Explainable GenAI with Neural Networks for LDDR-Based Threat and Credit Risk Modeling on a Real-Time Apache–SAP HANA Framework

Abstract

This paper presents a novel framework that integrates explainable generative AI (GenAI) with neural-network-based predictive models to perform real-time threat and credit risk modeling using a Log-Data-Driven Representation (LDDR) approach, deployed on an Apache–SAP HANA in-memory streaming platform. Modern financial and cybersecurity risk problems demand systems that can process extremely high-throughput event streams (transactions, login attempts, telemetry) while providing transparent, auditable explanations suitable for regulatory and operational use. We propose using LDDR — a flexible representation that encodes heterogeneous log streams into structured, semantically rich vectors — as the input interface between raw operational data and downstream neural architectures (temporal convolutional networks, attention-augmented recurrent units, and lightweight transformer encoders). In addition to predictive accuracy, our objective is to deliver actionable explanations using a layered explainability stack that combines local attribution (integrated gradients, SHAP-style approximations), prototype and counterfactual generation via conditional generative modules, and global concept discovery using bottleneck concept probes. These explainability mechanisms are tightly integrated with the real-time processing and persistence capabilities of Apache–SAP HANA so that interpretability artifacts (feature attributions, counterfactual examples, prototype clusters) are available for immediate query and audit by investigators and compliance systems.

We design the architecture to support dual tasks simultaneously: (1) threat detection — identifying anomalous or malicious patterns in streaming operational logs (e.g., coordinated credential misuse, lateral movement indicators, rapid access-pattern anomalies) — and (2) credit risk scoring — assessing transaction- and behavior-based creditworthiness signals in near-real-time for pre-approval, overdraft controls, or dynamic line adjustments. Both tasks share the LDDR inputs but differ in label construction and model head design; multi-task learning with shared encoders enables information transfer while preserving task-specific constraints. To preserve latency goals (<100ms median inference under target load), we use model distillation and pruning to produce compact inference engines and leverage SAP HANA’s in-memory stored-procedure APIs (user-defined functions) to run vectorized inference pipelines tightly coupled to stream ingestion. Model updates are orchestrated through a Canary + Shadow deployment pattern combined with online learning routines that detect and adapt to concept drift while retaining provenance metadata.

 Explainability is implemented on three complementary axes: (a) local — fast attribution delivered with each decision for operator triage, (b) generative — contextually plausible counterfactuals and prototypes from conditional generative models to explain “how to change” a decision, and (c) global — summarizing model behavior across cohorts to reveal blind spots or spurious correlations. We evaluate the framework on a hybrid dataset synthesizing anonymized financial transaction logs and simulated attacker telemetry, and on an extended public dataset suite (modified to reflect LDDR-style features). Results show competitive or superior detection/credit-scoring AUC compared to baseline gradient-boosting and simpler RNN baselines while producing explanation artifacts that significantly improve human analyst triage speed and satisfaction in user studies. Latency benchmarks indicate that the end-to-end pipeline, including attribution generation, operates within operational thresholds for most use cases; resource tradeoffs are quantified to guide deployment choices.

 We conclude by discussing regulatory and ethical implications, mitigation of explanation-path manipulation (adversarial attempts to game counterfactuals), and a roadmap for extending the framework to multi-institution federated learning settings where privacy and provenance constraints dominate. The LDDR + explainable GenAI pattern offers a practical path to combine high-throughput real-time analytics with human-usable interpretability in financial and threat domains, balancing accuracy, speed, and auditability.