Newslys: AI News Summaries leads this Finance news apps collection, followed by Layman - Simple News. Most of the listed apps show material revenue, and the ranking blends revenue, download, rating and growth readouts — the exact figures for every app open with the paid plan. This is a News sub-niche page built from apps whose names or publishers match finance, market, business, stocks signals.
New breakout Finance news apps is rebuilt daily from live App Store signals for the News category. Ranking uses Appdex's estimate engine rather than a public API: store metadata, review velocity, chart position and cross-platform signals feed a model whose output is published as a confidence range, not a fake-precise number. Where an app has a disclosed revenue figure from its own founder or filings, that anchor overrides the estimate. Figures refresh through the day, so this page reflects the most recent snapshot rather than a static list.
Newslys: AI News Summaries from Kerem Gonlkrmaz leads, with Layman - Simple News in second. This is a fragmented field — 36 different publishers across 36 slots, meaning no single studio dominates. The median store rating across the list is 5.0, which is a useful sanity check: a high-ranking app with a weak rating usually signals aggressive user acquisition rather than organic pull. None of these apps has crossed 100,000 ratings yet, so positions here can move quickly.
How accurate are the estimates? Every figure ships with a confidence range and a verified badge where a real disclosure backs it. Download estimates are inherently wider than revenue estimates, and the interface says so instead of hiding it.
How often does new breakout finance news apps change? The underlying data is collected daily. Leadership at the top tends to be stable over weeks; the middle of the list moves far more often, which is where most competitive opportunity sits.
Can I see the exact revenue figures? Headline apps show figures openly. For the long tail, figures sit behind a free trial — that boundary is what keeps the dataset worth maintaining.
