Apparatus and method for model agnostic fraud risk assessment

3A The apparatus of claim 1, wherein audit-record digests are stored in tamper-evident storage selected from append-only event stores, hash-chained logs, Merkle-tree-verified logs, write-once-read-many media, distributed hash tables, blockchains, or functionally equivalent approaches.

3B The method of claim 2, further comprising storing audit-record digests as recited in claim 3A.

4A The apparatus of claim 1, wherein an explainability module computes per-feature contribution indicators using one or more explainability techniques, such as feature-importance attribution, perturbation analysis, or model-agnostic interpretability methods, or functionally equivalent approaches, and stores the indicators with the audit records.

4B The apparatus of claim 1, wherein the explainability module employs natural-language generation or summarization techniques to produce machine-readable narrative explanations, which are stored with the audit records.

4C The method of claim 2, further comprising computing per-feature contribution indicators using one or more explainability techniques such as feature-importance attribution, perturbation analysis, or model-agnostic interpretability methods, or functionally equivalent approaches, and storing the indicators with the audit records.

4D The method of claim 2, further comprising generating machine-readable narrative explanations using natural-language generation or summarization techniques and storing the explanations with the audit records.

5A The apparatus of claim 1, wherein the scoring modules employ one or more of rules engines, statistical methods, decision trees, boosting methods, neural and non-neural networks, transformer or graph-based models, or ensemble techniques including weighted averaging, stacking, or voting.

5B The method of claim 2, wherein the scoring modules employ one or more of the techniques recited in claim 5A.

6A The apparatus of claim 1, wherein a velocity-control module monitors real-time or batched transactional streams and applies configurable thresholds by transaction type, origin, or risk score to trigger alerts or actions.

6B The method of claim 2, further comprising monitoring real-time or batched transactional streams and applying configurable thresholds by transaction type, origin, or risk score to trigger alerts or actions.

7A The apparatus of claim 1, wherein a retraining pipeline automatically updates model parameters based at least in part on adjudicated outcomes, feedback signals, or audit records, and deploys updated model versions to the scoring modules.

7B The method of claim 2, further comprising automatically updating model parameters based at least in part on adjudicated outcomes, feedback signals, or audit records, and deploying updated model versions to the scoring modules.

8A The apparatus of claim 1, wherein an override endpoint enforces role-based access policies and records feedback artifacts in the audit subsystem for use in the retraining pipeline.

8B The method of claim 2, further comprising enforcing role-based access policies through an override endpoint and recording feedback artifacts in the audit subsystem for use in retraining.

9A The apparatus of claim 1, wherein the input interface and scoring modules are signal-agnostic to input formats and sources.

9B The method of claim 2, wherein the input interface and scoring modules are signal-agnostic to input formats and sources.