Our focus is on making credit invisible, embedded directly into the customer’s digital journey: Ramesh Aithal, CDO, L&T Finance
L&T Finance is in the midst of a deep technology transformation that goes far beyond incremental digitisation. A reimagined digital core that treats credit decisioning, customer engagement and risk management as intelligent, interconnected platforms rather than isolated systems.
In an exclusive interaction with Express Computer, Ramesh Aithal, Chief Digital Officer, L&T Finance, talks about how they are modernising the lending value chain through dense AI-driven credit ecosystems, embedded finance partnerships, cloud-native architectures and agentic automation. Starting from handling complex underwriting decisions at scale to embedding credit invisibly within partner journeys, he also explains how technology is being used as a strategic growth lever rather than an operational utility.
The conversation also explores areas like the rapid integration of the Gold Loan business, the deployment of conversational AI in collections and servicing, and the evolution of PLANET 3.0 into a multi-service digital platform. Crucially, he discusses balancing near-term efficiency with long-term bets on AI, resilience and intelligent automation, offering a clear view into how NBFCs must prepare for a future defined by contextual finance, voice-led interactions and predictive decision-making.
L&T Finance is undergoing significant technology modernisation across the lending value chain. From a CDO’s perspective, what have been the most transformative digital interventions so far, and how are they contributing to scalable, sustainable growth?
One of the most recent transformative digital interventions at our end has been the creation of a dense ecosystem for credit decisioning. This ecosystem is engineered for scale, complexity, and accuracy, running over 50 validations to approve a single case. The process is structured into intelligent layers, beginning with rigorous checks at the login stage, including multiple identity checks, contactability checks, geospatial checks, and land verification, which establishes a high-confidence customer profile. This data is then enriched through industry and cross-entity intelligence, followed by a Cognitive Risk Layer, which utilises proprietary Artificial Intelligence (AI) and Machine Learning (ML) models to provide nuanced, predictive risk assessment. The final stage is the Autonomous Decision Layer. This includes engines like Cyclops by LTF, our next-gen credit underwriting engine, Project Nostradamus, for predictive risk modelling, and the Business Rule Engine to execute automated, final lending decisions.
These digital interventions directly contribute to scalable, sustainable growth. Scalability is achieved by automating the entire complex workflow via the Autonomous Decision Layer, allowing LTF to handle massive application volumes without a proportional increase in manual costs. Moreover, the system ensures sustainability through disciplined risk management through the Cognitive Risk Layer and AI-Based Portfolio Monitoring that ensures growth is risk-calibrated, improving asset quality and making the growth trajectory more predictable and resilient. In essence, this modernisation effort has transformed credit decisioning from a potential bottleneck into a strategic, intelligent asset.
Embedded finance is becoming central to LTF’s personal loan strategy. How are you leveraging partnerships and digital platforms to make credit “invisible” and intuitive for customers, and what are the key architectural principles powering this shift?
We are pivoting towards embedded finance as a key component of our personal loan strategy, aiming for finance that becomes invisible, intuitive and truly embedded for customers. The foundational principle driving this shift is leveraging partnerships as the primary growth engine. This allows the company to acquire customers not just through traditional channels, but by integrating credit where customers are already active. LTF has established extensive partnerships, including marketplace partnerships, OEM (Original Equipment Manufacturers) partnerships, and fintech aggregators. These partnerships have scaled up extensively over the past year, with LTF delivering a highly customised loan journey tailored specifically to the needs and platforms of each partner.
This seamless integration and customisation are powered by a robust architectural framework encompassing API orchestration, credit policy, and deployment capabilities. This system is engineered for faster partner onboarding tools such as modular journeys, an open API stack, and self-testing frameworks. Architecturally, the platform is built on a cloud-native, microservices-based architecture and incorporates smart load balancing, helping in customer acquisition at scale and with low latency levels.
By utilising this agile, high-performance technical architecture, we effectively embed credit access directly into the consumer’s digital journey.
The integration of the Gold Loan business marks an important diversification step. What were the major technology considerations during this integration, and how are you ensuring a unified experience—both operationally and for end-customers?
As a major strategic diversification, the integration of the Gold Loan business involved a comprehensive scope, mandating the execution of end-to-end tech integration and day-zero setup in a record time of about nine weeks. LTF successfully built and deployed core operational capabilities, including the loan journey, collections infrastructure, and a Direct-to-Consumer (D2C) setup. This rapid deployment was complemented with a focus on governance, ensuring regulatory and risk compliance, branch connectivity, employee transition, and HRMS setup.
A key consideration in integrating the Gold Loan business was addressing its inherent security requirements due to the highly sensitive information. To meet this challenge, multiple live command centres were built for 24×7 monitoring. Enhanced security was enforced through centrally controlled two-factor authentication for vaults and the setup of an exclusive One Time Code (OTC) Vault. Furthermore, the entire technology stack was upgraded to match enterprise benchmarks.
This comprehensive technological and security overhaul was crucial for delivering a unified experience. Operationally, the standardised tech stack, branch connectivity, and centralised monitoring ensure consistent performance and risk management across the entire integrated business. For end customers, the investment in a seamless loan journey, D2C setup, and robust security measures, from the two-factor vault authentication to the comprehensive CCTV coverage, provides assurance, confidence, and a consistent, high-quality service experience synonymous with the company’s brand.
LTF has implemented AI-based outbound calling for lead engagement. Could you elaborate on the design philosophy behind this system, the role of conversational AI, and the measurable impact it has had on turnaround times?
LTF has implemented a cutting-edge AI voice agent for outbound calling, specifically targeting collections efficiency for pre-qualified personal loan leads, operating on an AI-ML-based method of sales augmentation. The core design philosophy is centred on three broad pillars to ensure optimal performance. First, Quick Lead Filtering helps to swiftly identify high-quality prospects. Second, parallel calling maximises reach by enabling the agent to handle multiple calls at once. Finally, real-time dashboards provide live performance metrics, allowing continuous optimisation of the collection strategy.
The agent’s strength lies in its conversational AI, which leverages agentic AI principles to enhance the customer experience. The dialogue is designed to be human-like and dynamic, capable of accurately pitching the collection offer while being entirely functional. The agent is multilingual, supporting an impressive 11 languages, i.e., English, Hindi, Kannada, Tamil, Telugu, Marathi, Gujarati, Odia, Bengali, Malayalam, and Assamese, to ensure maximum regional accessibility. This sophisticated agent is fully integrated in real time with tools like SMS, WhatsApp, the payment system, and Customer Relationship Management for a seamless end-to-end collections process. This implementation has measurable benefits, as evidenced by an 80% improvement in Lead Engagement TAT (Turnaround Time).
PLANET 3.0 marks a major milestone for L&T Finance. What new features have been introduced in this version, and can you share more about the development of the industry’s first conversational agent embedded within the platform?
The revamped PLANET 3.0 is a major upgrade in terms of design, performance, and customer experience. The platform significantly expands its utility by embedding strategic partnerships and critical financial services directly into the customer journey. Key feature enhancements include the Auction Portal for two-wheeler and tractor vehicles, which allows customers to view and bid for repossessed vehicles transparently, ensuring faster liquidation and improved recovery outcomes. Partnerships drive customer convenience, such as the Two-Wheeler Marketplace, enabling users to explore and compare over 350 models for informed purchase decisions. Furthermore, the Farm Marketplace serves as a reliable digital companion for agri-loan customers, offering over 10 functionalities, including Live Mandi Prices, Weather Alerts, Soil Testing Services, and Expert Agri-advisory. Financial literacy and convenience are boosted by the property worth for home loan pre-qualification and ITR filing support, a strong value-add for the self-employed segment. Simultaneously, the platform fosters a smarter dealer ecosystem by introducing features like a Personalised Dashboard, One-Step TA Withdrawal, Real-Time Retail Dashboard, and Complete Portfolio Summary.
The revamped version of the PLANET app is particularly notable for launching the industry’s first conversational agent embedded within the platform (based on LTF’s internal assessment of peer BFSI applications). This multilingual voice agent transforms the user experience by handling comprehensive loan servicing and query handling for both existing and new loans. The agent was developed with sophisticated capabilities, including Emotional Aware Response and Interruptions Handling to ensure smooth, natural interactions, and maintains real-time transcripts for documentation. The focus on superior user experience is evident in its rapid response time, acknowledgements, and seamless language switching. Furthermore, the development prioritised integrity, ensuring factual correctness of policy, loan details, and processes. This agent acts as a round-the-clock service provider, improving customer support efficiency and user engagement.
As the CDO, how do you balance short-term operational digitisation needs with long-term technology bets, such as AI, cloud modernisation, and intelligent automation? What are your priority focus areas for the next 12–18 months?
We try to balance immediate operational digitisation and long-term technology bets through a disciplined bimodal approach. We prioritise operational digitisation, for instance, implementing multi-step workflows with AI-based automation and leveraging India Stack for compliance like e-KYC, in the short term to achieve immediate efficiency gains, reduced cost-to-serve, and improved regulatory adherence; these savings are then reinvested to self-fund our long-term transformation. The long-term effort focuses on one of our key pillars to achieve Lakshya goals, i.e., implementing futuristic digital architecture, by committing to building resilience at scale, making large-scale bets on agentic AI for critical functions like risk prediction, credit and sanction. The synergy is maintained by ensuring that the clean data and improved processes from the operational core feed the predictive power of the AI models, all governed by a ‘product mindset’ and rigorous observability using ‘AI-driven anomaly detection’ to ensure successful transition and scalability of these strategic bets.
Over the next quarters, our focus is on service intelligence, system resilience and performance by re-engineering business workflows for scale through innovation in architecture, automation and digital design. In addition, our ‘always-on’ digital backbone is integrated into a self-healing, adaptive, and continuously optimised ecosystem to ensure reliability at scale. We are dedicated to augmenting in-house productisation by designing modular, reusable, and outcome-orientated platforms that power business growth, which will support advancing toward an AI-led service ecosystem that anticipates needs, personalises engagement, and acts proactively. Furthermore, our efforts would be focused on strengthening the AI-driven collections stack for intelligent, automated, and insight-led recoveries.
With rapid innovation across fintech, lending, and embedded ecosystems, what emerging technologies or digital capabilities do you believe will redefine the NBFC landscape, and how is L&T Finance preparing to lead in this environment?
The NBFC landscape is set to be redefined by digital capabilities that emphasise deep personalisation, intelligent automation, and enhanced security. This transformation is driven by contextual finance, which customises every interaction, product, pricing, and recommendation based on predictive analytics of customer behaviour and life events. This level of personalisation is underpinned by multi-step workflows with AI-based automation, enabling systems to plan, execute, and optimise complex workflows autonomously instead of merely responding to static inputs. Furthermore, customer interaction is shifting to voice and conversational banking, where embedded voice interfaces allow users to transact, ask questions, and receive advice simply by speaking. Crucially, access and data protection will rely on multimodal authentication, which enhances security by combining multiple verification methods such as biometrics, voice, and behaviour, to ensure seamless and fraud-resistant user access.
We are preparing to lead this environment through tech modernisation. This involves leveraging agentic AI for sustainable growth across multiple areas: enhancing customer experience via virtual assistants and servicing agents; improving decision-making in identity and fraud protection and risk prediction, credit and sanction; and boosting productivity in product development and production support, utilising predictive monitoring, intelligent scaling, auto-remediation, and security intelligence.
