Multimodal Document Forensics

Problem: Legacy OCR systems fail to detect high-fidelity digital manipulations and “Frankenstein” IDs where portraits are seamlessly blended into legitimate document structures.

Solution: We deploy Convolutional Neural Networks (CNNs) for Error Level Analysis (ELA) and texture frequency mapping to identify pixel-level inconsistencies indicative of tampering.

Data Sources: Government-issued ID databases, holographic pattern libraries, and EXIF metadata repositories.

Integration: Direct hook into mobile onboarding SDKs via gRPC for sub-second latency.

Neural Entity Resolution

Problem: Sophisticated money laundering networks hide behind nested Shell Companies and UBO (Ultimate Beneficial Owner) structures that span multiple jurisdictions and naming conventions.

Solution: Graph Neural Networks (GNNs) mapped over a Neo4j knowledge graph identify non-obvious relationships between seemingly disconnected entities based on shared addresses, IP signatures, and transaction clusters.

Data Sources: Pan-European and Offshore corporate registries, SWIFT transaction logs, and internal CRM data.

Integration: Snowflake Data Cloud for large-scale ETL and vector embedding storage.

Cognitive Adverse Media

Problem: Manual screening for adverse media is inefficient, prone to “false name” matches, and often misses regional news published in non-English languages.

Solution: An LLM-driven pipeline using Named Entity Recognition (NER) and Sentiment Analysis across 50+ languages to contextualise mentions of PEPs (Politically Exposed Persons) and sanctioned individuals.

Data Sources: 100k+ global news sources, social media feeds, and legal databases (e.g., LexisNexis).

Integration: Real-time alerts via Webhooks into existing Case Management Systems (CMS).

Passive Liveness Detection

Problem: Generative AI has made “Deepfake-as-a-Service” a primary threat to video KYC (vKYC), allowing attackers to bypass traditional “blink or nod” challenges.

Solution: We implement passive 3D liveness detection using depth-sensing and micro-expression analysis that detects skin reflectance and pulse-based facial color changes (rPPG).

Data Sources: Real-time high-definition video stream from end-user devices.

Integration: Native iOS/Android SDK integration with cloud-based inference fallback.

Behavioral Profiling

Problem: Identity theft often involves a “clean” account setup that is later taken over by a malicious bot or human operator.

Solution: Sabalynx integrates continuous behavioral monitoring that analyzes keystroke dynamics, mouse movement entropy, and device orientation during the KYC application process.

Data Sources: DOM-level interaction telemetry and accelerometer data.

Integration: Lightweight JavaScript agent for web and mobile telemetry hooks.

Federated Risk Intelligence

Problem: Banks cannot share raw customer data due to GDPR/SOC2, yet criminals exploit this siloed approach to move funds between institutions.

Solution: We deploy Federated Learning models where risk signals are trained locally at each bank, and only the encrypted model weights are aggregated to update a global “suspicion score.”

Integration: Secure Enclave (TEE) environments on Azure/AWS for model aggregation.

Event-Driven Perpetual KYC

Problem: Traditional KYC refresh cycles (e.g., every 3 years) are too slow to react to real-time risk changes, leaving banks exposed to regulatory fines.

Solution: An event-driven architecture that triggers an automated KYC re-assessment only when a “Risk Event” occurs—such as a change in the company board or a cross-border transaction spike.

Data Sources: Real-time ERP feeds and external corporate event registries.

Synthetic Identity Scoring

Problem: Synthetic identities—combining real SSNs with fake names—are the fastest-growing form of financial crime, invisible to basic credit checks.

Solution: Generative Adversarial Network (GAN) based anomaly detection that flags identity profiles that lack “digital history depth” or exhibit robotic consistency in their data structure.

Integration: Integrated into the underwriting engine as a pre-screening risk layer.