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# 5.2 Data Representation and Feature Engineering

A central design task for the AI engine is defining how financial behavior is represented. Rather than operating directly on raw transaction lists, the system generates a rich set of features that capture periodicity, intensity, volatility, and composition. For example, the engine constructs rolling measures of income and outflow, utilization ratios for credit lines, repayment punctuality statistics, concentration measures of spending categories, and indicators of dependency on particular creditors or revenue sources.

Time plays a central role. Features are computed over multiple windows—short, medium, and long horizon—so that the engine can distinguish between short-term anomalies and sustained trends. Where possible, recurring patterns such as salary cycles or recurring bills are identified explicitly, which allows the engine to reason about deviations from expected behavior rather than treating every variance as equally significant.

These engineered features become the vocabulary on which the models operate. By constraining models to work on interpretable, well-defined signals rather than arbitrary embeddings alone, Credit Express preserves the ability to explain and audit its own outputs.


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