How deeply does Financial AI in Practice cover semi-supervised learning?

Defines the approach and outlines self-training and co-training procedures, without specifying an implementation or evaluation protocol.

Semi-supervised learning

As explained in Financial AI in Practice

Semi-supervised learning uses a limited amount of labeled data together with a larger pool of unlabeled data.

Fraud detection often has few labeled fraud cases but many unlabeled transactions. Semi-supervised learning uses both: a small labeled set guides learning from a much larger, mostly legitimate unlabeled pool. This can reduce reliance on expensive manual labeling and expose the model to more data patterns. section 7.4.3

Two common approaches differ in how they use unlabeled transactions:

Self-training: train on labeled data, predict labels for unlabeled transactions, add high-confidence predictions to the labeled set, then retrain. section 7.4.3
Co-training: train multiple models, often on different feature subsets, and use their confident or agreeing predictions to label unlabeled instances. section 7.4.3

How Financial AI in Practice covers it

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Also explained in

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Knowledge Graphs and LLMs in ActionSemi-supervised learning is a machine-learning paradigm that learns from a combination of labeled and unlabeled examples.

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Related concepts

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