debt metrics may be used to establish appropriate observability and derive predictive analytics on nature, impact and build-up of debt across relevant architecture layers. For example, transformation from product-centric to service-centric organization requires re-architecting of applications into pluggable modular components with associated interfaces. However, legacy applications pose multiple challenges as the extent and impact of such debt cannot be ascertained through manual or passive automation methods. Appropriate learning algorithms, as discussed in this context, can provide effective ways to detect and manage architecture debt. Existing literature covering architecture debt, while talks about mitigation of such debt using primarily rule based methods, do not cover HyperAutomation led mitigation.