The United Kingdom government has abandoned plans to make digital identification cards mandatory for workers, marking a significant policy reversal just months The United Kingdom government has abandoned plans to make digital identification cards mandatory for workers, marking a significant policy reversal just months

UK Drops Mandatory Digital ID Requirement for Workers After Public Backlash

Opposition parties have labeled this the Labour government’s 13th major U-turn since taking office in 2024, though exact counts vary.

The policy shift means workers will no longer be required to register with a government-issued digital ID system to prove their right to work. Instead, they can choose from multiple verification methods, including biometric passports, electronic visas, or commercial digital verification apps.

The Rise and Fall of the “BritCard”

Prime Minister Starmer announced the digital ID scheme on September 26, 2025, at the Global Progress Action Summit in London. The plan, nicknamed the “BritCard,” would have required all workers to hold a government-issued digital credential stored on their smartphones.

“Let me spell it out, you will not be able to work in the United Kingdom if you do not have digital ID,” Starmer declared at the announcement. The government framed the policy as a way to combat illegal immigration and prevent migrants from working in the “shadow economy.”

The digital ID would have included personal details such as name, date of birth, nationality or residency status, and a photo. It was designed to work through the Gov.uk Wallet app, which would also store digital driving licenses. The government claimed the system would be privacy-focused with no centralized database.

Source: @RupertLowe10

However, public support collapsed rapidly after the announcement. According to polling by More in Common, net support for mandatory digital IDs plummeted from +35% in early summer 2025 to -14% by late September. By October, only 31% of Britons supported the plan, down from 53% in June.

Historic Public Opposition

A parliamentary petition against mandatory digital ID cards gathered 2.9 million signatures, making it one of the largest petitions in UK parliamentary history. Opposition came from across the political spectrum, including Conservative, Liberal Democrat, Reform UK, Scottish National Party, and Sinn Féin leaders.

Civil liberties groups strongly criticized the proposal. Big Brother Watch described the plans as “wholly unBritish” and warned they would create a “domestic mass surveillance infrastructure.” Even Labour backbenchers publicly opposed the scheme, with Labour MP Rebecca Long Bailey expressing concerns about “building an infrastructure that can follow us, link our most sensitive information and expand state control over all our lives.”

Cabinet ministers reportedly described the digital ID plan as “incoherent,” “a fantasy,” and “too expensive and complicated.” One frontbencher called the eventual U-turn a disaster, reflecting deep frustration within Starmer’s own government.

What the Policy Change Means

On January 13-14, 2026, the Cabinet Office confirmed that digital ID would become optional rather than mandatory. A government spokesperson stated: “We are committed to mandatory digital right to work checks. Currently, right to work checks include a hodgepodge of paper-based systems with no record of checks ever taking place. This is open to fraud and abuse.”

The key distinction is that while employers must still conduct digital right-to-work checks by 2029, workers are not forced to use a specific government-issued digital ID. They can verify their employment eligibility through electronic visas, biometric passports, or certified commercial digital verification services.

Chancellor Rachel Reeves told BBC Breakfast the government is “pretty relaxed” about what form of digital documentation people use to prove their right to work. Transport Secretary officials confirmed that while mandatory digital checks remain the goal, these do not necessarily require the government’s digital ID system.

The government plans to launch a public consultation shortly to determine the final details of the digital verification system. Implementation is still targeted for 2029, by the end of the current parliamentary term.

Political Fallout and Criticism

Opposition parties seized on the reversal as evidence of government weakness. Conservative Shadow Cabinet Office Minister Mike Wood stated: “Keir Starmer’s spinelessness is becoming a pattern, not an exception. What was sold as a tough measure to tackle illegal working is now set to become yet another costly, ill-thought-out experiment abandoned at the first sign of pressure.”

Liberal Democrat Cabinet Office spokesperson Lisa Smart said: “Number 10 must be bulk ordering motion sickness tablets at this rate to cope with all their U-turns. It was clear right from the start this was a proposal doomed to failure.”

Reform UK leader Nigel Farage called the abandonment of mandatory digital ID “a victory for individual liberty against a ghastly, authoritarian government,” though he added that Reform UK would scrap the entire scheme if in power.

Civil liberties advocates welcomed the change. Big Brother Watch Director Silkie Carlo praised Starmer’s reported U-turn on “intrusive, expensive and unnecessary digital IDs.”

Context and Future Implications

The UK government has a troubled history with digital ID systems. The previous Gov.uk Verify platform, launched in 2013, cost over £220 million and failed to meet user adoption targets before being officially shut down. The government has not provided cost estimates for the current digital ID initiative, though the Office for Budget Responsibility has indicated the scheme would require £1.8 billion over three years, to be funded from existing departmental budgets.

Under current UK law, employers can face fines up to £45,000 for hiring unauthorized workers without proper status checks. The Border Security Act 2025 increased penalties to £60,000 per worker for some violations. The government reports that illegal working arrests have increased 50% under the current administration.

The digital ID scheme would have built on existing government infrastructure, including Gov.uk One Login (which already has 12 million users) and the Gov.uk Wallet app announced in January 2025. The government cited Estonia’s successful digital ID system as inspiration for the UK model.

Josh Simons, a Cabinet Office minister, has been appointed to lead the development of the revised digital identity program and will oversee the upcoming public consultation.

The Bottom Line

The UK’s digital ID reversal demonstrates how quickly public opinion can force policy changes in democratic systems. What began as a flagship immigration enforcement measure ended as an optional convenience feature after facing opposition from nearly 3 million petition signers, opposition parties, civil liberties groups, and even members of the governing Labour Party. While digital right-to-work checks will still become mandatory by 2029, British workers will maintain the freedom to choose how they verify their employment eligibility rather than being forced into a single government-controlled system.

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

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

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. Here’s a small example of how you might start preparing the data: import re# Example: using the transcript_text we fetched earliertext = transcript_text# Remove extra spaces and line breaksclean_text = re.sub(r'\s+', ' ', text).strip()# Split sections (this is a heuristic; real-world transcripts vary slightly)if "Question-and-Answer" in clean_text: prepared, qna = clean_text.split("Question-and-Answer", 1)else: prepared, qna = clean_text, ""print("Prepared Remarks Preview:\n", prepared[:500])print("\nQ&A Preview:\n", qna[:500]) With the transcript cleaned and divided, you’re ready to feed it into Groq’s LLM. Chunking may be necessary if the text is very long. A good approach is to break it into segments of a few thousand tokens, summarize each part, and then merge the summaries in a final pass. Summarizing with Groq LLM Now that the transcript is clean and split into Prepared Remarks and Q&A, we’ll use Groq to generate a crisp one-pager. The idea is simple: summarize each section separately (for focus and accuracy), then synthesize a final brief. Prompt design (concise and factual) Use a short, repeatable template that pushes for neutral, investor-ready language: You are an equity research analyst. Summarize the following earnings call sectionfor {symbol} ({quarter} {year}). Be factual and concise.Return:1) TL;DR (3–5 bullets)2) Results vs. guidance (what improved/worsened)3) Forward outlook (specific statements)4) Risks / watch-outs5) Q&A takeaways (if present)Text:<<<{section_text}>>> Python: calling Groq and getting a clean summary Groq provides an OpenAI-compatible API. Set your GROQ_API_KEY and pick a fast, high-quality model (e.g., a Llama-3.1 70B variant). We’ll write a helper to summarize any text block, then run it for both sections and merge. import osimport textwrapimport requestsGROQ_API_KEY = os.environ.get("GROQ_API_KEY") or "your_groq_api_key"GROQ_BASE_URL = "https://api.groq.com/openai/v1" # OpenAI-compatibleMODEL = "llama-3.1-70b" # choose your preferred Groq modeldef call_groq(prompt, temperature=0.2, max_tokens=1200): url = f"{GROQ_BASE_URL}/chat/completions" headers = { "Authorization": f"Bearer {GROQ_API_KEY}", "Content-Type": "application/json", } payload = { "model": MODEL, "messages": [ {"role": "system", "content": "You are a precise, neutral equity research analyst."}, {"role": "user", "content": prompt}, ], "temperature": temperature, "max_tokens": max_tokens, } r = requests.post(url, headers=headers, json=payload, timeout=60) r.raise_for_status() return r.json()["choices"][0]["message"]["content"].strip()def build_prompt(section_text, symbol, quarter, year): template = """ You are an equity research analyst. Summarize the following earnings call section for {symbol} ({quarter} {year}). Be factual and concise. Return: 1) TL;DR (3–5 bullets) 2) Results vs. guidance (what improved/worsened) 3) Forward outlook (specific statements) 4) Risks / watch-outs 5) Q&A takeaways (if present) Text: <<< {section_text} >>> """ return textwrap.dedent(template).format( symbol=symbol, quarter=quarter, year=year, section_text=section_text )def summarize_section(section_text, symbol="NVDA", quarter="Q2", year="2024"): if not section_text or section_text.strip() == "": return "(No content found for this section.)" prompt = build_prompt(section_text, symbol, quarter, year) return call_groq(prompt)# Example usage with the cleaned splits from Section 3prepared_summary = summarize_section(prepared, symbol="NVDA", quarter="Q2", year="2024")qna_summary = summarize_section(qna, symbol="NVDA", quarter="Q2", year="2024")final_one_pager = f"""# {symbol} Earnings One-Pager — {quarter} {year}## Prepared Remarks — Key Points{prepared_summary}## Q&A Highlights{qna_summary}""".strip()print(final_one_pager[:1200]) # preview Tips that keep quality high: Keep temperature low (≈0.2) for factual tone. 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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Medium2025/09/18 14:40