Machine Learning: how it works and real-world use cases in professional services

How machine learning works — step by step

Regardless of the specific algorithm, every machine learning project follows the same general lifecycle:

1. Define the problem — What decision or prediction are you trying to improve? A clear problem statement ("predict which invoices will be paid late") is essential before touching any data.
2. Collect and prepare data — Gather relevant historical data. Clean it: handle missing values, remove duplicates, normalise formats. This step typically consumes 60–80% of the project effort.
3. Choose features — Select (or engineer) the input variables the model will learn from. For invoice prediction, features might include client industry, invoice amount, payment history, and day of week.
4. Select and train the model — Pick an appropriate algorithm, split the data into training and test sets, and train the model. The algorithm adjusts its internal parameters to minimise prediction error on the training data.
5. Evaluate — Test the model on data it hasn't seen. Metrics like accuracy, precision, recall, and mean absolute error tell you whether the model is good enough for production.
6. Deploy and monitor — Put the model into production (often behind an API). Monitor its performance over time. Retrain when the data distribution changes ("model drift").

Supervised learning in more detail

Supervised learning is the most widely used form of ML and the easiest to understand. You give the algorithm a dataset where each example has both inputs (features) and a correct output (label). The algorithm learns a function that maps inputs to outputs, and then applies that function to new, unseen data.

If the output is a category (e.g. "fraud" vs "not fraud"), the task is called classification. If the output is a number (e.g. "expected revenue next quarter"), it's called regression. Common supervised algorithms include linear regression, logistic regression, decision trees, random forests, gradient-boosted trees, and neural networks.

Unsupervised learning — finding hidden patterns

When you don't have labels — just raw data — unsupervised learning can discover structure. Clustering algorithms group similar items together (e.g. segmenting clients by behaviour). Dimensionality reduction techniques compress high-dimensional data into fewer dimensions for visualisation or as a preprocessing step. Anomaly detection identifies data points that don't fit established patterns — crucial for fraud detection and quality assurance.

Reinforcement learning — learning by doing

Reinforcement learning (RL) is different: an agent takes actions in an environment and receives rewards or penalties. Over many iterations, it learns a policy — a strategy for choosing actions that maximises long-term reward. RL is behind game-playing AI (AlphaGo, OpenAI Five), robotics control, and, increasingly, the fine-tuning of large language models (RLHF).

Use cases in professional services

Machine learning is not confined to tech companies. Professional services firms are increasingly using ML to improve quality, speed, and client outcomes. Here are concrete examples across sectors:

Getting started with machine learning

You don't need a data science team of fifty to benefit from ML. A practical starting point:

• Identify a specific, measurable problem — "Reduce invoice processing time by 50%" is better than "use AI".
• Audit your data — ML is only as good as its training data. Check what you have, what's missing, and what's messy.
• Start with a pilot — Pick one workflow, build a simple model, measure the impact, and iterate. Don't try to transform everything at once.
• Invest in data infrastructure — Clean data pipelines and reliable storage are the foundation that makes all future ML work easier and faster.
• Keep humans in the loop — ML augments professional judgment; it doesn't replace it. Design systems where humans review, correct, and improve model outputs.