Design and development of mobile wallet solution for a leading telco in South East Asia, delivering a complete financial ecosystem that featured:

• Seamless banking integrations and transaction capabilities
• Intuitive user journeys optimized for customer engagement
• Dedicated merchant applications to expand marketplace reach

Architected and implemented a comprehensive consent management solution on OneTrust that centralizes all customer privacy preferences across 50 million users. The platform provides:

• Consolidated visibility of consent profiles enterprise-wide
• Seamless integration with multiple legacy systems
• Standardized consent collection and verification processes
• Real-time preference management capabilities

This initiative strengthened customers privacy compliance framework while enhancing customer trust through transparent data handling practices.

Challenge:
The client’s limited product flexibility and aggressive upselling led to customer dissatisfaction and revenue loss due to an inability to respond to market changes.

Solution:
We deployed an AI-powered 360° pricing strategy using predictive models, price elasticity, and location-based competitive intelligence. CMS and charging systems were upgraded to support real-time, personalized product offers.

Results:

• 5x Revenue Uplift on inbound channels
• 35% Higher ARPU for customers under dynamic pricing
• +3 Dynamic pricing NPS vs Regular pricing NPS

Challenge:
In a competitive telecom landscape, retaining value-seeking prepaid customers became difficult due to ineffective traditional churn models and aggressive competitor offerings.

Solution:
We built an AI-powered churn framework to segment users into key behavioral categories and personalized CVM offers accordingly. We implemented an omnichannel strategy to deliver targeted messages via mobile, USSD, SMS, and digital touchpoints.

Results:

• 20% lower churn rate in targeted base vs. control group
• Up to 88% campaign efficiency across segments
• Smart segmentation drove timely, relevant offers and improved retention

Challenge:
Customer experience issues, such as poor network coverage or pricing dissatisfaction, varied greatly by region, but existing systems lacked the visibility to act on hyper-localized insights.

Solution:
We integrated location data with AI-driven NPS analysis (Topic Modeling, Predictive Analytics) to map customer sentiment and complaints at national, city, and even tower-level precision. By combining customer feedback, usage metrics, revenue data and network performance KPIs, we built a location-aware ML recommendation engine that:

• Detects problematic zones (e.g., poor coverage, billing issues)
• Prioritizes network investments and customer experience actions by location
• Tracks NPS trends and ARPU changes at a regional level

Results:

• Pinpointed and resolved local service issues
• Enabled targeted churn reduction strategies per region
• Informed smarter network rollout plans using regional KPIs

Challenge:
The Telecom operator faced increasing pressure to deliver personalized customer experiences at scale. Despite investing in machine learning for churn prediction, usage forecasting, and upsell targeting, their data science workflows were slow, inconsistent, and hard to deploy across multiple platforms.

Solution:

To overcome these challenges, we adopted a centralized MLOps Model Factory, enabling end-to-end automation and governance of the AI lifecycle.

Key capabilities implemented:

• Unified CI/CD Pipelines: For automated training, testing, and deployment of models.
• Model Registry: Track model versions and promote best-performing models across markets.
• Monitoring & Drift Detection: Continuously assess model performance in production to trigger retraining when needed.
• Reusable Components: Accelerated rollout of model assets (e.g., churn prediction, ARPU uplift, next-best-offer) across multiple teams.

Results:

• Model Deployment Time: From 3-4 weeks to under 1 week.
• Reuse-able Assets: From limited reuseable assets to 80% of the assets being used for different use cases across the company.
• Offer-Personalization: From Rule-Based approach to Real-Time ML Driven.

Comprehensive digital transformation study examining the consolidation of disparate finance operations across eight European countries into a centralized shared service center in Eastern Europe.

The engagement involved:

• Analyzing operational efficiencies and cost structures across multiple finance centers
• Identifying process standardization opportunities and technology integration requirements
• Developing a robust business case with detailed ROI projections and implementation roadmap
• Creating a compelling change management strategy addressing cultural and operational transitions

The resulting strategic recommendation provided DHL’s regional board with a clear financial justification, risk mitigation approach, and phased implementation plan for this significant organizational transformation.

Orchestrated a comprehensive business transformation for HP’s global operations, delivering:

• Strategic process re-engineering optimized for offshore delivery models
• Custom solutions addressing complex technical, regulatory, and cultural requirements
• Seamless consolidation of operations across multiple international locations
• Implementation framework balancing global standardization with necessary country-specific adaptations

This initiative established a cohesive global operating model that maintained compliance with local regulations while achieving significant cost efficiencies and process improvements across HP’s international footprint.

Led the implementation of Mambu as a core banking solution for an Indonesian MSME neobank, addressing critical challenges including slow operations, irregular disbursements, and offline processes.

Key Achievements:

• Supported the client in securing a banking license in just 4 months
• Successfully onboarded 300,000 MSMEs to the platform
• Migrated over 200,000 MSME loan applications
• Improved system availability by 18%
• Enhanced loan disbursement efficiency by 6x
• Integrated existing SAAS solutions for a seamless customer experience
• Facilitated comprehensive onboarding of all business functions to the new core banking system

This transformation modernized the client’s operations and significantly improved service delivery to Indonesia’s MSME sector.

Revolutionized Paytm’s data infrastructure by transitioning from unreliable on-premises big data systems to a cloud-based solution, addressing challenges with their single daily batch process for over 4,000 tables.

• Engineered robust data pipelines to AWS EMR and S3
• Implemented an intelligent orchestration system for optimized data ingestion
• Established real-time streaming infrastructure
• Developed comprehensive data visualization dashboards
• Reduced data storage costs by 45%
• Slashed data landing time from 24 hours to just 2 hours
• Improved operations team efficiency 3x through real-time dashboards
• Cut data onboarding time by 92% (from 14 days to 1 day)

This transformation enabled Paytm to make faster business decisions with real-time insights while significantly reducing operational costs.