The Challenge: Inaccurate Cash Flow Projections

Many finance teams still build cash flow projections in large spreadsheets, driven by high-level DSO assumptions, budget figures, and generic payment terms. What looks tidy on paper rarely reflects reality: customers pay earlier or later than expected, seasonality amplifies peaks and troughs, and contract-specific clauses change inflow timing. The result is a forecast that feels precise but is only loosely connected to real payment behavior.

Traditional approaches struggle because they are too static and too manual. Updating a 12–18 month cash flow file means stitching together ERP exports, bank statements, and pipeline data, then manually adjusting formulas. There is rarely time to analyze historical patterns by customer, region, or product, or to model how contract terms and incentive schemes actually impact cash timing. As the business becomes more complex, the gap between the plan and what happens in the bank account grows wider.

The business impact is significant. Inaccurate cash flow forecasts create surprise liquidity gaps, emergency funding needs, or idle cash sitting uninvested. It becomes harder to optimize working capital, financing, and investment decisions. Leadership loses confidence in financial planning because every board meeting brings a new explanation for why actual cash diverged from plan. Over time, this undermines finance’s role as a strategic partner to the business and makes it more difficult to navigate volatile markets.

The good news: this problem is real but absolutely solvable. With the right use of AI for cash flow forecasting, finance teams can connect historical payment behavior, contract details, and operational drivers into one coherent view. At Reruption, we’ve seen how applied AI can transform messy financial data into reliable, scenario-ready insights. In the sections below, you’ll find practical guidance on using Claude to rebuild your cash flow forecasting approach around data, drivers, and dynamic planning.

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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-first financial workflows, we’ve seen that Claude is particularly effective when it augments, not replaces, existing planning models. Instead of throwing away your spreadsheets, Claude helps you interpret large ERP exports, reconcile inflows and outflows, and stress-test cash flow projections against realistic payment behavior and scenarios. Our hands-on engineering experience shows that the real value comes from combining Claude’s language understanding with robust financial logic and clear governance.

Treat Claude as a Financial Co-Pilot, Not an Autopilot

Claude is powerful at interpreting complex spreadsheets, contracts, and transaction histories, but it should sit inside a controlled financial planning and analysis process. Use it to surface patterns, inconsistencies, and risks in your cash flow projections, while keeping human finance experts in the loop for judgment calls. This mindset avoids the trap of delegating accountability for liquidity to a black box.

Strategically, define up front which decisions Claude can inform (e.g. revised DSO assumptions by segment, scenario narratives, risk flags) and which remain firmly with your treasury and FP&A teams. Make the AI’s role explicit in your planning calendar so stakeholders understand that the model is a co-pilot improving insight quality, not a replacement for sound financial governance.

Build Around Drivers and Behavior, Not Static Assumptions

Most inaccurate cash flow projections share a root cause: they are based on static, top-down assumptions. A strategic implementation of AI in cash flow planning instead anchors on underlying drivers: customer payment behavior, seasonality, contract terms, discount policies, and operational milestones. Claude is well suited to uncovering and describing these drivers from historical data and narrative inputs.

Before rolling out any tooling, align finance, sales, and operations on which drivers really move cash. Then use Claude to translate those drivers into clear narratives and parameter suggestions that feed your forecasting models. This shifts the organization from a budgeting culture (“what should happen”) to a behavior-based forecasting culture (“what usually happens and why”).

Get Your Data Supply Chain Finance-Ready

Claude can work with messy exports, but the quality of your AI cash flow projections still depends on your data supply chain. Strategically, you need clarity on the authoritative sources for invoices, collections, contract terms, pipeline data, and bank transactions. Equally important is a repeatable way of extracting and anonymizing data so finance can use Claude securely and consistently.

Invest time in defining data ownership (who curates what), refresh frequency (weekly, monthly), and minimum quality thresholds. Reruption often helps clients design lightweight pipelines that feed Claude with ERP exports and planning files, without waiting for a multi-year data lake project. This ensures AI insights are trustworthy enough to influence real liquidity decisions.

Prepare the Team for Narrative-Driven Scenario Planning

Claude’s strength is not just number crunching; it is narrative scenario analysis. Strategically, this shifts how finance collaborates with the business. Instead of presenting one static cash flow plan, your team can co-create several cash flow scenarios with clear, documented assumptions: delayed collections, accelerated growth, pricing changes, or different financing strategies.

To get value from this, prepare stakeholders to think in scenarios, not point estimates. Train finance business partners to prompt Claude with realistic narratives and to use its output to facilitate discussions with management. The organization needs to accept that the goal is not a “perfect forecast” but a robust set of scenarios with transparent logic.

Address Risk, Compliance, and Explainability Upfront

Any use of AI in finance must address risk and compliance from the start. Strategically define where Claude is allowed to see real data, where anonymization is mandatory, and which outputs become part of your official planning cycle. Establish clear guardrails so AI-powered cash flow forecasting remains explainable and auditable.

Put in place a simple model governance framework: documentation of prompts and workflows, versioning of assumptions, and periodic review of forecast accuracy versus actuals. This not only reduces operational risk but also gives controllers and auditors confidence that Claude’s contributions can be traced, challenged, and improved over time.

Used with the right strategy, Claude becomes a powerful lever to turn static, assumption-heavy spreadsheets into dynamic, scenario-ready cash flow projections. It helps finance teams understand real payment behavior, reconcile inconsistencies, and communicate risks and options in clear language to management. Reruption brings the engineering depth and Co-Preneur mindset needed to embed Claude into your existing planning processes, not just as a pilot, but as a reliable part of how you manage liquidity. If you want to explore what this could look like for your finance organization, our team can help you move from idea to working solution quickly and safely.

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Real-World Case Studies

From Payments to Streaming Media: Learn how companies successfully use Claude.

Nubank (Pix Payments)

Payments

Nubank, Latin America's largest digital bank serving over 114 million customers across Brazil, Mexico, and Colombia, faced the challenge of scaling its Pix instant payment system amid explosive growth. Traditional Pix transactions required users to navigate the app manually, leading to friction, especially for quick, on-the-go payments. This app navigation bottleneck increased processing time and limited accessibility for users preferring conversational interfaces like WhatsApp, where 80% of Brazilians communicate daily. Additionally, enabling secure, accurate interpretation of diverse inputs—voice commands, natural language text, and images (e.g., handwritten notes or receipts)—posed significant hurdles. Nubank needed to overcome accuracy issues in multimodal understanding, ensure compliance with Brazil's Central Bank regulations, and maintain trust in a high-stakes financial environment while handling millions of daily transactions.

Lösung

Nubank deployed a multimodal generative AI solution powered by OpenAI models, allowing customers to initiate Pix payments through voice messages, text instructions, or image uploads directly in the app or WhatsApp. The AI processes speech-to-text, natural language processing for intent extraction, and optical character recognition (OCR) for images, converting them into executable Pix transfers. Integrated seamlessly with Nubank's backend, the system verifies user identity, extracts key details like amount and recipient, and executes transactions in seconds, bypassing traditional app screens. This AI-first approach enhances convenience, speed, and safety, scaling operations without proportional human intervention.

Ergebnisse

  • 60% reduction in transaction processing time
  • Tested with 2 million users by end of 2024
  • Serves 114 million customers across 3 countries
  • Testing initiated August 2024
  • Processes voice, text, and image inputs for Pix
  • Enabled instant payments via WhatsApp integration
Read case study →

UPS

Logistics

UPS faced massive inefficiencies in delivery routing, with drivers navigating an astronomical number of possible route combinations—far exceeding the nanoseconds since Earth's existence. Traditional manual planning led to longer drive times, higher fuel consumption, and elevated operational costs, exacerbated by dynamic factors like traffic, package volumes, terrain, and customer availability. These issues not only inflated expenses but also contributed to significant CO2 emissions in an industry under pressure to go green. Key challenges included driver resistance to new technology, integration with legacy systems, and ensuring real-time adaptability without disrupting daily operations. Pilot tests revealed adoption hurdles, as drivers accustomed to familiar routes questioned the AI's suggestions, highlighting the human element in tech deployment. Scaling across 55,000 vehicles demanded robust infrastructure and data handling for billions of data points daily.

Lösung

UPS developed ORION (On-Road Integrated Optimization and Navigation), an AI-powered system blending operations research for mathematical optimization with machine learning for predictive analytics on traffic, weather, and delivery patterns. It dynamically recalculates routes in real-time, considering package destinations, vehicle capacity, right/left turn efficiencies, and stop sequences to minimize miles and time. The solution evolved from static planning to dynamic routing upgrades, incorporating agentic AI for autonomous decision-making. Training involved massive datasets from GPS telematics, with continuous ML improvements refining algorithms. Overcoming adoption challenges required driver training programs and gamification incentives, ensuring seamless integration via in-cab displays.

Ergebnisse

  • 100 million miles saved annually
  • $300-400 million cost savings per year
  • 10 million gallons of fuel reduced yearly
  • 100,000 metric tons CO2 emissions cut
  • 2-4 miles shorter routes per driver daily
  • 97% fleet deployment by 2021
Read case study →

DHL

Logistics

DHL, a global logistics giant, faced significant challenges from vehicle breakdowns and suboptimal maintenance schedules. Unpredictable failures in its vast fleet of delivery vehicles led to frequent delivery delays, increased operational costs, and frustrated customers. Traditional reactive maintenance—fixing issues only after they occurred—resulted in excessive downtime, with vehicles sidelined for hours or days, disrupting supply chains worldwide. Inefficiencies were compounded by varying fleet conditions across regions, making scheduled maintenance inefficient and wasteful, often over-maintaining healthy vehicles while under-maintaining others at risk. These issues not only inflated maintenance costs by up to 20% in some segments but also eroded customer trust through unreliable deliveries. With rising e-commerce demands, DHL needed a proactive approach to predict failures before they happened, minimizing disruptions in a highly competitive logistics industry.

Lösung

DHL implemented a predictive maintenance system leveraging IoT sensors installed on vehicles to collect real-time data on engine performance, tire wear, brakes, and more. This data feeds into machine learning models that analyze patterns, predict potential breakdowns, and recommend optimal maintenance timing. The AI solution integrates with DHL's existing fleet management systems, using algorithms like random forests and neural networks for anomaly detection and failure forecasting. Overcoming data silos and integration challenges, DHL partnered with tech providers to deploy edge computing for faster processing. Pilot programs in key hubs expanded globally, shifting from time-based to condition-based maintenance, ensuring resources focus on high-risk assets.

Ergebnisse

  • Vehicle downtime reduced by 15%
  • Maintenance costs lowered by 10%
  • Unplanned breakdowns decreased by 25%
  • On-time delivery rate improved by 12%
  • Fleet availability increased by 20%
  • Overall operational efficiency up 18%
Read case study →

Wells Fargo

Banking

Wells Fargo, serving 70 million customers across 35 countries, faced intense demand for 24/7 customer service in its mobile banking app, where users needed instant support for transactions like transfers and bill payments. Traditional systems struggled with high interaction volumes, long wait times, and the need for rapid responses via voice and text, especially as customer expectations shifted toward seamless digital experiences. Regulatory pressures in banking amplified challenges, requiring strict data privacy to prevent PII exposure while scaling AI without human intervention. Additionally, most large banks were stuck in proof-of-concept stages for generative AI, lacking production-ready solutions that balanced innovation with compliance. Wells Fargo needed a virtual assistant capable of handling complex queries autonomously, providing spending insights, and continuously improving without compromising security or efficiency.

Lösung

Wells Fargo developed Fargo, a generative AI virtual assistant integrated into its banking app, leveraging Google Cloud AI including Dialogflow for conversational flow and PaLM 2/Flash 2.0 LLMs for natural language understanding. This model-agnostic architecture enabled privacy-forward orchestration, routing queries without sending PII to external models. Launched in March 2023 after a 2022 announcement, Fargo supports voice/text interactions for tasks like transfers, bill pay, and spending analysis. Continuous updates added AI-driven insights, agentic capabilities via Google Agentspace, ensuring zero human handoffs and scalability for regulated industries. The approach overcame challenges by focusing on secure, efficient AI deployment.

Ergebnisse

  • 245 million interactions in 2024
  • 20 million interactions by Jan 2024 since March 2023 launch
  • Projected 100 million interactions annually (2024 forecast)
  • Zero human handoffs across all interactions
  • Zero PII exposed to LLMs
  • Average 2.7 interactions per user session
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 →

Best Practices

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

Use Claude to Reconstruct Historical Payment Behavior

Start by giving Claude a clear view of how customers actually pay. Export invoice- and payment-level data from your ERP (e.g. invoice date, due date, payment date, amount, customer, region, product line) into a CSV or Excel file. Then load a filtered subset into Claude (respecting your data policies) and ask it to derive behavioral patterns that matter for cash flow.

For example, you can paste a summary table (e.g. customer, average days to pay, standard deviation, seasonality indicators) and use a prompt like:

Act as a senior FP&A analyst.
You receive historical invoice and payment data aggregated by customer.

1. Identify customer segments with systematically slower or faster payment behavior.
2. Highlight clear seasonality in payment timing (e.g. Q4 delays, summer slow-down).
3. Suggest revised DSO / cash collection assumptions per segment that better reflect reality.
4. Output the result as a table with these columns:
   - Segment name
   - Key pattern observed
   - Recommended average days to pay
   - Confidence level (high/medium/low)
   - Comments for the CFO

Use these recommendations as inputs to your forecasting model, while sanity-checking against your own experience. Over time, iterate by feeding Claude more granular views (by region, product, or contract type) to refine your cash flow drivers.

Let Claude Audit and Reconcile Your Existing Cash Flow Model

Most teams already have a spreadsheet-based cash flow forecast that roughly works, but contains hidden inconsistencies. Claude is effective at auditing these workbooks for logical gaps between inflow assumptions, outflow schedules, and actual patterns from the ERP. Export key tabs (summary, AR schedule, AP schedule, key assumptions) into a single, simplified file before sharing.

Then use a prompt like:

You are an expert in corporate cash flow forecasting.
I will share:
- A current 12-month cash flow forecast (by month)
- Underlying assumptions for DSO, DPO, and growth
- A high-level summary of historical payment behavior

Tasks:
1. Identify inconsistencies between the assumptions and historical behavior.
2. Flag any months where the projected net cash position looks unrealistic
   given the business context (e.g. seasonality in orders, known project milestones).
3. Suggest 3-5 specific adjustments to improve realism while
   keeping the model structure unchanged.
4. Provide a short narrative for the CFO explaining the key changes
   and their impact on cash risk.

Implement the recommended adjustments in your spreadsheet, and log them as a change history with rationale. Repeat this audit step as part of your monthly or quarterly planning cadence to keep the model honest.

Build Rolling Cash Flow Forecasts with Structured Prompts

Instead of rebuilding a static 12-month forecast every year, use Claude to help you run a rolling cash flow forecast that extends your current view by one month at each update. Feed Claude with (1) the current forecast file, (2) the last 3–6 months of actuals, and (3) key business updates (pipeline, churn, major contracts, capex plans).

Structure your prompt so Claude explicitly updates assumptions and documents the new baseline:

Act as a rolling cash flow planning assistant.
We have:
- Current monthly cash flow forecast for the next 9 months
- Actual inflows and outflows for the last 6 months
- Notes on major upcoming events (projects, capex, new contracts)

Tasks:
1. Compare actual vs. forecast for the last 3 months and quantify deviations.
2. Update the core assumptions (e.g. DSO, DPO, growth by segment)
   where the deviations are systematic.
3. Propose an updated 12-month rolling forecast, extending the horizon by 3 months.
4. Summarize key assumption changes and their cash impact in bullet points
   suitable for a CFO briefing.

Then manually transfer the updated assumptions and monthly figures into your master planning file. This keeps Claude tightly integrated with your existing process while avoiding uncontrolled changes to the model structure.

Use Claude for Narrative Scenario Analysis and Stress Tests

Claude excels at turning high-level business questions into quantified cash flow scenarios. Use it to run stress tests around late payments, sales slowdowns, or pricing changes. Provide your base case forecast plus a short description of potential shocks, and ask Claude to adjust key drivers and articulate their consequences.

A practical scenario prompt might look like:

You are advising the CFO on liquidity risk.
Base case: The attached monthly cash flow forecast for the next 12 months.

Define and quantify 3 scenarios:
1) Mild downturn: 10% lower revenue, 5 days longer DSO from month 3.
2) Severe downturn: 25% lower revenue, 15 days longer DSO from month 2,
   and 10% of customers delaying payments by 60+ days.
3) Upside: 15% higher revenue with improved DSO due to new collection initiatives.

For each scenario:
- Provide an adjusted monthly cash flow view (high-level is fine).
- Identify the first month where liquidity becomes critical.
- Suggest 3 concrete actions management could take to mitigate cash risk.
- Write a short narrative (max 200 words) to present to the board.

Use these narratives and high-level numbers as input to your internal planning meetings, then refine the details in your main forecasting model.

Standardize Prompts and Outputs into Repeatable Finance Workflows

To move from experimentation to a robust AI-assisted cash planning process, standardize how your team interacts with Claude. Document a small set of approved prompts for recurring tasks: historical analysis, forecast audit, rolling update, and scenario creation. Store them in a shared playbook or within your finance knowledge base.

For each workflow, define the input file structure (e.g. which tabs and columns must be present), the exact Claude prompt, and the expected output format (tables, narratives, bullet points). Over time, you can work with your IT and data teams—or with a partner like Reruption—to wrap these prompts into simple internal tools or scripts so that finance users just click a button instead of copying and pasting.

Track Accuracy and Process Metrics to Prove Value

Finally, treat Claude as part of your performance management system. Track forecast accuracy (e.g. absolute variance between forecasted and actual cash position per month), lead time to produce an updated cash forecast, and number of meaningful scenario discussions with management. Compare these metrics before and after introducing Claude into your cash flow forecasting.

Realistic outcomes many teams see after a disciplined rollout include: 20–40% faster forecast updates, a visible reduction in surprise liquidity gaps, and a structured set of 3–5 standard scenarios used in every board cycle. The exact numbers will depend on your starting point, but the combination of data-driven assumptions and narrative clarity is what consistently shifts finance from reactive explanations to proactive cash management.

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Frequently Asked Questions

Claude improves cash flow forecast accuracy by connecting your existing models with real payment behavior and contract details. It can analyze ERP exports of invoices and payments, identify segments with systematically late or early payments, and suggest more realistic DSO assumptions per segment. It also reconciles your current forecast with recent actuals, flags inconsistent assumptions, and generates clear narratives explaining where and why cash is likely to deviate from plan.

Instead of relying on one top-down DSO figure for the entire business, finance teams can use Claude to maintain a behavior-based view of collections, seasonality, and contract-specific terms, and embed these drivers back into their planning spreadsheets.

At a minimum, you need access to exports from your ERP or accounting system covering invoices, payments, and basic master data (customer, region, product group). It also helps to have your current cash flow planning file available in a reasonably structured format (tabs for AR, AP, assumptions, and summary).

On the skills side, you do not need data scientists to get started, but you do need finance professionals who understand your current forecasting logic and can sanity-check Claude’s suggestions. Basic "prompt engineering" skills—knowing how to ask structured questions and specify desired output formats—are sufficient. Over time, many organizations add light engineering support to automate data extraction and standardize prompts.

Most finance teams can see tangible benefits within a few weeks if they focus on a specific use case, such as auditing the next quarterly cash flow forecast or building three board-ready scenarios. The initial setup—aligning on data extracts, defining 2–3 core prompts, and running the first analyses—typically fits within a single planning cycle.

Within 1–3 months, you can embed Claude into a regular rolling cash flow forecasting cadence, where each update includes an AI-assisted comparison of forecast vs. actual and a refreshed set of assumptions. Deeper automation (e.g. integrated pipelines, standardized workflows, internal tools) may take longer, but you do not need full automation to get meaningful early wins.

The direct tooling cost for using Claude is usually modest compared to the financial impact of improved cash decisions. The main ROI drivers are: fewer surprise liquidity gaps (and therefore less need for expensive short-term financing), better utilization of excess cash, and reduced manual effort in preparing and reconciling forecasts. Many teams also report qualitative benefits: higher confidence from leadership and more constructive scenario discussions.

To quantify ROI, track metrics such as reduction in forecast error, avoided overdraft or emergency financing costs, and time saved per planning cycle. These benefits typically outweigh the cost of Claude usage and the incremental time spent setting up AI-enabled cash forecasting workflows, especially for organizations with significant working capital tied up in receivables.

Reruption helps finance teams move from idea to working solution quickly. With our AI PoC offering (9.900€), we validate whether Claude can reliably support your specific cash flow use case: we define the use case and metrics with you, test different prompt and data setups, build a functioning prototype that works with your actual ERP exports and planning files, and evaluate performance on speed, quality, and cost per run.

Beyond the PoC, our Co-Preneur approach means we embed with your team to integrate Claude into your planning calendar, design secure data flows, and standardize prompts into repeatable workflows. We focus on real implementation inside your finance organization—working with your spreadsheets, tools, and constraints—so that AI becomes a dependable part of how you plan and manage liquidity, not just a one-off experiment.

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