A Neo Fintech platform for your ultimate financial assistance

CXOTi day has engaged in an exclusive interview with Mr. Kanti Bejjavarapu, Head – Product Strategy, Happy – A Neo Fintech Platform

1. With what mission and goals was the company set up? In short, tell us about your journey since inception?

We began our journey with the vision of making banking easier and more accessible to millions of people. Our vision is to revolutionize the banking industry by offering accessible, practical and transparent financial services to everyone, regardless of their location or financial situation.

Our mission is to make banking easier and more convenient for our customers by offering a wide range of financial products and services through a simple and intuitive digital platform. We are committed to using technology to make banking faster, cheaper and safer, and to building long-term relationships with our customers through excellent service and transparent pricing.

Happy is a lending service provider that partners with technology aggregators, lenders and other entities in the digital lending value chain to offer accessible banking, financial and insurance services to its customers.

2. Tell us how your company is contributing to the AI/Alternate Data Analytics industry in the country?

We are aware of how advanced technology is reshaping industries and the nation at large. Needless to say, we also contribute to the same. Happy contributes to the AI/Alternate Data Analytics industry in the country in the following ways:

1. Creation of Machine Learning (ML)-based lending tools: We analyze data from technology aggregators and other sources to create tools that help identify individuals with a propensity to default.
2. Sharing knowledge with industry: We regularly publish our analysis as industry white papers and present our understanding at various summits and conferences.
3. Cooperation with lenders: We work with various lenders across the country and help them utilize our data sources and analysis tools for better decisions in lending operations.
4. Cooperation with technology aggregators: We seek to improve the value proposition for technology aggregators by enabling lending options to various units in the technology aggregator’s channel.

3. What is your biggest USP that sets the company apart from the competition?

Our biggest unique selling points are as follows:

1. Better credit guarantee: Our proprietary machine learning (ML) procedures have the ability to analyze various sets of benchmarks such as individual financial indicators, credit history and basic demographic data to arrive at the best score for the individual. We strongly believe that this can benefit borrowers who have no or very limited credit history or those who are typically considered high risk by existing lenders.
2. Demonstration of propensity to default: Our ML-based processes are tuned to identify patterns in various types of data sets that may indicate the individual’s propensity for financial crime. We use this information to help our lenders prevent the issuance of such loans and protect both lenders and borrowers.
3. Faster processing time: Our developed ML algorithms can process data from multiple data sources in parallel and help make credit decisions in near real time, which translates to the fact that loan approval is much faster compared to lenders working with traditional lending models.
4. Seamless experience: Our tools are enabled with intuitive interfaces and capabilities that make it easy for borrowers to manage our loan proposals and lenders to manage their loan applications.
5. Hyper-Personalized Value Proposition: Our algorithms can recommend the best loans to applicants based solely on data by generating personalized loan terms that match the borrower’s data.

4. How do you see trends changing in the industry going forward?

We believe that the trends in the industry can develop and extend to the following branches:

1. Extending data sources for decisions: Currently, most algorithmically driven lending operations rely on individual financial indicators, credit history indicators and basic demographic data. We believe this can be extended to include other data sources such as social media data, device data, etc. to improve scoring models with better accuracy.
2. Better collaboration: Lenders or Fintechs or other entities in the digital lending value chain will increasingly partner with companies that have similar capabilities to us, such as specialization in data analytics and others. This partnership can contribute to better innovation and improve the overall efficiency and effectiveness of the lending industry.
3. Improved data security and privacy: Since most lending operations are data-intensive, we believe there is a solid basis for the need for airtight privacy and security laws and practices. We believe that all entities in the digital lending value chain are likely to invest more effort in these areas to become adept at protecting user privacy while complying with changing regulatory obligations.
4. Lending ecosystem in development: The entire digital lending value chain is likely to expand into other financial areas, such as wealth management, financial planning and financial literacy to create an all-encompassing financial experience for users.
5. Focus on intuitive data science, machine learning and artificial intelligence: We believe that the various decision-making units in the value chain for digital lending must work to improve transparency in being able to explain how credit decisions are made. These steps help build trust with borrowers and other units in the value chain.

5. How does your company help customers deliver relevant business results by adopting the company’s technology innovations?

When it comes to helping clients deliver relevant business results, we typically follow a typical product management approach that is common with all product-driven companies. We create a hypothesis, test the hypothesis and develop using the feedback loop.

We begin by developing a deep and intuitive understanding of the customer’s pain points, needs and goals. This intuitive understanding helps us tailor our solutions to meet these needs. For example, when we identified that the target segment is likely to be one of the most underserved segments in the banking space due to logistical issues, we started addressing the logistical challenges by leveraging technology solutions for seamless value delivery. We started with already available solutions and gradually developed to build in-house customized solutions for the best possible value for our customers.

We have created a large set of metrics that help us and our partners in the digital lending value chain to monitor key KPIs in the portfolio. This helps us to react with minimal latency to any deviations in the portfolio’s performance.

We also use the metrics to monitor the performance of our products and their impact on our customer’s business results and adjust as and when we observe a divergent trend.

Another motto of our company is to continuously innovate to meet the evolving need and statement of our target segment and try to stay ahead of the competition.

6. What are your plans going forward, how do you plan to add value to your community in the next year?

We plan to reduce our reliance on partnerships for onboarding our customers. Instead, we will focus on creating hooks and engagement processes that attract customers from our target segment. Some of the ways we want to onboard and engage with them are as follows:

1. Partners in the value chain for digital lending
   1. Facilitate seamless integration to access value from our platform
   2. Develop personalized lending models for various combinations of partners in the digital lending value chain
   3. Enable customized reporting, analytics and insights for partners
   4. Enable heartbeat monitoring of partner platforms and provide performance analytics
2. Customers

Develop features that provide price transparency across technology aggregators to enable our customers to make informed decisions while selecting partners to provide services to their end users.

We want to offer capacities that supplement our customers’ top line or bottom line. The options planned are those that customers tend to use on a daily basis. Since there will be a clear boost in the financial aspects, we expect continued commitment from our customers.

We want to make it possible to provide reports, analyzes and comparative insight into the daily operations carried out by our customers.

Enable insight into partner system uptime to enable customers to make operational decisions