The post the programmable bank for the digital asset era appeared on BitcoinEthereumNews.com. Pave Bank has announced a fundraising of 39 million dollars in a Series A round led by Accel, with participation from prominent investors such as Tether Investments, Quona Capital, Wintermute, Helios Digital Ventures, Financial Technology Partners, Yolo Investments, Kazea Fund, and GC&H Investments.  This operation brings the total funding obtained by the bank to over 44 million dollars, marking a crucial step in the expansion of the world’s first programmable bank, designed to seamlessly integrate traditional finance and digital assets. Founded in December 2023 by former executives of BigPay, Monzo, and VP Bank, Pave Bank presents itself as a concrete response to the growing fragmentation of financial services. Until now, companies were forced to turn to different providers for traditional banking management, custody of digital assets, and payment or liquidity services, resulting in operational inefficiencies, compliance risks, and slowed access to liquidity.  Pave Bank overcomes these obstacles by offering a unique and regulated platform where corporate and institutional clients can manage both fiat and digital assets under one roof. Pave Bank: the innovation of the programmable bank The core of Pave Bank‘s offering is an integrated platform that combines commercial banking services—such as deposit accounts, extensive payment coverage, FX liquidity, payment card issuance, and treasury management—with institutional-level digital asset management tools, an instant settlement network, and an OTC trading desk. Instead of having to coordinate multiple providers for fiat management, custody, and liquidity, clients can operate on both fronts through a single regulatory framework, one compliance standard, and a unified interface. According to Salim Dhanani, co-founder and CEO of Pave Bank: “The global financial system is shifting towards regulated on-chain finance, and institutions need a reliable bridge between the old and the new. We have built a multi-asset bank that merges the stability and oversight of traditional finance with the… The post the programmable bank for the digital asset era appeared on BitcoinEthereumNews.com. Pave Bank has announced a fundraising of 39 million dollars in a Series A round led by Accel, with participation from prominent investors such as Tether Investments, Quona Capital, Wintermute, Helios Digital Ventures, Financial Technology Partners, Yolo Investments, Kazea Fund, and GC&H Investments.  This operation brings the total funding obtained by the bank to over 44 million dollars, marking a crucial step in the expansion of the world’s first programmable bank, designed to seamlessly integrate traditional finance and digital assets. Founded in December 2023 by former executives of BigPay, Monzo, and VP Bank, Pave Bank presents itself as a concrete response to the growing fragmentation of financial services. Until now, companies were forced to turn to different providers for traditional banking management, custody of digital assets, and payment or liquidity services, resulting in operational inefficiencies, compliance risks, and slowed access to liquidity.  Pave Bank overcomes these obstacles by offering a unique and regulated platform where corporate and institutional clients can manage both fiat and digital assets under one roof. Pave Bank: the innovation of the programmable bank The core of Pave Bank‘s offering is an integrated platform that combines commercial banking services—such as deposit accounts, extensive payment coverage, FX liquidity, payment card issuance, and treasury management—with institutional-level digital asset management tools, an instant settlement network, and an OTC trading desk. Instead of having to coordinate multiple providers for fiat management, custody, and liquidity, clients can operate on both fronts through a single regulatory framework, one compliance standard, and a unified interface. According to Salim Dhanani, co-founder and CEO of Pave Bank: “The global financial system is shifting towards regulated on-chain finance, and institutions need a reliable bridge between the old and the new. We have built a multi-asset bank that merges the stability and oversight of traditional finance with the…

the programmable bank for the digital asset era

Pave Bank has announced a fundraising of 39 million dollars in a Series A round led by Accel, with participation from prominent investors such as Tether Investments, Quona Capital, Wintermute, Helios Digital Ventures, Financial Technology Partners, Yolo Investments, Kazea Fund, and GC&H Investments. 

This operation brings the total funding obtained by the bank to over 44 million dollars, marking a crucial step in the expansion of the world’s first programmable bank, designed to seamlessly integrate traditional finance and digital assets.

Founded in December 2023 by former executives of BigPay, Monzo, and VP Bank, Pave Bank presents itself as a concrete response to the growing fragmentation of financial services. Until now, companies were forced to turn to different providers for traditional banking management, custody of digital assets, and payment or liquidity services, resulting in operational inefficiencies, compliance risks, and slowed access to liquidity. 

Pave Bank overcomes these obstacles by offering a unique and regulated platform where corporate and institutional clients can manage both fiat and digital assets under one roof.

Pave Bank: the innovation of the programmable bank

The core of Pave Bank‘s offering is an integrated platform that combines commercial banking services—such as deposit accounts, extensive payment coverage, FX liquidity, payment card issuance, and treasury management—with institutional-level digital asset management tools, an instant settlement network, and an OTC trading desk. Instead of having to coordinate multiple providers for fiat management, custody, and liquidity, clients can operate on both fronts through a single regulatory framework, one compliance standard, and a unified interface.

According to Salim Dhanani, co-founder and CEO of Pave Bank:

Automation and real-time management

Companies that choose Pave Bank can manage both fiat and digital assets in real-time, automate treasury operations, and reduce reliance on intermediaries. An exchange or a market maker, for example, can manage digital assets, fiat, and fixed-income treasury products in one place, interacting with their counterparts through the Pave Network

This approach enhances operational liquidity and reduces operational risks. Companies exploring the use of stablecoins in their operations can thus unify digital and fiat treasury with regulatory clarity and complete security, optimizing speed, control, and cost efficiency.

A sustainable and technologically advanced growth

Since its launch, Pave Bank has focused on a sustainable and technology-driven operational model, avoiding the race for growth at all costs. A remarkable achievement: the bank has reached profitability in seven of the first nine months of activity, a rare milestone for a newly licensed bank. This was made possible through the intensive use of automation and artificial intelligence in all key functions, from software engineering to compliance, from operations to treasury management. With a team of just over fifty people, the goal is to continue scaling intelligently, maintaining profitability and a central focus on risk and compliance.

Salim Dhanani highlights how Pave Bank’s clients are large companies and sophisticated institutions, who expect their bank to have the same speed and adaptability as the tech companies they collaborate with, but with the security, compliance, and oversight of a regulated financial institution. “This is the gap we are bridging,” says Dhanani.

Investor Support

Investors share the vision of Pave Bank. Rachit Parekh, partner at Accel, highlights the need for a regulated and full-reserve banking approach at the intersection of fiat and digital assets, emphasizing the role of Pave Bank as a pioneer of this infrastructural transformation. 

Ganesh Rengaswamy of Quona Capital adds that Pave’s programmable bank, with its full-reserve approach, combines the best of traditional banking and digital assets, catalyzing the adoption of stablecoins and promoting financial inclusion in global markets.

Regulation and Global Vision

The capital raising reflects the growing institutional demand for a new type of financial institution, capable of managing regulated digital assets – from stablecoins to bitcoin – while offering all the services expected from a commercial bank: instant settlement, programmable flows, and prudential supervision. Pave Bank has operated from the outset within regulatory frameworks for digital assets and, as regulations mature, works closely with authorities to ensure compliance and interoperability between different jurisdictions.

International Expansion and New Products

Looking to the future, Pave Bank intends to expand its regulatory coverage, enrich the range of programmable treasury products and institutional financial services, as well as integrate with major financial and digital asset ecosystems. The long-term goal is to become the global reference bank for companies and institutions, the meeting point between traditional and digital economy.

A Bridge Between Two Worlds: The Mission of Pave Bank

Pave Bank presents itself as a fully regulated commercial bank, built for the modern economy. It allows corporates and institutions to manage regulated fiat and digital assets side by side, thanks to an instant settlement network, stablecoin and digital asset management, programmable money flows, comprehensive payment solutions, and corporate treasury management. The mission is clear: to move money securely, intelligently, and automatically through global financial systems.

Headquartered in Singapore, with a banking license issued by the National Bank of Georgia and offices in London, Pave Bank is expanding its presence in the United Arab Emirates, the United States, Hong Kong, and the European Economic Area. A growth path that confirms the desire to be a key player in building the new global financial architecture, where tradition and innovation finally meet.

Source: https://en.cryptonomist.ch/2025/10/23/pave-bank-raises-39-million-the-revolution-of-programmable-banking-for-the-digital-asset-era/

Disclaimer: The articles reposted on this site are sourced from public platforms and are provided for informational purposes only. They do not necessarily reflect the views of MEXC. All rights remain with the original authors. If you believe any content infringes on third-party rights, please contact service@support.mexc.com for removal. MEXC makes no guarantees regarding the accuracy, completeness, or timeliness of the content and is not responsible for any actions taken based on the information provided. The content does not constitute financial, legal, or other professional advice, nor should it be considered a recommendation or endorsement by MEXC.

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Summarize Any Stock’s Earnings Call in Seconds Using FMP API

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Turn lengthy earnings call transcripts into one-page insights using the Financial Modeling Prep APIPhoto by Bich Tran Earnings calls are packed with insights. They tell you how a company performed, what management expects in the future, and what analysts are worried about. The challenge is that these transcripts often stretch across dozens of pages, making it tough to separate the key takeaways from the noise. With the right tools, you don’t need to spend hours reading every line. By combining the Financial Modeling Prep (FMP) API with Groq’s lightning-fast LLMs, you can transform any earnings call into a concise summary in seconds. The FMP API provides reliable access to complete transcripts, while Groq handles the heavy lifting of distilling them into clear, actionable highlights. In this article, we’ll build a Python workflow that brings these two together. You’ll see how to fetch transcripts for any stock, prepare the text, and instantly generate a one-page summary. Whether you’re tracking Apple, NVIDIA, or your favorite growth stock, the process works the same — fast, accurate, and ready whenever you are. Fetching Earnings Transcripts with FMP API The first step is to pull the raw transcript data. FMP makes this simple with dedicated endpoints for earnings calls. If you want the latest transcripts across the market, you can use the stable endpoint /stable/earning-call-transcript-latest. For a specific stock, the v3 endpoint lets you request transcripts by symbol, quarter, and year using the pattern: https://financialmodelingprep.com/api/v3/earning_call_transcript/{symbol}?quarter={q}&year={y}&apikey=YOUR_API_KEY here’s how you can fetch NVIDIA’s transcript for a given quarter: import requestsAPI_KEY = "your_api_key"symbol = "NVDA"quarter = 2year = 2024url = f"https://financialmodelingprep.com/api/v3/earning_call_transcript/{symbol}?quarter={quarter}&year={year}&apikey={API_KEY}"response = requests.get(url)data = response.json()# Inspect the keysprint(data.keys())# Access transcript contentif "content" in data[0]: transcript_text = data[0]["content"] print(transcript_text[:500]) # preview first 500 characters The response typically includes details like the company symbol, quarter, year, and the full transcript text. If you aren’t sure which quarter to query, the “latest transcripts” endpoint is the quickest way to always stay up to date. Cleaning and Preparing Transcript Data Raw transcripts from the API often include long paragraphs, speaker tags, and formatting artifacts. Before sending them to an LLM, it helps to organize the text into a cleaner structure. Most transcripts follow a pattern: prepared remarks from executives first, followed by a Q&A session with analysts. Separating these sections gives better control when prompting the model. In Python, you can parse the transcript and strip out unnecessary characters. A simple way is to split by markers such as “Operator” or “Question-and-Answer.” Once separated, you can create two blocks — Prepared Remarks and Q&A — that will later be summarized independently. This ensures the model handles each section within context and avoids missing important details. 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Summarize the following earnings call section for {symbol} ({quarter} {year}). Be factual and concise. 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If a section is extremely long, chunk at ~5–8k tokens, summarize each chunk with the same prompt, then ask the model to merge chunk summaries into one section summary before producing the final one-pager. If you also fetched headline numbers (EPS/revenue, guidance) earlier, prepend them to the prompt as brief context to help the model anchor on the right outcomes. Building the End-to-End Pipeline At this point, we have all the building blocks: the FMP API to fetch transcripts, a cleaning step to structure the data, and Groq LLM to generate concise summaries. The final step is to connect everything into a single workflow that can take any ticker and return a one-page earnings call summary. The flow looks like this: Input a stock ticker (for example, NVDA). Use FMP to fetch the latest transcript. Clean and split the text into Prepared Remarks and Q&A. Send each section to Groq for summarization. Merge the outputs into a neatly formatted earnings one-pager. Here’s how it comes together in Python: def summarize_earnings_call(symbol, quarter, year, api_key, groq_key): # Step 1: Fetch transcript from FMP url = f"https://financialmodelingprep.com/api/v3/earning_call_transcript/{symbol}?quarter={quarter}&year={year}&apikey={api_key}" resp = requests.get(url) resp.raise_for_status() data = resp.json() if not data or "content" not in data[0]: return f"No transcript found for {symbol} {quarter} {year}" text = data[0]["content"] # Step 2: Clean and split clean_text = re.sub(r'\s+', ' ', text).strip() if "Question-and-Answer" in clean_text: prepared, qna = clean_text.split("Question-and-Answer", 1) else: prepared, qna = clean_text, "" # Step 3: Summarize with Groq prepared_summary = summarize_section(prepared, symbol, quarter, year) qna_summary = summarize_section(qna, symbol, quarter, year) # Step 4: Merge into final one-pager return f"""# {symbol} Earnings One-Pager — {quarter} {year}## Prepared Remarks{prepared_summary}## Q&A Highlights{qna_summary}""".strip()# Example runprint(summarize_earnings_call("NVDA", 2, 2024, API_KEY, GROQ_API_KEY)) With this setup, generating a summary becomes as simple as calling one function with a ticker and date. You can run it inside a notebook, integrate it into a research workflow, or even schedule it to trigger after each new earnings release. Free Stock Market API and Financial Statements API... Conclusion Earnings calls no longer need to feel overwhelming. With the Financial Modeling Prep API, you can instantly access any company’s transcript, and with Groq LLM, you can turn that raw text into a sharp, actionable summary in seconds. This pipeline saves hours of reading and ensures you never miss the key results, guidance, or risks hidden in lengthy remarks. Whether you track tech giants like NVIDIA or smaller growth stocks, the process is the same — fast, reliable, and powered by the flexibility of FMP’s data. Summarize Any Stock’s Earnings Call in Seconds Using FMP API was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story
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