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CAPABILITY 09

AI & Machine Learning

Use predictive models to support decisions in finance, operations and customer teams.

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BUSINESS CONTEXT AND APPROACH

Use predictive models to support decisions in finance, operations and customer teams.

A useful model improves a defined decision. We agree the prediction target, available data and business baseline before training, then test whether the model is accurate enough and practical enough for the people who will use it.

Forecasts and risk scores can deteriorate when behaviour changes. Our delivery covers data preparation, validation, explainability and handover, with thresholds for when a person should investigate or override an output.

OUTCOME AND MEASURES

A model assessed against business baselines, with documented limits and ownership.

Before delivery starts we agree baselines for:

  • Forecast or classification error against a baseline
  • Coverage and stability across relevant segments
  • Decision adoption and operational exceptions
AI & MACHINE LEARNING DELIVERY ARCHITECTURE

Frame, prepare, validate, integrate and monitor.

A reference delivery architecture for predictive models that are framed around a business decision, validated against a baseline and monitored after handover.

ANIMATED SCENARIO

Turn a forecast into a planning decision

The scenario follows a forecast from data preparation and backtesting to a planning decision with a documented override threshold.

REPRESENTATIVE USE CASES

Use cases shaped
around real work.

Representative patterns are tailored to your landscape, controls, data readiness and business priorities.

01

Demand forecasting

Use sales history and operational drivers to support planning, with backtesting by product and period.

02

Customer retention

Identify customers for review and explain the signals behind a propensity or churn score.

03

Transaction anomaly review

Prioritise unusual transactions for investigation without treating a model score as a final fraud decision.

WHAT WE DELIVER

Artifacts your teams can use.

  • Demand, sales and cash-flow forecasting
  • Customer churn, propensity and segmentation models
  • Risk scoring, fraud signals and anomaly detection
  • Document extraction, data preparation and model validation
AGREED BASELINES
01Forecast or classification error against a baseline
02Coverage and stability across relevant segments
03Decision adoption and operational exceptions
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