ArthaShield Technology Architecture

Scalable Machine Learning & Pattern Detection

Apache Mahout

Apache Mahout provides distributed machine learning capabilities optimized for large-scale data environments such as Hadoop. In ArthaShield, Mahout supports clustering and classification of complaints and cases across millions of records.

By operating on distributed and aggregated datasets, Mahout enables pattern detection without unnecessary exposure of raw personal data, supporting privacy-preserving analytics aligned with regulatory guidance.

Natural Language Processing for BFSI Complaints

Apache OpenNLP (Primary Classification Engine)

Apache OpenNLP is the core NLP engine used for complaint and grievance classification in ArthaShield. As a Java-based Apache project, it integrates naturally with enterprise BFSI technology stacks.

• Tokenization and sentence segmentation
• Part-of-speech tagging
• Named Entity Recognition (products, channels, issues)
• Chunking for structured extraction

OpenNLP enables deterministic and explainable NLP pipelines, a key requirement for regulated environments where model decisions must be auditable and interpretable.

Spark NLP (Advanced & Large-Scale NLP)

Spark NLP, built natively on Apache Spark and licensed under Apache 2.0, is used for high-volume and advanced NLP workloads. It supports modern transformer-based models such as BERT and T5.

In ArthaShield, Spark NLP enables semantic similarity detection, complaint deduplication, and contextual analysis across large datasets.

Apache Lucene (Text Analysis & Retrieval)

Apache Lucene provides foundational text analysis, indexing, and retrieval capabilities. It powers fast search, relevance scoring, and historical case lookup across complaints and regulatory records.

Data Minimization, Storage & Auditability

Apache Parquet

Apache Parquet is the primary analytical storage format used in ArthaShield. Its columnar design, compression, and encoding techniques significantly reduce storage footprint while improving analytical performance.

• Column-level storage for selective data access
• Compression using Snappy and Gzip
• Efficient encoding (Dictionary, RLE)

This design enforces data minimization by ensuring only necessary fields are stored and processed, supporting FD, GDPR, and DPDP compliance.

Apache Ranger

Apache Ranger provides centralized access control, authorization policies, and immutable audit logs. ArthaShield uses Ranger to ensure that complaint and case data access is role-based, monitored, and regulator-ready.

Streaming Architecture & Privacy-by-Design

Apache Pulsar

Apache Pulsar enables real-time ingestion and processing of complaints and case updates. Its configurable retention policies allow sensitive data to remain short-lived where required.

Using Pulsar Functions, personal data can be anonymized or redacted before long-term storage, ensuring compliance with privacy and supervisory requirements.

ArthaShield Technology Stack

Apache Mahout

Apache OpenNLP

Spark NLP

Apache Lucene

Apache Parquet

Apache Ranger

Apache Pulsar

Why Apache-First Architecture

ArthaShield prioritizes Apache open-source technologies over non-Apache alternatives such as NLTK or spaCy to ensure long-term sustainability, vendor neutrality, and regulatory acceptance.

• Apache 2.0 licensing for enterprise and government adoption
• Proven scalability in large BFSI environments
• Strong governance, auditability, and ecosystem maturity
• Alignment with regulator expectations for transparency