The Challenge: Weak Scenario Planning

Most finance teams know they should model more than a base case and a simple best/worst case. But building multiple financial scenarios is slow, manual work. Data sits in scattered spreadsheets, assumptions are locked in people’s heads, and every new scenario means copying tabs, updating drivers, and checking formulas. Under time pressure, teams end up testing only a few simplistic cases.

Traditional approaches to scenario planning in finance were built for a world with stable markets and annual planning cycles. In that world, a static model and a one-off budgeting exercise could be “good enough”. Today, demand, prices and supply conditions shift too quickly. Manual Excel gymnastics and one-size-fits-all planning tools cannot keep up with constantly changing assumptions, external data, and management questions like “What if we cut this product?” or “What if energy prices spike 20%?”

The business impact is significant. Weak scenario planning leaves companies poorly prepared for shocks in demand, prices, or supply. Strategic decisions are made without robust downside views or credible upside cases. Finance teams spend their time fixing links and reconciling versions instead of challenging assumptions or quantifying strategic options. The result: higher risk, slower decisions, and a competitive disadvantage against organisations that can translate new information into updated financial scenarios in days, not months.

The good news: this is a solvable problem. Modern AI tools like Claude can digest large planning files, understand narrative assumptions, and help you build, compare, and stress-test many more scenarios with far less manual work. At Reruption, we’ve seen how AI-first thinking can replace fragile spreadsheet chains with robust, dynamic planning workflows. In the rest of this page, you’ll find concrete, finance-specific guidance to turn scenario planning from a bottleneck into a strength.

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Our Assessment

A strategic assessment of the challenge and high-level tips how to tackle it.

From Reruption’s work building AI solutions for finance and strategy teams, we’ve learned that tools like Claude are most powerful when they augment — not replace — your existing financial models. Claude’s long-context capabilities allow it to read complex budget workbooks, planning decks, and assumption documents in one go, then help finance teams design richer scenarios, highlight risks, and explain sensitivities in clear business language.

Think in Drivers and Narratives, Not Just in Excel Tabs

Weak scenario planning often starts with the wrong mental model: copying the base case spreadsheet and tweaking a few percentage points. To use Claude for financial planning effectively, shift your focus to business drivers (volume, price, mix, FX, headcount, capacity, churn, etc.) and the narratives behind them. Claude is extremely good at turning narrative assumptions into structured sets of drivers and scenarios.

Begin by documenting the stories your management team actually worries about: “rapid demand drop in key region”, “input cost spike”, “slower hiring”, “aggressive expansion”. Feed these narratives plus your existing planning model structure into Claude and ask it to suggest which drivers to flex and what ranges are realistic. This moves your planning from cosmetic tweaks to scenario narratives tied to clear financial levers.

Use Claude as a Scenario Architect, Not a Black-Box Forecaster

Claude should not be treated as an oracle that spits out a perfect forecast. Instead, use it as a scenario architect that helps finance teams design, organise, and interrogate scenarios at scale. Claude can propose scenario frameworks (e.g. macro, operational, strategic), cluster assumptions, and define consistent naming and documentation conventions across scenarios.

This mindset keeps ownership of numbers and critical assumptions with Finance, while Claude does the heavy lifting around structure, documentation, and comparison. By clearly separating “AI helps us design and evaluate scenarios” from “humans sign off on the numbers”, you reduce model risk and increase trust in AI-supported planning.

Assess Data and Model Readiness Before Scaling AI

Before you roll out Claude broadly, evaluate how ready your planning models and data are. Claude can work with messy spreadsheets, but you’ll get far more value if core structures are stable: clear revenue and cost bridges, consistent account mapping, and a clean separation between input assumptions and calculation logic.

At Reruption, we often start by analysing a client’s existing budget and forecast workbooks with Claude itself: loading sample files, asking it to map the model structure, identify key drivers, and spot inconsistencies or circularities. This quick health check gives you a realistic view of what Claude can safely automate today, and what needs to be tidied up first to avoid scaling spreadsheet chaos.

Prepare the Finance Team to Work with AI, Not Around It

Even the best AI scenario planning setup fails if finance professionals don’t trust or know how to use it. Invest early in training that is practical and finance-specific: how to brief Claude, how to review its output, and how to translate management questions into structured prompts and scenario requests.

Clarify roles: who is responsible for scenario design, who validates key assumptions, who owns communication with stakeholders. Position Claude as a powerful assistant that extends the team’s capacity to explore more scenarios, not as a threat to expertise. When controllers, FP&A managers, and business partners see that Claude helps them deliver better insights faster, adoption follows naturally.

Manage Risk with Guardrails, Reviews, and Traceability

Using AI for financial planning introduces new risks: misinterpreted formulas, out-of-date files, or overconfident conclusions. Mitigate these with explicit governance for AI-supported planning. Define which tasks Claude may support (e.g. summarising scenarios, drafting risk analyses, proposing sensitivities) and which remain strictly under human control (final numbers, official forecasts, external guidance).

Implement a simple review workflow: Claude produces scenario documentation and comparisons; a designated finance lead reviews and signs off; changes are logged. Ensure all Claude interactions that influence planning decisions are captured (e.g. in a collaboration tool or version-controlled repository). These guardrails keep your auditors, CFO, and board comfortable while still unlocking the speed and depth benefits of AI.

Used with the right mindset, Claude turns weak, manual scenario planning into a structured, scalable process where finance teams can explore more scenarios, document assumptions clearly, and surface risks faster. Reruption combines this tool capability with deep implementation experience — from model assessment to workflow design and enablement — so your planning process becomes both more robust and more adaptable. If you want to test what Claude can do on your real budgeting files before a big rollout, our team can help you run a focused pilot and translate the results into a concrete roadmap.

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Real-World Case Studies

From Fintech to Energy: Learn how companies successfully use Claude.

Klarna

Fintech

Klarna, a leading fintech BNPL provider, faced enormous pressure from millions of customer service inquiries across multiple languages for its 150 million users worldwide. Queries spanned complex fintech issues like refunds, returns, order tracking, and payments, requiring high accuracy, regulatory compliance, and 24/7 availability. Traditional human agents couldn't scale efficiently, leading to long wait times averaging 11 minutes per resolution and rising costs. Additionally, providing personalized shopping advice at scale was challenging, as customers expected conversational, context-aware guidance across retail partners. Multilingual support was critical in markets like US, Europe, and beyond, but hiring multilingual agents was costly and slow. This bottleneck hindered growth and customer satisfaction in a competitive BNPL sector.

Lösung

Klarna partnered with OpenAI to deploy a generative AI chatbot powered by GPT-4, customized as a multilingual customer service assistant. The bot handles refunds, returns, order issues, and acts as a conversational shopping advisor, integrated seamlessly into Klarna's app and website. Key innovations included fine-tuning on Klarna's data, retrieval-augmented generation (RAG) for real-time policy access, and safeguards for fintech compliance. It supports dozens of languages, escalating complex cases to humans while learning from interactions. This AI-native approach enabled rapid scaling without proportional headcount growth.

Ergebnisse

  • 2/3 of all customer service chats handled by AI
  • 2.3 million conversations in first month alone
  • Resolution time: 11 minutes → 2 minutes (82% reduction)
  • CSAT: 4.4/5 (AI) vs. 4.2/5 (humans)
  • $40 million annual cost savings
  • Equivalent to 700 full-time human agents
  • 80%+ queries resolved without human intervention
Read case study →

Cleveland Clinic

Healthcare

At Cleveland Clinic, one of the largest academic medical centers, physicians grappled with a heavy documentation burden, spending up to 2 hours per day on electronic health record (EHR) notes, which detracted from patient care time. This issue was compounded by the challenge of timely sepsis identification, a condition responsible for nearly 350,000 U.S. deaths annually, where subtle early symptoms often evade traditional monitoring, leading to delayed antibiotics and 20-30% mortality rates in severe cases. Sepsis detection relied on manual vital sign checks and clinician judgment, frequently missing signals 6-12 hours before onset. Integrating unstructured data like clinical notes was manual and inconsistent, exacerbating risks in high-volume ICUs.

Lösung

Cleveland Clinic piloted Bayesian Health’s AI platform, a predictive analytics tool that processes structured and unstructured data (vitals, labs, notes) via machine learning to forecast sepsis risk up to 12 hours early, generating real-time EHR alerts for clinicians. The system uses advanced NLP to mine clinical documentation for subtle indicators. Complementing this, the Clinic explored ambient AI solutions like speech-to-text systems (e.g., similar to Nuance DAX or Abridge), which passively listen to doctor-patient conversations, apply NLP for transcription and summarization, auto-populating EHR notes to cut documentation time by 50% or more. These were integrated into workflows to address both prediction and admin burdens.

Ergebnisse

  • 12 hours earlier sepsis prediction
  • 32% increase in early detection rate
  • 87% sensitivity and specificity in AI models
  • 50% reduction in physician documentation time
  • 17% fewer false positives vs. physician alone
  • Expanded to full rollout post-pilot (Sep 2025)
Read case study →

Revolut

Fintech

Revolut faced escalating Authorized Push Payment (APP) fraud, where scammers psychologically manipulate customers into authorizing transfers to fraudulent accounts, often under guises like investment opportunities. Traditional rule-based systems struggled against sophisticated social engineering tactics, leading to substantial financial losses despite Revolut's rapid growth to over 35 million customers worldwide. The rise in digital payments amplified vulnerabilities, with fraudsters exploiting real-time transfers that bypassed conventional checks. APP scams evaded detection by mimicking legitimate behaviors, resulting in billions in global losses annually and eroding customer trust in fintech platforms like Revolut. Urgent need for intelligent, adaptive anomaly detection to intervene before funds were pushed.

Lösung

Revolut deployed an AI-powered scam detection feature using machine learning anomaly detection to monitor transactions and user behaviors in real-time. The system analyzes patterns indicative of scams, such as unusual payment prompts tied to investment lures, and intervenes by alerting users or blocking suspicious actions. Leveraging supervised and unsupervised ML algorithms, it detects deviations from normal behavior during high-risk moments, 'breaking the scammer's spell' before authorization. Integrated into the app, it processes vast transaction data for proactive fraud prevention without disrupting legitimate flows.

Ergebnisse

  • 30% reduction in fraud losses from APP-related card scams
  • Targets investment opportunity scams specifically
  • Real-time intervention during testing phase
  • Protects 35 million global customers
  • Deployed since February 2024
Read case study →

Stanford Health Care

Healthcare

Stanford Health Care, a leading academic medical center, faced escalating clinician burnout from overwhelming administrative tasks, including drafting patient correspondence and managing inboxes overloaded with messages. With vast EHR data volumes, extracting insights for precision medicine and real-time patient monitoring was manual and time-intensive, delaying care and increasing error risks. Traditional workflows struggled with predictive analytics for events like sepsis or falls, and computer vision for imaging analysis, amid growing patient volumes. Clinicians spent excessive time on routine communications, such as lab result notifications, hindering focus on complex diagnostics. The need for scalable, unbiased AI algorithms was critical to leverage extensive datasets for better outcomes.

Lösung

Partnering with Microsoft, Stanford became one of the first healthcare systems to pilot Azure OpenAI Service within Epic EHR, enabling generative AI for drafting patient messages and natural language queries on clinical data. This integration used GPT-4 to automate correspondence, reducing manual effort. Complementing this, the Healthcare AI Applied Research Team deployed machine learning for predictive analytics (e.g., sepsis, falls prediction) and explored computer vision in imaging projects. Tools like ChatEHR allow conversational access to patient records, accelerating chart reviews. Phased pilots addressed data privacy and bias, ensuring explainable AI for clinicians.

Ergebnisse

  • 50% reduction in time for drafting patient correspondence
  • 30% decrease in clinician inbox burden from AI message routing
  • 91% accuracy in predictive models for inpatient adverse events
  • 20% faster lab result communication to patients
  • Improved autoimmune detection by 1 year prior to diagnosis
Read case study →

Bank of America

Banking

Bank of America faced a high volume of routine customer inquiries, such as account balances, payments, and transaction histories, overwhelming traditional call centers and support channels. With millions of daily digital banking users, the bank struggled to provide 24/7 personalized financial advice at scale, leading to inefficiencies, longer wait times, and inconsistent service quality. Customers demanded proactive insights beyond basic queries, like spending patterns or financial recommendations, but human agents couldn't handle the sheer scale without escalating costs. Additionally, ensuring conversational naturalness in a regulated industry like banking posed challenges, including compliance with financial privacy laws, accurate interpretation of complex queries, and seamless integration into the mobile app without disrupting user experience. The bank needed to balance AI automation with human-like empathy to maintain trust and high satisfaction scores.

Lösung

Bank of America developed Erica, an in-house NLP-powered virtual assistant integrated directly into its mobile banking app, leveraging natural language processing and predictive analytics to handle queries conversationally. Erica acts as a gateway for self-service, processing routine tasks instantly while offering personalized insights, such as cash flow predictions or tailored advice, using client data securely. The solution evolved from a basic navigation tool to a sophisticated AI, incorporating generative AI elements for more natural interactions and escalating complex issues to human agents seamlessly. Built with a focus on in-house language models, it ensures control over data privacy and customization, driving enterprise-wide AI adoption while enhancing digital engagement.

Ergebnisse

  • 3+ billion total client interactions since 2018
  • Nearly 50 million unique users assisted
  • 58+ million interactions per month (2025)
  • 2 billion interactions reached by April 2024 (doubled from 1B in 18 months)
  • 42 million clients helped by 2024
  • 19% earnings spike linked to efficiency gains
Read case study →

Best Practices

Successful implementations follow proven patterns. Have a look at our tactical advice to get started.

Let Claude Map Your Existing Planning Model Before Changing Anything

Start by having Claude build a mental model of your current budgeting and forecasting setup. Upload a representative planning workbook (or share anonymised structures), plus any documentation or slide decks you use in the annual budget process. Ask Claude to identify key tabs, link structures, and driver relationships.

This gives you an immediate x-ray of how your model actually works versus how people think it works. Claude can point out where assumptions are hard-coded, where growth rates are inconsistent, and which inputs are reused across many calculations. Use this as the foundation for cleaner scenario design.

Example prompt to map your planning file:

You are assisting the FP&A team with understanding their planning model.

1) Read the attached Excel file(s) and describe:
- Main sheets and their purpose
- Key input assumptions and where they are entered
- Main calculation flows (e.g. revenue build-up, staff costs, capex & depreciation)
- Links between sheets that are critical for the P&L, balance sheet, and cash flow

2) Identify potential risks for scenario planning:
- Hard-coded assumptions instead of driver-based inputs
- Inconsistent formulas across years/business units
- Circular references or complex dependencies

3) Provide a concise summary we can share with the CFO.

Expected outcome: a structured overview of your planning model in 30–60 minutes instead of days of manual documentation, and a clear list of weaknesses to fix before scaling AI-driven scenarios.

Use Claude to Generate a Scenario Library Aligned to Your Strategy

Instead of inventing scenarios ad-hoc, build a reusable scenario library with Claude. Provide your strategic plan, risk register, and market outlook reports, then ask Claude to propose a structured set of scenarios: macro cases, operational disruptions, strategic choices, and regulatory shocks.

For each scenario, have Claude define the narrative, affected drivers, and quantitative ranges (which you will later validate). This creates a repeatable catalogue that you can reuse every planning cycle and adjust as your strategy evolves.

Example prompt for building a scenario library:

You are an FP&A co-pilot helping us design a scenario library.

Input:
- Strategic plan deck (attached)
- Latest risk register (attached)
- Current base-case financial forecast (attached)

Tasks:
1) Propose 8–12 distinct scenarios grouped into:
   - Macro/market
   - Operational/supply
   - Strategic decisions
2) For each scenario, define:
   - 3–5 sentence narrative
   - Key financial drivers impacted (volume, price, mix, FX, headcount, capex, etc.)
   - Suggested quantitative ranges for each driver (we will validate)
3) Output in a structured table we can paste into Excel.

Expected outcome: a consistent, strategy-aligned scenario set that moves you beyond simplistic “+/-10% revenue” thinking.

Automate Scenario Documentation and Management Updates

One of Claude’s biggest wins is eliminating the PowerPoint treadmill. After Finance has run numbers in Excel or your planning system, use Claude to turn raw outputs into clear, narrative scenario summaries for management.

Export key tables (P&L, cash flow, KPIs) for each scenario and paste them into Claude with a short description of the assumptions. Ask Claude to highlight key differences vs. base case, quantify impact on critical metrics (EBIT, FCF, leverage), and phrase it in language your CEO and business leaders actually use.

Example prompt for management-ready scenario summaries:

You are helping the CFO prepare a scenario planning update for the executive team.

Input:
- Base case P&L and cash flow tables (FY+3 years)
- Two alternative scenarios with key assumptions

Tasks:
1) Summarise each scenario in max. 5 bullet points.
2) For each scenario, explain the financial impact vs. base case on:
   - Revenue
   - EBIT
   - Free cash flow
   - Net debt / EBITDA
3) Highlight top 3 risks and top 3 opportunities per scenario.
4) Propose one slide outline per scenario for our management deck.

Expected outcome: management-ready scenario narratives in under an hour, freeing finance capacity for deeper analysis.

Have Claude Run Sensitivity Analyses and Spot Hidden Exposures

Beyond discrete scenarios, use Claude to explore sensitivities and hidden exposures. Provide your base case plus several scenario outputs and ask Claude to identify which drivers the financials are most sensitive to and where downside risk is concentrated.

You can also ask Claude to propose additional stress cases that specifically test these sensitive drivers: for example, combining a modest volume drop with a specific cost increase to reflect more realistic risk clusters rather than extreme, unlikely shocks.

Example prompt for sensitivity analysis:

You are an FP&A analyst reviewing scenario outputs.

Input:
- Base case and 5 scenario outputs (P&L, cash flow, key driver values)

Tasks:
1) Identify which financial KPIs are most sensitive to changes in:
   - Volume, price, and mix
   - FX rates
   - Personnel costs
   - Capex
2) Rank the drivers by impact on EBIT and free cash flow.
3) Suggest 3 additional stress-test scenarios that combine the most impactful drivers
   in a realistic but adverse way.
4) Summarise findings for the CFO in one short paragraph.

Expected outcome: a clear view of which levers matter most, enabling targeted management attention and better risk conversations.

Standardise Prompts and Templates for the Finance Team

To make Claude part of your finance workflow, don’t rely on everyone inventing their own prompts every time. Create a set of standard prompt templates for recurring tasks: model mapping, scenario creation, management summaries, and risk analyses. Store them in your collaboration tools or finance playbook so they can be reused and improved.

Encourage team members to log which prompts work best and where Claude struggled, then refine templates centrally. Over time, this evolves into a tailored “AI handbook for FP&A” that reflects your specific business model, KPIs, and planning cadence.

Example standard prompt for recurring quarterly scenario updates:

Quarterly Scenario Refresh – Standard Prompt

Context:
- We update base case and scenarios every quarter.

Tasks for Claude:
1) Compare the new actuals and updated base case to last quarter's forecast.
2) Identify which assumptions behind our existing scenarios are now outdated.
3) Propose updated parameter ranges for affected drivers.
4) Suggest which scenarios to retire, keep, or add.
5) Draft a short note for business unit leaders explaining the key changes.

Expected outcome: faster, more consistent quarterly updates with less variation in quality between team members.

Connect Claude to Your Planning Stack Gradually

In the longer term, you may want to integrate Claude more tightly with your planning environment (Excel-based workflows, planning tools, or data warehouse). Start simple: use exports and structured CSVs that Claude can read, then progressively move to API-driven workflows where outputs are written back into templates or reporting layers.

Work with IT and security to define where Claude can access real data vs. anonymised extracts, and ensure compliance requirements are met. A clear integration roadmap prevents ad-hoc experiments from turning into shadow IT, while still letting your team experiment and learn quickly.

Expected outcomes when these best practices are implemented: finance teams can typically cut scenario preparation time by 30–50%, expand from 2–3 simplistic cases to 8–15 well-structured scenarios per cycle, and provide management with clearer, faster insight into risks and options. The result is not just efficiency, but materially better financial decisions.

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Frequently Asked Questions

Claude improves scenario planning in finance by handling the high-effort, low-value parts of the work. It can read large Excel workbooks, map your model structure, propose scenario sets based on your strategy, and generate clear summaries comparing each case to the base plan.

Instead of building every scenario from scratch, your team defines the key drivers and assumptions, and Claude helps with structuring, documentation, and narrative output. This allows you to explore more scenarios in the same time, focus human judgment on validating assumptions, and give management a clearer view of risks and options.

You don’t need a data science team to start. The essential ingredients are: a reasonably structured planning model (usually in Excel or a planning tool), finance professionals who understand your business drivers, and access to Claude in a compliant environment.

Helpful skills include comfort with Excel exports, basic data hygiene, and the ability to describe planning assumptions in clear language. Reruption typically supports clients by setting up the first prompts, workflows, and templates so controllers and FP&A managers can work with Claude without needing to become AI experts.

In our experience, you can see tangible benefits within one planning cycle if you start focused. A targeted pilot on a specific use case — for example, documenting and comparing 4–6 scenarios for the next budget round — can usually be set up in a few weeks.

In the first 2–4 weeks, Claude typically helps you map your model, define a scenario library, and generate better management summaries. Over 1–3 subsequent cycles, you can standardise prompts, refine workflows, and gradually expand usage to more business units, making AI-supported planning part of your normal finance rhythm.

The direct usage cost of Claude is typically modest compared to overall finance budgets; the main investment is in setup, governance, and training. Most ROI comes from time saved in preparing scenarios, more informed strategic decisions, and better risk management.

Realistically, finance teams can often reduce manual scenario preparation time by 30–50%, avoid expensive last-minute rework when assumptions change, and support better capital allocation by quantifying more strategic options. The combination of efficiency gains and better decision quality usually makes the business case compelling, even in conservative environments.

Reruption works as a Co-Preneur alongside your finance and IT teams to turn Claude from an interesting tool into a working solution. We start with a 9.900€ AI PoC that uses your real planning files and processes to prove what’s technically and operationally feasible: model assessment, first workflows, and measurable impact.

From there, we help design and build the concrete solution: Claude prompts and templates tailored to your business, secure integration into your planning stack, and enablement for controllers and FP&A teams. With our Co-Preneur approach, we don’t stop at slides — we embed with your team, iterate quickly, and ship working automations and internal tools that strengthen your financial planning capabilities.

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