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# 4.2 High-Level Architecture Overview

The data layer is responsible for securely interfacing with financial data providers and user-authorized sources. This may include bank and card transaction feeds, billing and subscription records, and where applicable, on-chain or wallet-related signals. Data is ingested via secure APIs or connectors, passed through validation and normalization routines, and then written into storage designed for analytical workloads rather than long-term raw logging. The guiding principle is to retain only what is necessary to generate insights, and to avoid building a general-purpose data warehouse of user history.

Above this sits the processing and analytics layer. Here, normalized data is transformed into behavioral features and credit-relevant signals. This layer hosts the AI models, rule engines, and aggregation logic that interpret patterns over time. It is designed as a set of services rather than a monolith, so that individual components—such as anomaly detection, risk trend estimation, or cash flow profiling—can be upgraded, replaced, or extended independently.

The application layer exposes these capabilities to users through two main surfaces: the web dashboard and the Telegram-based Credit Express Bot. Both interact with a common API layer that mediates access to analytics results, user configurations, and CRX-gated features. The web application focuses on depth and visual context, while the bot focuses on immediacy and conversational access. Crucially, neither client interacts directly with raw financial data; they consume processed, permission-filtered outputs from the analytics layer.

The security and governance layer cuts across the entire architecture. It enforces encryption at rest and in transit, access control policies, key management, rate limits, and auditability. This layer also governs how anonymized or aggregated data may be used to improve models, ensuring that any such use remains consistent with user expectations and applicable regulation.


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