Harnessing AI to Revolutionize Wealth Management: A Conversation with Dhruv Pathak, CTO, INDmoney
In an era where technology is reshaping industries at an unprecedented pace, wealth management is no exception. INDmoney, a leading player in the wealth-tech space, is at the forefront of this transformation, leveraging cutting-edge technologies like Artificial Intelligence (AI) to redefine the way users manage their wealth.
In this exclusive interaction, Dhruv Pathak, Chief Technology Officer at INDmoney, delves into how the company is leveraging AI to enhance user experiences, optimize infrastructure scalability, and address the unique technological challenges of global investing. He also shares insights on the company’s robust cybersecurity frameworks, API-driven architecture, and how user feedback plays a pivotal role in shaping INDmoney’s tech roadmap.
Some edited excerpts from the interview:
How is INDmoney leveraging AI, and how do you envision this technology reshaping the wealth management landscape?
The evolution of AI capabilities and their suited manifestation into our products is a continuous process. We use AI to enhance the user’s product experience with features like near-realtime general market insights and sentiment snippets at scale for both Indian and global markets, semantic search to provide the user with the most relevant content by understanding the meaning of their searches, and assistance in KYC process and document parsing. AI interventions in our app products, and for customer issue resolution, are also being piloted. With each iteration, we make the systems smarter, faster, and more cost-efficient, ensuring that it works flawlessly, even when dealing with multiple types of data, including real-time data.
We also utilize Custom GPTs, AI-based Copilots & IDEs for our development processes.
Multiple use cases for wealth management can be built on top of personal financial data combined with capital markets data. The use cases which needed custom implementations can now be done swiftly with AI . With AI, wealth management aspects like tax efficiency, personalized portfolio insights, and goal planning are easier implementations, as AI understands the data as well as the use case context.
How does INDmoney approach scalability to handle rapid user growth and evolving demands?
Our infrastructure is hybrid, utilizing both private data centers and the cloud. Automated scaling policies based on anticipated loads enable us to manage evolving demands while ensuring our low latency SLAs remain intact, as speed is crucial in numerous scenarios. Our approach to scaling and low latency engineering involves observability at every stage of user interaction, optimization at each component in networking & I/O , and prior load testing of our components to handle significantly higher loads than forecasted. This guarantees that our service delivery to the last mile remains high-performing for our growing user base across various cities. We continuously measure the 99th percentile performance of our systems at various stages of user activity.
Scaling with rapid user growth involves cost considerations, and we maintain a disciplined approach to fixed infrastructure costs with measurements at the active userbase level. These underlying efficiencies ensure that infrastructure investments are not directly proportional to user growth.
What are some of the technological challenges in enabling Indian users to invest in U.S. and global markets, and how has INDmoney overcome them?
Reliable and swift cross-border money movement is a complex orchestration involving multiple partner banks and payment systems. This is achieved through the accurate and secure interaction of numerous microservices and reconciliation systems.
Another challenge for Indian users is a quick and easy overview of the latest price fluctuations, including pre-market prices, global market news, and stock related information nudges and sentiments, especially for stocks and ETFs that are not mainstream. INDMoney addresses this through real-time data pipelines and AI-based processing making it easier for Indian users to make informed financial decisions.
What cybersecurity models and frameworks have INDmoney implemented to safeguard user data, particularly in the context of global investing and cross-border transactions?
Our storage and usage of user data adheres to industry best practices, tailored to INDmoney’s product design. Cybersecurity is also governed by regulatory bodies’ guidelines, Play Store and App Store policies, cybersecurity frameworks, and certifications. We ensure data encryption and tokenization, multi-factor authentication, user device binding, and app security, along with continuous threat monitoring through systems like SIEM.
Periodic cybersecurity audits, and certifications like ISO 27001 and PCI DSS are also integral to a regulated fintech business based on the services we are providing.
API-led financial services are transforming the industry. Can you elaborate on INDmoney’s API design structure and the benefits it has brought to your business?
We use a microservices-based architecture to improve modularity, manageability, scalability, and monitoring. Each API stack has a distinct role within the ecosystem. These stacks are orchestrated to deliver the necessary services to our users and partners. API gateways enable standardized monitoring, optimized network connections, and streamlined maintenance.
Our API backend also functions as the driving force behind our apps through a Backend-Driven UI architecture, allowing us to expedite feature delivery and conduct multiple experiments for an optimized user experience. For real-time applications, our API stack is enhanced by socket streaming, exemplified by our in-house price streamer, which delivers real-time stock market data for numerous stocks and derivatives to end users.
How does INDmoney integrate user feedback into its tech development cycle to continuously improve the platform?
User feedback, both positive and negative, is a catalyst in driving improvements to our platforms. We constantly monitor user feedback reaching us through multiple channels, including social media, Play Store reviews, and our customer care user tickets.
Our playbooks and processes are designed to collate high-impact user feedback and ship those features with variants, data based feedback loops on the experiments help us scale the optimal variant of the features. We also utilize AI to summarize and categorize user requests, issues, and criticisms based on their urgency and importance.
What’s your vision for the evolution of INDmoney’s technology stack over the next three years?
We will continue optimizing our low-latency engineering to power fast experiences on our products, especially trading. This will be supplemented by evolution in overall cloud infrastructure and network infrastructure on the cloud providers in terms of regions and geographic coverage, and ever increasing efficiencies in technical protocols and standards. A set of current SaaS components will move to fully in-house software engineered from scratch for multi-fold better performance and granular control.
We will keep scaling and evolving our stacks to cater to a rising user base while adhering to a sensible cost discipline.
AI is going to play a significant role in reducing operational overhead in the tech stack, automations, and pipelines. Enhancing QA automation pipelines through AI is already showing promising signs, and the cost to onboard or create such solutions will become optimal.
Technology projects and investments are also undertaken to uphold high standards of data privacy, cybersecurity, and regulatory compliance, especially in conjunction with evolving cybersecurity and cyber resilience frameworks.
What are some of the key technology projects currently in the pilot or deployment stage at INDmoney?
Recent projects include a detailed dividends calendar to track dividends on Indian and US stock investments and automated trading pattern detection on trading charts. We’re introducing even faster experiences in our trading ecosystem with single-screen interactions and scalping modes, supplemented by advanced AI and data-driven tools like automated pattern detection and news events for chart-based trading.
Tracking your overall portfolio and total net worth is also one of our salient features, so that users can make smarter decisions and see their wealth grow, all in one place. We have projects underway to enhance the coverage of tracked asset types and the accuracy and freshness of data. We’re also piloting AI interventions to supercharge product experience and customer service, with promising results.
