Indian Wealth-Tech Platform Scales to 5 Million Users on BSE, NSE & Moneycontrol Data Pipeline

Vertical

Financial Services / Wealth-Tech

Client

Indian wealth-tech startup (name withheld under NDA)

Engagement Duration

24 months (ongoing)

Key Metric

5M+ active users, 72% infrastructure cost reduction, Series B closed at $180M valuation

Executive Summary

An Indian wealth-tech startup partnered with Actowiz Solutions over 24 months to build a comprehensive Indian financial data infrastructure covering BSE, NSE, Moneycontrol, Screener, AMFI, and SEBI sources. During the engagement, the platform scaled from 450,000 to over 5 million active users, reduced data infrastructure costs by 72% versus benchmark institutional alternatives, and raised a $45M Series B at a $180M valuation — with the comprehensive data moat positioned as a core differentiator. This case study documents how one of India's fastest-growing wealth-tech platforms built enterprise-grade financial data infrastructure on fintech-friendly economics.

Client Background

The client is a Mumbai-based wealth-tech platform founded in 2022, offering Indian retail investors mutual fund investments, stock research, portfolio analytics, and goal-based investment planning through a mobile-first experience.

The founding team — with backgrounds at Zerodha, Kotak Mahindra, and Goldman Sachs India — identified that Indian retail investors had excellent execution platforms (Zerodha Kite, Groww) but poor research and analytical tools. Existing platforms either charged institutional-grade subscription fees (out of reach for retail) or offered shallow, ad-driven free experiences.

Their product vision: institutional-quality research and analytical tools, with a consumer-grade UX, accessible to every Indian retail investor at low or no cost.

The challenge: institutional-quality tools require institutional-quality data. And Indian financial data infrastructure is famously hard.

Data Sources Required

To deliver their product vision, the team needed reliable feeds across:

• BSE and NSE for equities prices (end-of-day and intraday)
• Moneycontrol for financial statements and company data
• Screener for fundamental ratios and historical financials
• AMFI for mutual fund NAVs and scheme data, SEBI for regulatory disclosures and insider trading data
• MCA for corporate data and director mapping
• Corporate actions data with proper price adjustments for backtesting
• News feeds for company-specific news aggregation.

Initial Approaches That Didn't Work

The founding team tried three paths:

Paid institutional data vendors (Refinitiv, Bloomberg equivalents for India): quoted at ₹40 lakh+ annually with restrictive commercial terms incompatible with a B2C product.

Direct exchange APIs: technically available but with usage caps, restrictive terms, and limited historical depth

In-house scraping: a 3-engineer team spent 8 months building scrapers with data quality issues, regulatory uncertainty, and recurring anti-bot blocks.

By month 10 of their seed phase, they had 450,000 users but a fragile data infrastructure that the CTO described as "a dam with ten leaks — we patch one, another springs." Series A investors raised concerns about scalability before committing.

The Challenge: Indian Financial Data Has No Easy Answers

Indian financial data infrastructure poses challenges that generic scraping providers don’t solve:

1. Heterogeneous Sources

BSE, NSE, Moneycontrol, Screener, AMFI, SEBI — each has different data formats, different update cadences, different anti-bot measures. Unified consumption requires source-specific engineering.

2. Corporate Action Handling

Indian companies have frequent corporate actions (dividends, splits, bonus issues, rights, demergers, mergers). Historical price adjustments must be correct, or every backtest and historical return calculation is wrong.

3. Symbol History Continuity

Companies get renamed, merged, delisted, and suspended regularly. Tracking these changes is essential for survivorship-bias-free analysis.

4. Real-Time vs End-of-Day Requirements

Different product features have different data freshness requirements. Research tools work with end-of-day data. Portfolio tracking and alerts need near-real-time. Infrastructure must serve both economically.

5. Mutual Fund Complexity

Indian MF data includes daily NAVs, monthly portfolio disclosures, fund manager histories, and scheme categorisation changes. Tracking this properly requires dedicated MF-specialised infrastructure.

6. Regulatory Awareness

SEBI regulations on financial data usage, insider trading disclosures, and market data redistribution require careful legal navigation. Generic providers often don't understand these nuances.

7. Cost Sensitivity for B2C Models

Unlike institutional use cases where $500K+ data budgets are normal, B2C wealth-tech platforms must deliver institutional-quality data on B2C economics. Cost discipline is essential.

The Solution: Full-Stack Indian Financial Data Pipeline

Actowiz designed a comprehensive data infrastructure purpose-built for an Indian wealth-tech platform.

Component 1: Multi-Source Scraping Infrastructure

Continuous coverage across: BSE + NSE — end-of-day prices, corporate actions, shareholding, index data; Moneycontrol — financial statements, ratios, company data, news; Screener — fundamental ratios and 10+ year historical financials; AMFI — daily NAV data, scheme categorisation, portfolio holdings; SEBI — regulatory filings, insider trading disclosures; MCA — corporate data and director mapping (supplementary).

Component 2: Corporate Action Processing

Dedicated corporate action handling: Daily corporate action monitoring across BSE and NSE; Historical price adjustment for splits, bonuses, special dividends; Rights issue handling with proper entitlement calculations; Merger and demerger event processing with share ratio adjustments; Historical data delivered in both adjusted and unadjusted forms.

Component 3: Symbol History Continuity

Comprehensive tracking of: Symbol changes and rebranding events; Suspensions and resumptions; Delistings with final price capture; New listings as they occur; Parent-subsidiary relationship changes.

Component 4: Tiered Data Freshness Architecture

Different freshness tiers for different product features: Real-time tier (for portfolio tracking, alerts, live prices): sub-minute updates from direct exchange feeds; Intraday tier (for research, charts): 15-minute delayed data; End-of-day tier (for fundamental analysis, backtests): daily refresh; Fundamental tier (for financial statements): quarterly refresh with interim updates on filings.

Component 5: Mutual Fund Intelligence

Specialised MF data processing: Daily NAV ingestion from AMFI; Portfolio holding disclosures (monthly); Returns calculation across multiple time periods; Fund manager change tracking; Scheme categorisation and relabelling handling.

Component 6: Cost-Optimised Delivery

Rather than traditional API-call pricing, delivery was structured around: Bulk daily snapshots (economical for most user queries); Intraday refresh only on tiers that required it; Caching layer architecture that reduced redundant fetches; Delta-only updates where feasible. This architecture delivered 70%+ cost reduction vs. naive institutional-vendor-replica approaches.

Component 7: Compliance-Aware Architecture

Data handling compliant with: SEBI regulations on market data redistribution; RBI data localisation requirements; India's Digital Personal Data Protection Act (DPDP); Exchange-specific terms of use.

Regular legal review ensured ongoing compliance as regulations evolved.

Implementation Timeline

Months 1-2: Requirements gathering, architecture design, priority source identification Months 3-4: BSE + NSE production pipeline live; AMFI integration Month 5: Moneycontrol + Screener integration; fundamental data delivery begins Month 6: Corporate action processing deployed; historical adjustments applied Months 7-8: Real-time tier architecture deployed for portfolio tracking Months 9-12: SEBI + MCA integration; full data coverage achieved Months 13-18: Scale optimisation as user base grew 3x Months 19-24: Enterprise reliability hardening; Series B due diligence support

Results: Quantified Outcomes

Business Metrics

• Users grew 11x: from 450,000 to 5 million+ over the 24-month engagement
• Data-driven feature adoption: 78% of active users use research/analytical features (vs. <30% typical for competitor apps)
• Series B raised: $45M at $180M valuation — up from sub-$20M seed valuation
• Data infrastructure cited in Series B investment memo as a key differentiator

Cost Efficiency

• Data infrastructure cost reduction: 72% vs. equivalent institutional vendor alternatives
• Cost per active user for data infrastructure: under ₹12/month — economical for freemium and low-subscription models
• Engineering team reallocation: 3 engineers freed from data infrastructure to ML features

Data Quality Metrics

• 99.98% pipeline uptime across 24 months
• 0.15% error rate on delivered financial data
• Zero regulatory incidents across 2+ years of operation
• Corporate action adjustment accuracy: 100% on sampled 2,000 events (institutional-grade)

Product Impact

• "Time to insight" in the app: user queries return in under 200ms (from 1.5s baseline)
• App Store rating: improved from 4.2 to 4.7 over engagement
• Daily active usage: increased 4.3x per user (more engaged users)
• Cross-selling of premium tier: 23% of active users converted to paid tier (well above industry benchmarks)

Use Case Deep Dive: How Backtesting Became a Growth Engine

One of the platform’s most-used features is “Backtest Your Strategy” — allowing users to test investment strategies on historical Indian market data.

The data requirements for backtesting are brutal: Accurate historical prices going back 10+ years - Proper corporate action adjustments for every event in that history - Survivorship-bias-free universe (including delisted, suspended, and renamed companies) - Fundamental data history aligned to price history - Fast query response times

Before the Actowiz engagement: Backtests took 45-90 seconds per strategy and had inconsistent accuracy due to imperfect corporate action handling.

After deployment: Backtests complete in 2-4 seconds. Accuracy is institutional-grade — validated against published academic papers using the same data. Users run 8-12x more backtests per session.

Business impact: Backtesting became the #1 “aha moment” feature for new users. Users who ran a backtest in their first session converted to paid tiers at 3.1x the rate of users who didn’t.

Lessons Learned

1. Data Quality Compounds into User Experience

Every piece of financial data in the app is visible to users. A single misstated corporate action creates visible errors that damage user trust. Data quality isn't an engineering concern — it's a retention metric.

2. Real-Time Isn't Always Necessary

The team's initial instinct was "we need everything in real-time." Careful architecture showed that tiered freshness — real-time only where it added user value — delivered 70%+ cost savings with no UX compromise.

3. Backtesting Is the Killer Feature for Serious Retail

For users who want to become better investors, backtesting is magical. But it only works if the underlying data is institutionally clean. This is hard to achieve without specialised data infrastructure.

4. Corporate Action Handling Is Underestimated

Most wealth-tech teams don't understand how much engineering goes into proper corporate action processing. Getting this right differentiates serious platforms from superficial ones.

5. Compliance Is Strategic, Not Tactical

Treating data compliance as strategic (not a compliance-team afterthought) meant the platform could scale confidently. Competitors that cut corners have faced increasing regulatory friction.

6. Specialised Partners Enable Founder Focus

The engineering hours saved on data infrastructure went to the features users actually paid for. This focus accelerated product development by 12-18 months vs. a DIY approach.

About Actowiz Solutions

Actowiz Solutions operates specialised Indian financial data infrastructure serving quant funds, fintech startups, wealth-tech platforms, PMS/AIF managers, and corporate intelligence platforms.

Our Indian financial data specialisations: - Multi-source financial data scraping (BSE, NSE, Moneycontrol, Screener, AMFI, SEBI, MCA) - Corporate action processing and historical price adjustments - Symbol history continuity and survivorship-bias-free datasets - Real-time and tiered freshness architectures - Mutual fund intelligence - Regulatory compliance-aware delivery - Similar capabilities across US (SEC EDGAR), UK (FCA, Companies House), and UAE (DFM, ADX) financial data