Construction Materials Cash Flow

The AI Cash Forecast

Transforming working capital management with predictive machine learning

Client Construction Materials SME
Industry Construction & Materials
Services Financial Performance, AI & Predictive Analytics
Result 14 Days DSO Reduction
The Challenge

Cash Flow Blindness in a Seasonal Business

A fast-growing construction materials distributor with £18M turnover was struggling with chronic cash flow volatility. Despite strong revenue growth, the business faced regular liquidity crunches that strained supplier relationships and limited growth opportunities.

The core problems were:

  • No forward visibility: Cash forecasting was manual, spreadsheet-based, and consistently inaccurate. The finance team couldn't predict when customers would actually pay.
  • Seasonal complexity: Construction activity varied dramatically by season and weather, making historical averages unreliable for forecasting.
  • Customer payment behaviour: Days Sales Outstanding (DSO) averaged 68 days—far above industry benchmarks—but varied wildly by customer, project type, and contract terms.
  • Reactive credit management: Without predictive insights, the team couldn't proactively manage late payers or adjust credit terms before problems escalated.
  • Working capital inefficiency: Excess cash was left idle whilst the business simultaneously paid premium rates on its overdraft facility.

The business needed intelligent, automated cash forecasting that could predict customer payment patterns and enable proactive working capital management.

The Solution

AI-Powered Cash Flow Intelligence

We deployed our proprietary FinSight AI platform to build predictive cash flow models and transform working capital management:

Phase 1: Data Foundation & Analysis (Months 1-2)

  • Extracted and cleaned 3 years of historical invoice, payment, and customer data from disparate systems
  • Identified 47 variables influencing payment timing: customer type, invoice size, payment terms, seasonality, project type, and payment history
  • Discovered that 22% of customers consistently paid 30+ days late, accounting for 60% of DSO problems
  • Built segmentation model to classify customers by payment reliability

Phase 2: Machine Learning Model Development (Months 2-3)

  • Developed ensemble machine learning models (Random Forest, XGBoost, Neural Networks) to predict invoice-level payment dates
  • Achieved 85% accuracy predicting payment timing within 5-day window
  • Built customer-level risk scores to flag high-risk accounts before credit was extended
  • Created automated early-warning system for invoices predicted to be paid late
  • Result: Transformed cash forecasting from guesswork to data-driven precision

Phase 3: FinSight AI Dashboard Deployment (Month 4)

  • Deployed real-time cash flow dashboard showing predicted vs. actual receipts
  • Automated 13-week rolling cash forecast updated daily based on live invoice and payment data
  • Integrated credit management workflows: automatic reminders, escalation protocols, and payment plan triggers
  • Built scenario planning tools to model impact of credit policy changes

Phase 4: Process Optimisation & Change Management (Months 4-6)

  • Redesigned credit approval process using predictive risk scores
  • Implemented proactive collections: targeted outreach to high-risk invoices before due dates
  • Trained finance and sales teams on interpreting AI insights and taking action
  • Established weekly working capital review meetings using dashboard data
The Results

Transformed Working Capital Performance

14 Days
DSO Reduction

Days Sales Outstanding decreased from 68 to 54 days within 6 months

£720k
Working Capital Released

Cash freed up through faster collections and improved payment visibility

85%
Forecast Accuracy

AI models predict payment dates within 5-day window with 85% accuracy

40 Hours
Time Saved Monthly

Finance team freed from manual forecasting and reporting tasks

The business eliminated liquidity crunches, reduced reliance on expensive overdraft facilities, and gained the confidence to invest released working capital in strategic growth initiatives.

Additional Benefits

  • Reduced bad debt: Early warning system identified deteriorating accounts, reducing write-offs by 35%
  • Better supplier relationships: Improved cash predictability enabled the business to negotiate early payment discounts
  • Strategic decision-making: Leadership could model the cash impact of new contracts and pricing strategies before committing
  • Scalable platform: FinSight AI adapted as the business grew, maintaining accuracy as customer base expanded

"Before Greenwich Strategy, our cash forecasting was little more than educated guesswork. The FinSight AI platform gave us predictive visibility we didn't think was possible for a business our size. We now manage working capital proactively rather than reactively—and that's made all the difference to our growth trajectory."

— Finance Director, Construction Materials SME
Technologies Used

Tools & Methodologies

Machine Learning & AI

  • FinSight AI (Greenwich proprietary platform)
  • Python (scikit-learn, XGBoost, TensorFlow)
  • Random Forest & Gradient Boosting models
  • Neural networks for payment prediction

Data Engineering

  • SQL Server for data warehousing
  • ETL pipelines for invoice and payment data
  • API integrations with accounting systems
  • Real-time data synchronisation

Analytics & Visualisation

  • Power BI interactive dashboards
  • Real-time cash position tracking
  • 13-week rolling forecast automation
  • Customer risk scoring and segmentation

Financial Management

  • Working capital optimisation framework
  • Credit risk assessment models
  • Automated collections workflows
  • Scenario planning and sensitivity analysis

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