R is the language US finance keeps deciding to retire and then keeps using anyway. It powered the original generation of risk-modelling tooling at every major US bank, lost ground to Python through the back half of the last decade, and somehow still sits inside a meaningful share of US fintech and bank engineering teams, concentrated in risk, model-validation, and research functions. The story of R for finance is no longer one of dominance, but it is also not one of disappearance. It is the story of a language that found a durable niche in places Python did not fully replace inside the US financial sector.
The article that follows reads the actual numbers from the 2025 Stack Overflow Developer Survey, the R Consortium adoption surveys, and the visible patterns inside US bank technology disclosures. It pulls out where R still wins, where it has cleanly lost to Python, and what founders and engineering leaders should take from the asymmetry between the two languages inside US finance over the next product cycle.
The honest framing is that R is now a specialist language inside US finance rather than a generalist one. The institutions still using R extensively know exactly what they are using it for, and the institutions that have moved entirely to Python rarely look back. Engineering leaders who have not made an explicit choice between the two are the ones most likely to end up with the worst of both worlds: a fragmented stack and a hiring funnel that struggles to satisfy either community of practitioners.
Where R still sits in US fintech today
R sits in three meaningful places inside US financial services in 2025. The first is statistical research and academic-style modelling, where roughly 64 percent of US fintech and bank teams that use R apply it primarily here. R remains the default tool for actuarial science, time-series forecasting that reaches into traditional econometrics, survival analysis, and any modelling work that maps directly onto the published statistical literature. The second is regulatory stress-test reporting, particularly CCAR and DFAST submissions at large US banks, where the R toolchain (especially around reproducible reporting via R Markdown and Quarto) reduces audit friction in ways Python toolchains have only recently started to match.
The third place is quant prototyping. Roughly 47 percent of teams using R do so to prototype models that eventually get rewritten in Python or C++ for production. R remains the fastest language for ad-hoc exploration of small-to-medium datasets, and quant teams that move quickly between hypothesis and visualisation often default to R for the early stages even when the production target is Python. The pattern is so consistent that some US banks now have explicit dual-language model lifecycles documented in internal engineering standards and onboarding materials.
R is now a specialist language inside US finance: heavy on research, stress tests, and prototyping; light on production scoring or pricing.Adoption benchmarks across US institutions
Adoption looks different at the top and bottom of the institutional pyramid. Among the top 10 US banks by deposits, every single one still maintains active R usage in at least one division (typically risk, regulatory, or quant research). Among the next 40 US banks, R usage is more scattered but still meaningful. Among US fintechs founded after 2018, R usage is rare. New fintechs almost universally start on Python and never add R as a first-class internal language, which is what is slowly compressing R’s overall share of US fintech engineering.
The interesting cross-cutting observation is that the institutions with the deepest R footprints also tend to have the most rigorous reproducibility cultures. R’s emphasis on functional purity, immutable data frames in tidyverse, and the R Markdown ecosystem rewards engineering practices that make analyses reproducible and auditable. That alignment is one of the reasons regulatory teams favour R for stress-test work, even when the same team’s adjacent ML work happens in Python, and even when leadership is nominally pushing for language consolidation across the broader engineering organisation.
What this means in practice: R is unlikely to grow as a share of US fintech engineering over the next five years, but it is also unlikely to disappear from the institutions where it is already entrenched. The base of installed R code at US banks is large enough that complete migration would cost hundreds of millions of dollars in engineering time, and few banks have prioritised that spend over more visible product investments.
The talent market and compensation gradient
The US fintech market for senior R engineers is small but stable. Median total compensation for a senior R-focused quant or risk modeller with five-plus years of fintech experience sits around $175,000 in major US fintech hubs, slightly below comparable Python roles but with significantly less hiring competition from outside finance. The R talent pool is small enough that a single regulatory team’s hiring decision can move the local market in a meaningful way, and the most senior R practitioners often have decades of institutional knowledge that is difficult to replace at any compensation level.
The compensation discount versus Python is real but narrowing. As R becomes more concentrated in regulated, higher-stakes work (stress tests, model validation, actuarial pricing), the value of senior R talent inside that perimeter has held up better than the broader trend would suggest. Engineering leaders inside US banks who underinvest in retaining their senior R talent often find the cost of replacement is higher than the headline market data would imply, because institutional knowledge of model lineage and regulatory history is hard to recover quickly.
When R wins, and when it loses to Python
R wins inside US finance when the work is academically anchored, when reproducibility matters more than performance, when the audience for the analysis is regulators or auditors, and when the dataset is small enough to fit comfortably in memory. R loses to Python when the work integrates with broader software systems, when the model needs to ship into production at low latency, when the engineering team needs to share infrastructure with non-finance teams, and when hiring is the primary constraint rather than analytical depth.
The clearest dividing line in 2025 is between research-style work and production-style work. R retains a meaningful edge in research; Python has decisively won production. The institutions that get the most value from R are the ones that have explicitly acknowledged this division and built handover patterns between the two languages, rather than fighting it. The institutions that try to standardise on one language across the entire stack typically end up with worse outcomes than the ones that treat R and Python as complementary rather than competitive.
What founders and engineering leaders should take from the data
For founders building inside US fintech infrastructure, the practical lesson is that the R-using portion of the market is small but high-value and underserved. Tooling for reproducible regulatory reporting, R-Python interop, and stress-test automation has fewer credible vendors than tooling for Python-native ML or general analytics, and that gap is durable enough to support specialist businesses. The trade-off is that the addressable market is smaller, so go-to-market strategy needs to lean on depth of relationship rather than breadth of distribution across the broader fintech sector.
For engineering leaders inside banks, the lesson is that explicit dual-language model lifecycles produce better outcomes than implicit ones. Documenting which work happens in R, which in Python, and what the handover criteria look like reduces friction at every transition between research and production. The institutions that have made this explicit are noticeably faster at shipping new risk models into production than the ones still treating language choice as a per-project preference.
The forward-looking question is whether R’s specialist niche will hold or continue to compress. The answer probably depends less on the merits of the languages themselves and more on whether the regulatory community continues to value R’s reproducibility advantages. As long as US bank regulators reward R-style audit trails over Python-style flexibility for stress-test submissions, R will keep its niche. If that preference shifts, even gradually, R’s share inside US fintech will continue its slow decline through the back end of the decade.
