This story was originally published on HackerNoon at: https://hackernoon.com/neobank-card-fraud-case-study-what-the-novo-example-shows-about-card-risk-measurement.
How the Novo case reveals why card fraud must be measured across the lifecycle—balancing disputes, false positives, recovery rates, and customer experience.
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This story was written by: @sanya_kapoor. Learn more about this writer by checking @sanya_kapoor's about page, and for more stories, please visit hackernoon.com.

Card fraud in neobanks doesn’t start at the transaction—it builds across onboarding, login, funding, and behavior signals. The Novo case shows why measuring only chargebacks is flawed. Strong fraud systems balance loss prevention with user experience by tracking false positives, recovery rates, and lifecycle signals. The goal isn’t blocking more—it’s making smarter decisions with less friction.

This story was originally published on HackerNoon at: https://hackernoon.com/how-satish-kumar-is-redefining-real-time-payment-systems-at-scale.
Discover how Satish Kumar transformed payment systems with scalable architecture, reducing latency and enabling real-time, resilient financial infrastructure.
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This story was written by: @sanya_kapoor. Learn more about this writer by checking @sanya_kapoor's about page, and for more stories, please visit hackernoon.com.

Satish Kumar transformed legacy payment systems into a scalable, real-time architecture handling billions in transactions. By replacing monolithic systems with a modular, plug-and-play design, he reduced latency, improved fraud response speed, and enabled faster deployments. His work at Capital One–Discover integration showcases how strong engineering leadership can modernize financial infrastructure while improving reliability and customer experience.

This story was originally published on HackerNoon at: https://hackernoon.com/we-were-promised-jetpacks-why-ai-isnt-accelerating-feature-delivery.
Despite AI coding tools generating more code than ever, engineering productivity lags because these tools excel at building, not debugging or operating systems.
Check more stories related to programming at: https://hackernoon.com/c/programming. You can also check exclusive content about #software-development, #ai-code-generation, #ai-debugging-challenges, #software-lifecycle-ai-tools, #agentic-coding-limitations, #sdlc-ai-productivity-gap, #ai-code-maintenance-costs, #good-company, and more.

This story was written by: @playerzero. Learn more about this writer by checking @playerzero's about page, and for more stories, please visit hackernoon.com.

AI coding tools have transformed the creative process of writing code, enabling rapid prototyping and automation. Yet productivity gains remain elusive because AI struggles with debugging, testing, and operationalizing software. Current models excel at building “up” but fail at the scientific, investigative “building down” required for reliable production systems. Future tools must bridge this gap.