Q1. You’ve led IT transformations across multiple financial service organisations—how did your early experience in building enterprise systems shape your current approach to designing scalable, customer-centric digital ecosystems?

When I look back at my 25+ years in financial services technology, the journey has been shaped by one theme: deep, end-to-end understanding of both technology and business. I began my career as a hands-on developer, writing code across the front end, the back end, and even getting involved in infrastructure setup and installation. Those early years gave me a ground-level understanding of how enterprise systems truly work—what breaks, what scales, and what customers actually feel on the front lines.

Over time, that foundation became invaluable as I moved beyond pure development. Around 2003, I joined ICICI Prudential Mutual Fund, where I shifted into the IT business side. This role pushed me to see technology not merely as systems and code, but as an enabler of business outcomes. I began leading implementations of enterprise applications—CRM, data warehouses, API platforms—and eventually took responsibility for enterprise architecture and PMO functions.

In every organization I’ve worked with, the structure has always been broad: lending business, insurance, mutual funds, home finance, leasing, SME lending. My responsibility consistently spanned common, group-wide applications rather than isolated vertical systems. Whether CRM, data warehouse, API management, websites, ACS/eCOS portals, or mobile apps for customers, partners, and dealers—my role has been to build scalable, reliable platforms that can serve diverse business lines while maintaining a unified experience.

I’ve also overseen the complete communication stack—WhatsApp, email, SMS, RCS—ensuring that every digital touchpoint delivers a consistent and seamless customer experience. All of this shaped my approach to designing enterprise-grade, customer-centric digital ecosystems that can evolve with changing business needs.

 

Q2. As financial institutions move from legacy CRM and DWH platforms to unified data fabrics, how do you ensure that integration speed doesn’t compromise governance or data quality, especially at Mahindra Finance’s scale?

As organizations scale, especially at the size of Mahindra Finance or similar large NBFCs, the challenge is clear:

How do you modernize into unified data fabrics without disturbing core systems or compromising governance?

The approach we established is deliberate and structured.

We created a common data layer that operates independently from core transactional systems like LMS, LOS, and accounting. These systems must never be burdened during business hours; their job is to serve customers and operations. So data extraction happens only during night batches.

During those windows, data flows into multiple staging areas where:

• raw data is ingested,
• ETL processes run,
• transformations occur based on unified structures,
• data from multiple LOS/LMS systems is normalized,
• and everything is prepared for the centralized data warehouse.

Because batch windows are only 4–5 hours, full reloads are not feasible; so we rely heavily on incremental processing—only loading what has changed. This gives us both speed and reliability.

Before any data is moved into the final warehouse, we run:

• automated count checks,
• validation checks,
• audit logging,
• and quality gates to ensure data integrity.

For cases where near-real-time data is required—such as a customer opening the mobile app to see their updated balance—we implemented an active-active database setup. This allows specific customer-critical data to refresh every 10–30 minutes without straining the core systems.

The result is a unified data fabric that supports:

• CRM
• MIS/BI tools
• campaign engines
• cross-sell and upsell models
• regulatory reporting
• and real-time customer experiences

—while maintaining strong governance and data quality.

 

Q3. Middleware and APIs are now the backbone of financial interoperability. How are you seeing the role of API gateways like Apigee evolve in enabling open finance, embedded lending, and ecosystem partnerships?

Today, no financial application can function without APIs. Middleware is the backbone of open finance, embedded lending, and ecosystem partnerships. In my experience implementing systems using IBM ESB and full Apigee, I’ve seen this firsthand.

Without proper API management, organizations end up writing and rewriting the same integration logic across:

• multiple LOS systems,
• multiple LMS platforms,
• CRM,
• mobile apps,
• websites,
• partner platforms,
• dealer systems,
• and third-party integrations.

A simple example is credit bureau checks. If an organization has several LOS/LMS platforms for different business lines—vehicle finance, consumer finance, SME loans, leasing—you would otherwise need to embed bureau logic in each system separately.

With an API gateway:

the logic is written once,
reused everywhere,
and exposed securely to internal and external consumers.

API gateways also ensure that backend systems are not exposed to external partners. Only the gateway interacts with the outside world, enforcing policies, throttling, security layers, governance rules, and performance controls.

Inside the organization, too, APIs prevent duplication. Customer mobile apps, partner apps, dealer apps, CRM, and website systems can all consume the same APIs instead of bespoke integrations. This reduces system size, improves performance, and enhances scalability.

In essence, API management has become the central nervous system for both internal operations and external ecosystem partnerships.

 

Q4. You’ve overseen major BI/MIS and data-lake implementations. With the advent of near-real-time analytics and generative BI tools, what capabilities do you believe are critical for financial organisations to unlock decision-making speed without inflating cost?

When implementing BI/MIS platforms or data lakes, the first priority is robust infrastructure. Whether you choose Microsoft, SAP, Snowflake, or any other stack, you must ensure the right capacity, right architecture, and right scalability.

Key capabilities include:

• Parallel processing to handle large data volumes
• Multiple staging layers to process data simultaneously
• Purpose-built transformation logic aligned with real business needs
• Clear understanding of reporting audiences (CXO dashboards vs. branch-level reports)
• Performance tuning and optimization at tool level and data level

If your logic is not correctly designed, your MIS/BI tool cannot serve the business on time.

For near real-time requirements—like unified customer views for service teams—dedicated processes are needed to fetch and refresh only critical data every:

• 10 minutes,
• or 30 minutes,
• or hourly, depending on the need.

Since the main data warehouse holds T-1 data, these real-time slices fill the gap, enabling service teams to operate with up-to-date information during business hours.

This selective, requirement-driven approach ensures speed without inflating costs.

 

Q5. Managing IT consolidation, IAM/IDM, and BCP/DR across thousands of users is a massive operational feat. How do you balance regulatory compliance, security, and business continuity when deploying shared enterprise applications?

Managing consolidation of IT systems, identity access management, and BCP/DR at enterprise scale requires a highly controlled, highly documented approach.

We regularly conduct full BCP/DR runs—sometimes operating the entire business for up to 36 hours from the DR data center. To prepare for this, we begin with:

• listing every application,
• mapping every dependency,
• identifying connectivity requirements,
• recording firewall rules,
• documenting internal ticket flows,
• and defining the support teams involved.

Before any official drill, we run technical BCP/DR rehearsals at night. Production systems are turned off, and DR systems are turned on, allowing us to test real switching conditions.

These exercises span one to two months and involve:

• more than 100 applications,
• 500–600 services,
• multiple lines of business,
• and strict RBI-mandated requirements.

Every issue discovered—whether downtime, connectivity gaps, or configuration errors—is documented and resolved before the official DR run.

This disciplined approach ensures that enterprise systems are resilient, compliant, and secure.

 

Q6. As automation and AI reshape customer communication (from WhatsApp-based engagement to proactive credit servicing), what’s your view on the next frontier of digital experience in financial services?

In financial services, most companies today offer similar products—home loans, personal loans, vehicle loans, SME finance, leasing. Even interest rates, except in the case of national banks, are comparable.

This means the real differentiator is customer service:

• How fast can you approve a loan?
• How quickly can you resolve a query?
• How efficiently can you avoid risky lending without delaying customers?

This is where AI, automation, AML, and CICD pipelines come in.

Wherever automation is possible—whether in onboarding flows, document evaluation, internal routing, or risk checks—it improves both speed and accuracy.

In customer servicing:

• queries must be resolved instantly,
• unified customer views must be nearly real time,
• emails and calls must be routed to the right teams automatically,
• and frontline teams must have all customer information ready before the call connects.

AI allows us to:

• pre-identify customers based on mobile numbers,
• understand patterns of past communication,
• proactively surface likely issues,
• and resolve many queries even before they reach a human agent.

The future is hyper-personalized, predictive financial servicing, where most friction is removed before the customer feels it.

 

Q7. From an investor or board perspective, what signals in a financial institution’s IT roadmap indicate that its technology maturity directly supports business scalability and long-term value creation?

From an investor’s perspective, technology maturity is not measured by the number of tools an organization uses—it’s measured by how fast the organization can adopt digital processes to:

• approve loans faster,
• service customers better,
• reduce risk,
• and scale operations efficiently.

They look for:

• robust IT systems and architecture,
• strong security controls (IDM, MFA, auditing),
• comprehensive reporting,
• process governance,
• clear application strategies,
• and operational agility.

Ultimately, technology maturity is proven when IT investments directly translate into business scalability and long-term value creation.