In Fintech, Trust Is the Real Metric: How Navi Is Rewiring Customer Experience with AI
In fintech, speed gets attention. Trust builds the business. At Navi, customer experience has been designed with that clarity. This approach has translated into tangible outcomes. AISHA, Navi’s GenAI engine, now resolves over 80% of customer queries end-to-end — handling everything from transaction updates to loan-related requests with speed and consistency.
At the same time, more complex situations such as disputes or financially sensitive cases are seamlessly routed to human agents, ensuring that judgment and empathy are not compromised. This balance is reinforced by a channel-agnostic support model, where customers can move across chat, call, and email without losing context — reducing friction and minimizing escalations. The impact is visible: complaint volumes have declined, turnaround times have improved, and customer satisfaction scores continue to outperform industry benchmarks.
What differentiates Navi is not the adoption of AI, but the way it has been embedded into operations — as a system designed to improve resolution quality and build trust, not just automate interactions. In this conversation, Sugandha Sharma, Head of Operations – Navi shares how Navi is rethinking customer experience — moving from reactive support to a model where AI delivers scale, humans deliver reassurance, and every interaction strengthens customer confidence.
Some edited excerpts:
Fintech customers don’t just expect speed — they expect reassurance. When did Navi realize that customer experience would become a strategic differentiator rather than just an operational function? Why did Navi choose to invest deeply in CX infrastructure at this stage of its journey?
At Navi, customer experience has always been central to how we design and deliver our products. From the outset, our north star has been a simple question: “Is the customer satisfied?” As we scaled, we recognized that satisfaction in financial services is shaped by more than quick responses, it’s about clarity, reassurance, and trust. A fast response that does not resolve the issue can easily erode trust.
Customers reaching out to us are rarely asking abstract product questions; they are raising concerns that affect their money, credit, and peace of mind. In that context, speed alone is not enough. An answer that is fast but inaccurate can easily shake customer confidence.
A customer’s experience during a problem or query becomes their most memorable interaction with a brand. This is especially important at Navi, where many of our users are first-time borrowers or first-time insurance buyers. For them, the way an issue is resolved often becomes their first real impression of a digital financial platform. That’s when CX clearly emerged as a strategic differentiator for us rather than just an operational function.
As our product portfolio and customer base expanded, it became important that our support systems scale with the same discipline. This is why we invested early in strengthening our CX infrastructure across processes, technology, and teams so that customers receive quick, clear, and complete resolutions, ideally in a single interaction.
Ultimately, we see CX not simply as support, but as a core pillar of building trust in digital finance. Reputation compounds over time, and every well-resolved interaction strengthens the long-term relationship we are building with our customers.
You’ve seen a significant year-on-year decline in complaint volumes. What changed operationally to make that happen?
The decline in complaint volumes is largely the result of fixing friction at the source rather than just handling complaints faster.
First, we strengthened first-contact resolution by building a balanced AI-plus-human support model. Today, a large share of routine queries such as transaction updates or loan-related information are handled through AI-driven chatbots and IVRs, allowing customers to receive instant answers. This allows human agents to focus on complex or sensitive cases – disputes, investigations, or scenarios requiring human expertise and empathy, improving the quality of resolutions where it matters most.
Second, we have focused on cohort-based analysis and root-cause identification. Instead of treating complaints as isolated incidents, we analyze patterns across products, geographies, and customer segments to identify recurring pain points. Those insights are fed back into product and process improvements so the same problems don’t repeat for other customers.
Finally, we operate with clear response-time benchmarks across our channels ensuring customers receive timely and predictable support. .
Together, these changes help resolve issues earlier, prevent escalation, and ultimately reduce the overall volume of complaints.
AISHA and your GenAI systems now resolve a significant share of queries end-to-end. What kinds of queries is AI taking care of now and what are some of the more complex queries that AI will handle in the future?
AISHA and our GenAI systems today resolve over 80% of customer queries end-to-end. AISHA began as a text-based chatbot and has since expanded into a two-way voice bot, supporting customers across chat, IVR, and email.
Currently, AI handles high-volume, predictable queries such as pending or failed transaction updates, refund and payment inquiries, loan-related questions, and other requests with well-defined resolution paths. These are areas where AI performs best – delivering instant responses, consistent answers, and 24/7 availability.
More complex scenarios – such as disputes, investigations, regulatory or risk-sensitive cases, and emotionally sensitive situations, are handled by human agents, where judgment and empathy are essential.
Looking ahead, AI will take on more contextual and proactive roles. Over the next 12-18 months, we expect GenAI to handle more complex servicing queries and initial customer interactions, identify potential issues before customers raise them, and then seamlessly route cases that require deeper investigation to human teams.
The guiding principle remains that AI will take on greater complexity only when it can do so with the same accuracy and accountability we expect from humans.
Explainability is becoming critical in financial AI. How do you ensure that decisions made by AI can be audited, understood, and defended?
Explainability begins with how the AI is trained and governed. We train our AI systems using real customer interaction data – specifically analyzing the types of queries we receive and how human agents resolve them in cases where customers were satisfied with the outcome. This helps provide the model with clear context and reliable resolution patterns, reducing the risk of inaccurate information and ensuring responses are grounded in proven support flows.
We also introduce strict checks and controlled rollouts. Queries are mapped carefully so that AI only handles categories where resolution paths are well defined. We allow AI to take on greater complexity when it consistently demonstrates accuracy and accountability.
Operationally, every AI deployment is continuously monitored. We track metrics such as resolution success, customer journey completion, and escalation patterns to ensure the system is performing as intended. In addition, we regularly sample and audit bot conversations – not just for factual accuracy, but also for clarity, empathy, and conversational quality.
Finally, we maintain a clear human support escalation pathway. Any interaction involving sensitive concerns – such as regulatory considerations, financial implications, disputes is escalated by the bot for human support. This ensures that critical decisions remain auditable, understandable, and accountable, while AI continues to operate within well-defined boundaries.
Empathy doesn’t scale easily. What does “empathy by design” mean in a GenAI-enabled support environment? How do you train AI systems to understand financial stress — for example, missed payments or claim disputes — without escalating friction?
“Empathy by design” means building empathy into the system architecture itself, rather than expecting AI to simply sound polite. In a GenAI-enabled support environment, this starts with how the model is trained and governed.
We train our models on real customer interaction patterns where resolutions led to positive customer outcomes, so the AI learns not just the factual response but also the tone, clarity, and reassurance that helped resolve the situation. The system is designed to generate context-aware responses, interpret emotional cues in conversations, and respond in a way that acknowledges the customer’s situation rather than delivering purely transactional answers.
We also embed guardrails for tone, clarity, and reassurance, and regularly audit bot conversations to evaluate not just accuracy but also conversational quality and empathy signals. Metrics like CSAT and escalation patterns help us continuously refine how the system interacts with customers.
At the same time, we’re very clear about where AI should step back. Situations involving financial stress such as missed payments, disputes, or complex claims often require judgment and emotional sensitivity. In those cases, the system is designed to detect distress signals and seamlessly escalate to human agents, who can bring the empathy and contextual understanding that AI cannot replicate.
Ultimately, the goal is to combine AI’s strengths of speed, consistency, and scale with human judgment and emotional intelligence, ensuring that customers receive both efficient support and genuine reassurance when it matters most.
Many brands deploy AI as a superficial layer. What makes Navi’s approach fundamentally different?
What differentiates Navi’s approach is that AI is integrated into the core of our systems, rather than added as a superficial layer. From the beginning, our focus has been to embed AI into operations so that scale, speed, and service quality improve together, instead of simply automating isolated touchpoints.
Every customer facing AI rollout has been trust-led and phased, with strong guardrails. AI was introduced as an assistive layer, handling well-defined queries while humans continued to manage exceptions and complex cases. This approach helped build internal confidence first and, over time, customer trust as well.
We have always focused on measurable outcomes rather than an innovation display. Every deployment is evaluated against clear operational metrics such as resolution rates, CSAT, response times, escalation trends, and cost efficiency, to ensure that the technology is delivering real value.
Finally, our goal is to use AI to improve resolution quality, not just deflect queries. By connecting AI systems to customer context through APIs and internal data layers, the AI understands where the customer is in their journey and can provide relevant assistance. In this model, AI and human agents work together as a resolution layer, combining speed and scale with human judgment and empathy.
Over the next 12-24 months, what are some of the initiatives planned that will define the next leap in customer experience for your firm?
Over the next 12-24 months, our focus is on evolving customer support from reactive to predictive. The goal is to ensure that assistance begins before a customer even needs to raise a query, reducing friction across the journey.
One key initiative is advancing the accuracy and capability of our AI systems, while strengthening guardrails to ensure responses remain reliable, accountable, and aligned with customer needs. As the technology matures, AI will be able to handle a broader set of service interactions while maintaining the same quality standards we expect from human teams.
We’re also investing in proactive support models that can identify potential issues early whether it’s a transaction delay, a payment reminder, or a process bottleneck; and guide the customer before the situation escalates into a support request.
Another priority is deeper integration across channels, ensuring customers can move seamlessly between chat, voice, and other touchpoints without losing context. Finally, we’re further exploring predictive insights that help guide financial behavior, enabling customers to make better decisions with timely nudges and contextual information.
The broader aim is to create a support ecosystem that feels effortless, intuitive, and human.