To combat money laundering and terrorist financing globally, the World Bank and Nepal Rastra Bank (NRB) collaborated to implement the goAML system. In this process, NRB directed Banking & Financial Institutions (BFI) to follow the NRB guidelines, monitor suspicious transactions and money laundering activities, and submit required reports. One significant challenge faced by the BFIs during implementation was related to the scattered and incomplete nature of the data. The manual reporting and processing time of the banks was also significantly prolonged due to the dispersed data. Another major issue that the BFIs faced was the absence of a national Anti-Money Laundering (AML)/ Combating the Financing of Terrorism (CFT) System in Nepal and the cost of acquiring an international System was muchmore expensive for the BFIs.
Why Replacing Developers with AI Is Going Wrong and What Smart Leaders Are Learning Instead?
AI has not removed engineering effort. It has relocated it. Time once spent writing code is now spent reviewing, debugging, securing and unpicking it. Organisations that rushed to replace developers with AI are discovering that they have traded clear headcount savings for hidden technical debt, fragile systems and a greater dependence on their most experienced engineers. Vibe coding with AI can be powerful for proofs of concept and investor friendly demos, but only when the code is treated as disposable experiment tooling rather than the foundation of a production platform. The organisations pulling ahead are not asking how to replace developers with AI. They are asking how to combine machine speed with human judgement, using rapid AI powered experimentation at the edges and disciplined engineering at the core to build systems that are faster, safer and more resilient.
