Harender Bisht, PhD, is a Technical Architect and Lead Developer with more than 10 years of experience designing and delivering enterprise technology solutions across insurance, reinsurance, finance, regulatory reporting, data engineering, and applied artificial intelligence. Based in New Jersey, he currently serves as a Lead Developer and Technical Architect at Crum & Forster, where he leads strategic initiatives involving IFRS 17, reinsurance modernization, financial automation, enterprise data architecture, intelligent reporting, and AI-enabled decision-support systems.

His work includes designing a company-wide IFRS 17 data and calculation framework that integrates claims, premiums, reinsurance, actuarial, and financial information with platforms such as Moody’s Analytics and OneStream. He has architected enterprise integrations for Sapiens Reinsurance, supported the modernization and retirement of legacy systems, and developed a hybrid cloud and on-premises job orchestration platform for centrally managing, monitoring, scheduling, and auditing enterprise batch operations.

Harender’s applied AI work focuses on building governed, secure, and explainable AI capabilities for enterprise environments. He has contributed to intelligent financial reporting solutions that combine retrieval-augmented generation, large language models, natural-language querying, semantic search, metadata-driven retrieval, and structured enterprise data pipelines. These solutions are designed to help business users explore financial and operational information, identify trends and variances, compare reporting periods, and generate evidence-based insights while maintaining traceability to authoritative data sources.

He has also designed microservices for OFAC sanctions screening using Azure Cognitive Search, fuzzy matching, weighted entity attributes, configurable decision thresholds, and auditable screening workflows. His broader AI architecture experience includes agentic workflows, intent classification, dynamic query planning, vector and keyword retrieval, prompt orchestration, model evaluation, human feedback, confidence assessment, observability, and responsible AI guardrails. He emphasizes the importance of grounding AI-generated responses in validated enterprise data, preserving lineage, controlling access, monitoring performance, and reducing hallucinations in high-stakes financial and regulatory applications.

His technical expertise includes Azure cloud architecture, enterprise AI architecture, microservices, .NET, C#, Python, SQL, data engineering, Snowflake, OneStream, Azure Cognitive Search, retrieval-augmented generation, large language models, LangChain, LangGraph, LangSmith, semantic search, and enterprise integration patterns. He works closely with finance, actuarial, compliance, operations, data, and technology leaders to ensure that technical and AI architectures align with business objectives, regulatory obligations, security standards, governance policies, and operational requirements.

Harender earned his PhD in Information Technology from the University of the Cumberlands, where his research examined the ethical implications of explainable artificial intelligence in insurance fraud detection. His research focused on how fairness, transparency, accountability, trust, interpretability, and auditability influence the adoption and use of AI-supported decisions in high-stakes insurance environments. His work bridges technical AI development with responsible AI governance and the practical needs of professionals who rely on algorithmic recommendations.

He also holds a master’s degree in Computer Applications from the University of Bridgeport and a bachelor’s degree in Computer Science from Amity University. His professional and academic work reflects a continued focus on developing scalable, trustworthy, and business-aligned AI systems that strengthen enterprise decision-making without compromising governance, transparency, or human oversight.