As India’s NBFC ecosystem moves rapidly towards AI-led lending, data-driven risk management, and platform-based growth, technology is becoming central to scale, resilience, and trust. Cloud-native systems, intelligent automation, and ecosystem partnerships are reshaping how NBFCs operate and innovate responsibly. Dharmvir Singh, Chief Technology Officer at Samman Capital, shares his views on AI-powered transformation, governance, partnerships, and building future-ready technology foundations for the evolving NBFC landscape, in an exclusive interaction with Vishwas Sinha of Elets News Network (ENN). Edited excerpts:
How do you see the technology landscape of India’s NBFC sector evolving as institutions scale digital lending, analytics-driven decisions, and customer-centric platforms?
India’s NBFC technology landscape is evolving from basic digitization to AI-enabled, end-to-end credit platforms. As digital lending scales, NBFCs will modernize core loan and collections systems with modular, API-led architectures, enabling faster product launches and embedded finance partnerships. A complete shift in customer onboarding is underway, from document-heavy, branch-assisted flows to instant, consent-based, mobile-first journeys using video KYC, Account Aggregators, alternative data, and real-time decisioning. AI will sit at the center of this transformation: powering cashflow underwriting, risk-based pricing, fraud detection, early-warning systems, and hyper-personalized offers, while improving operational efficiency through AI-driven collections, customer support, and document intelligence. As analytics-driven decisions deepen, institutions will invest in model governance, explainability, and compliance to meet rising regulatory expectations. Customer-centric platforms—vernacular UX, omnichannel engagement, transparency, and proactive servicing—will be key differentiators. Cybersecurity, privacy-by-design, and resilient cloud-native stacks will increasingly shift from hygiene factors to sources of competitive advantage.
What are the key technology priorities driving operational efficiency, risk governance, and long-term business resilience?
Key technology priorities today increasingly revolve around AI/ML-led transformation to drive efficiency, strengthen risk governance, and ensure long-term resilience. Cloud-native, modular core platforms enable automation across origination, servicing, and collections, while providing the flexibility needed to embed AI at scale. AI/ML models power straight-through processing, intelligent credit decisioning, dynamic pricing, predictive collections, and AI-assisted customer support—significantly lowering cost-to-serve and improving turnaround times. A strong data and ML foundation – real-time data pipelines, feature stores, model monitoring, and enterprise analytics—supports faster insights and regulatory reporting. From a governance lens, explainable AI, model lifecycle management, bias detection, and auditability are critical as regulators scrutinise automated decisions. Cybersecurity, privacy-by-design, and AI-aware fraud detection safeguard digital ecosystems. Finally, resilient, observable architectures with automated recovery and stress testing ensure business continuity, trust, and scalability amid rapid growth and market volatility.
Partnerships are becoming central to BFSI innovation. How do technology collaborations and ecosystem alliances help accelerate growth and capability building?
Partnerships are becoming a strategic accelerator for BFSI innovation, especially in the era of AI/ML-led transformation. Technology collaborations with fintechs, SaaS providers, and data platforms help institutions rapidly adopt AI/ML capabilities such as alternative-data underwriting, real-time fraud detection, predictive collections, personalisation engines, and generative-AI–driven customer service—without long build cycles or heavy upfront investment. Ecosystem alliances with digital platforms and enterprises enable embedded finance, while shared data and ML insights improve targeting, risk selection, and lifetime value. Open APIs and modular stacks allow BFSI players to experiment, train models, and scale AI use cases faster, shortening time-to-market. Partnerships also bring access to specialised AI talent, mature MLOps frameworks, and continuously learning models. Crucially, regulated institutions benefit from partners that offer explainable, compliant, and well-governed AI, strengthening trust. Ultimately, BFSI leaders will win by orchestrating AI-powered ecosystems – combining institutional trust with partner-led intelligence to drive growth and superior customer experiences.
Additionally, partnerships help institutions future-proof themselves by sharing innovation risk and investment, co-creating industry standards, and staying aligned with rapidly evolving AI regulations. As models improve through federated learning and shared intelligence, BFSI players can continuously enhance decision accuracy, operational resilience, and customer trust without rebuilding their technology stack from scratch.
Without naming specifics, could you share how recent or ongoing partnerships are enabling better data intelligence, automation, or customer experience?
Recent and ongoing partnerships across the BFSI ecosystem are enabling technology-led advances in data intelligence, automation, and experience orchestration without large internal engineering investments. Collaborations with external data platforms, feature stores, and analytics engines are augmenting credit decisioning with rich behavioral and alternative data, strengthening ML-driven risk models and reducing reliance on legacy scorecards. Automation-focused partnerships are enabling API-first, event-driven workflow orchestration across digital onboarding, identity verification, underwriting, servicing, and collections – delivering straight-through processing, faster cycle times, and lower operational leakage. On the experience layer, integrations with conversational AI, decision engines, and personalisation platforms are driving real-time, context-aware engagement and frictionless self-service across channels. In parallel, shared MLOps, model governance, and explainability frameworks are helping institutions deploy regulatory-compliant, auditable AI at scale. Secure, consent-based data exchange and identity frameworks further strengthen trust, resilience, and scalability while accelerating innovation and customer outcomes.
With rising cyber threats and regulatory scrutiny, how are CTOs re-architecting systems to ensure security, compliance, and trust by design?
With escalating cyber threats and regulatory scrutiny, we are re-architecting BFSI systems around AI/ML-driven security, compliance, and trust-by-design. Legacy perimeter models are giving way to zero-trust architectures augmented by ML-based identity risk scoring, continuous authentication, and adaptive access controls across users, devices, and APIs. Cloud-native stacks now embed encryption-by-default, confidential computing, tokenization, and HSM-backed key management, while AI models monitor data access patterns to detect misuse in real time. Compliance is being operationalised through policy-as-code, automated control validation, and continuous compliance analytics, reducing manual audits. AI/ML-powered SIEM, SOAR, UEBA, and fraud detection systems enable predictive threat intelligence, anomaly detection, and faster incident response. In parallel, responsible AI governance—model explainability, drift detection, bias monitoring, and auditability—is integrated into platforms. Advanced observability, resilience engineering, and AI-assisted recovery ensure system integrity, availability, and sustained customer trust at scale.
Looking ahead, which emerging technologies or operating models do you believe will most significantly shape the future of NBFCs in India over the next few years?
Over the coming years, financial institutions are expected to evolve through a combination of modern technology stacks and platform-based operating models. Consent-driven data frameworks will enable a shift from traditional, bureau-led credit assessment to real-time, behavior- and cashflow-based decisioning using interoperable data rails. AI/ML and generative technologies will extend beyond credit evaluation into end-to-end automation, including intelligent document processing, proactive servicing, next-best-action engines, and AI-enabled collections, supported by robust MLOps, explainability, and model governance. Composable, cloud-native architectures built on API-first, event-driven microservices will support faster product innovation and seamless integrations. Embedded finance and ecosystem-led distribution models will increasingly define go-to-market strategies, with institutions operating as modular risk and product engines across multiple channels. On the trust front, privacy-enhancing technologies, tokenization, and zero-trust security will form the foundation. Finally, regulatory technology and continuous compliance capabilities will distinguish scalable, future-ready organisations.
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