Happy: A Neo Fintech platform for your ultimate financial assistance

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

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

We began our journey, keeping in mind making banking services easier and accessible for millions of people. Our vision is to revolutionize the banking industry by providing accessible, convenient, 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 providing 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 more secure, and to building long-term relationships with our customers through excellent service and transparent pricing.

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

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

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

1. Creation of Machine Learning (ML)-based lending tools: We analyse data from technology aggregators and other sources to create tools that help in identifying individuals with propensity to default.
2. Sharing knowledge with industry: We periodically publish our analysis as industry white-papers and present our understanding during various summits and conferences.
3. Partnerships with lenders: We partner with various lenders across the country and help them leverage our data sources and analytical tools for better decisioning in lending operations.
4. Partnering with technology aggregators: We seek to enhance the value proposition of technology aggregators by enabling lending options to various entities in the channel of the technology aggregator.

3. What is your biggest USP that differentiates the company from competitors?   

Our biggest Unique Selling Points are as follows:

1. Better credit underwriting: Our in-house created Machine Learning (ML) procedures have the capability to analyse diverse sets of benchmarks such as individual economic indicators, credit history and basic demographic data to arrive at the best score for the individual. We strongly think this can benefit borrowers who have no or very limited credit history or those that are typically considered high-risk by existing lenders.
2. Detection of propensity to default: Our ML based processes have been tuned to identify patterns in varied types of datasets which may indicate the propensity of financial crime by the individual. We use this information to help our lenders prevent the issuance of such loans and protect both lenders and borrowers.
3. Faster turn-around-time: Our developed ML algorithms can process data from multiple data sources in-parallel and help to make credit decisions in close to real-time, which translates to the fact that loan approvals are much faster compared to lenders working with traditional models for lending.
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 the loan applications.
5. Hyper-personalized value propositions: Our algorithms can recommend the best loans to the applicants solely based on data by generating personalized loan terms that match the borrower’s data.

4. How do you see the trends changing in the industry in future ahead?

We think the trends in the industry may evolve and extend into the following branches:

1. Extending data sources for decisioning: Currently most algorithmic driven lending operations rely on individual economic indicators, credit history indicators and basic demographic data. We think this could extend to include other data sources, such as social media data, device data etc. for enhancing scoring models with better accuracy.
2. Better collaboration: Lenders or Fintechs or other entities in the digital lending value chain are increasingly likely to partner with companies that have similar capabilities to ours such as specialization in data analytics, and others. This partnership could help in better innovation and improve overall effectiveness and efficiency in the lending industry.
3. Improved data security and privacy: Since most of the lending operations are data intensive, we think there is a robust case for the need of air-tight data privacy and security laws and practices. We think all entities in the digital lending value chain are likely to invest more efforts in these areas to become nimble-footed to protect users’ privacy while complying with changing regulatory obligations.
4. Evolving lending ecosystem: The entire digital lending value chain is likely to expand into other areas of finance, such as wealth management, financial planning, and financial literacy to create an all-inclusive financial experience for users.
5. Focus on intuitive Data Science, Machine Learning & Artificial Intelligence: We think the various decisioning entities in the digital lending value chain must work on improving transparency in being able to explain how credit decisions are made. These steps help to build trust with borrowers and other entities in the value chain.

5. How is your company helping customers deliver relevant business outcomes through adoption of the company’s technology innovations?

When it comes to helping customers deliver relevant business outcomes we usually follow a typical product management approach as is with every product driven company. We create a hypothesis, test the hypothesis and evolve with the help of the feedback loop.

We begin by developing a deep and intuitive understanding of the customer’s pain points, needs and objectives. This intuitive understanding helps us tailor our solutions to meet those needs. For example, when we identified our target segment is probably one of the most under-served segments in banking space due to logistical issues, we started to address the logistical challenges by leveraging technological solutions for seamless value delivery. We began with already available solutions and gradually evolved to build in-house custom solutions for best value delivery to our customers.

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

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

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

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

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

Develop capabilities that provide transparency of pricing across technology aggregators to enable our customers to make informed decisions while choosing partners to provide services to their end users.

We would like to offer capabilities that supplement our customers’ top line or bottom line. The capabilities planned are those that the customers tend to use on a daily basis. Since there is going to be a definite uplift on the financial aspects, we expect continued engagement from our customers.

We would like to enable capabilities to provide reports, analytics and comparative insights on the daily operations carried out by our customers.

Enable insights on partner system uptime to enable the customers to make operational decisions