Interview with Mr Manish Khera, Founder & MD, Happy- A Neo Fintech Platform
Manish Khera is an entrepreneur, advisor, and investor. Manish is a founder of Happy, a financial service provider & Neo Fintech Platform. Currently he serves the company as its CEO.
Manish Khera holds a bachelor’s degree in electrical from Delhi College of Engineering and received his master of business administration degree in finance, general at Faculty of Management Studies, University of Delhi.
Can you tell us about your background and what inspired you to start a digital lending platform?
Manish Khera: We are a digital lending fintech service provider and have partnered with technology aggregators, lenders and other entities in the digital lending value chain to provide accessible banking, financial and insurance services to customers.
The thought behind Happy was to provide easy credit to India’s large base of underserved and new-to-credit MSMEs, who are not otherwise covered by traditional lenders, banks & NBFCs.
Kindly brief us about Happy Loans, its specialization, and the services that it offers
Manish Khera: Happy is the brainchild of Mr. Manish Khera (Founder & MD) and has Ms. Shweta Aprameya as the co-founder.
Happy Loans provides easy banking that is more convenient for the customer with multiple options of financial products and services through a simple and intuitive digital platform.
Happy is a fast-growing neo-fintech platform that empowers MSMEs in India with quick and easy loans.
The business has grabbed the opportunity to disrupt the new era of digitalization in financial services and connects lenders and borrowers via its fully digital platform.
With their 100% digital lending process, they maintain and ensure trust and value between the participants.
The lending service provider has partnered with technology aggregators, lenders, and other entities in order to provide seamless financial services to its customers.
Happy has been able to reimagine credit and assist MSMEs in their better future by promoting market trust and confidence.
It has been contributing to the empowerment of micro, small, and medium enterprises by providing them access to finances, especially when they do not have a long credit history.
They have been bridging the credit gap which has led to the growth and development of several MSMEs.
The efforts and contributions have been in line with boosting the Indian economy, which effectively makes Happy an “impact tech”.
The firm currently has its operational corporate offices in Mumbai and Gurugram. Since its inception in 2016, the company has been committed to serving India’s underserved and new-to-credit MSMEs.
The founder had an overarching goal of catering to the needs of these businesses, which are not covered by conventional banks or other lenders.
Hence, Happy came into being and is revolutionising the fintech sector while targeting the multi-billion dollar credit gap among India’s MSMEs.
It has over 100 enthusiastic employees who are dedicatedly serving their purpose with a clear goal that resonates with the enterprise’s.
Happy earns its revenue by originating, underwriting, servicing, and collecting loans. Its in-house developed machine learning (ML) procedures have given it an advantage over competitors, as they can ensure better credit underwriting and detect default propensity.
Furthermore, with its ML algorithms, it can provide faster turn-around time (TAT), which means quicker transactions.
The platform also has intuitive interfaces that facilitate a seamless experience for both borrowers and lenders.
The algorithms can further recommend the best loans to the applicants solely based on data by generating personalised loan terms that match the borrower’s data.
The firm has achieved several awards and recognitions, including “2019 Inclusive Fintech 50” by Metlife Foundation, VISA, Accion, and the World Bank Group; “The best AI startup of the year 2018” by ET Now and Entrepreneur India.
Furthermore, Times Now has also recognised Happy as “the most innovative startup of the year 2018.” Enabled by data and technology, Happy now provides its seamless services at 550+ locations.
Furthermore, the company has already facilitated over 5 lakh loans and disbursed loans worth Rs 200 crore via its platform.
Happy seeks to broaden its reach in order to provide coverage and credit to a larger base of MSMEs, empowering them to grow their businesses.
We are specialized in using technology to make banking faster, cheaper, and more secure and to build long-term relationships with our customers through excellent service and transparent pricing.
How do you ensure the security and privacy of customer data on your platform?
Manish Khera: In order to ensure maximum security and data protection, Happy takes an end-to-end approach to address the data security and privacy pillar along the operational framework.
It is important to protect individual data and not just the digital infrastructure of the firm, and hence it is potent to implement data security and privacy by design.
Along with designing the protection measures, it is equally important for platforms to adhere to the regulatory requirements and guidelines of the government.
Data plays a central role in the financial industry and with discussed threats from third-party integrations.
Hence, we have integrated layers of security to tackle potential threats and ensure data protection. To start with, All the data is stored in secured access on the AWS cloud to improve the ability to meet core security and compliance requirements, such as data locality, protection, and confidentiality.
Secondly, we have implemented user and table-level access control for better and smooth functioning. Lastly, we have an approval mechanism in place to access the data that makes it easy for customers and difficult for outsiders.
Can you walk us through the process of applying for a loan through your platform?
Manish Khera: The loan disbursement process followed by our platform is quick, easy and with minimal documentation, making it easier for our customers to get the right help within a very short time.
Firstly, We generate user-specific application links followed by customer authentication using registered mobile numbers along with the updation of personal and business details.
The next steps involve authentication of KYC through Digilocker and Enach registration. After these steps, our team takes the lead and shares terms and conditions and requirements for the customer’s acceptance.
Once the customer agrees to the platform’s terms, we follow KYC validation and lender approval from our end. The final steps then involve a pre-disbursement call and disbursement of the final loan.
What are the criteria you use to determine loan eligibility and interest rates?
Manish Khera: To ensure no loopholes, Happy uses a total of 24 parameters as loan criteria. The criteria largely includes digital transaction data that we get from channel partners.
How do you assess the creditworthiness of borrowers without traditional credit history?
Manish Khera: We use digital transactions data to underwrite the customer. Also, we pull some additional details of the customer using external integration to support the underwriting decision.
What measures do you have in place to prevent fraudulent activity on your platform?
Manish Khera: We have simple yet effective measures to prevent any kind of fraudulent activity on our platform.
- Authentication of customer through OTP
- Authentication of KYC through Digilocker
- Authentication of borrower account through penny drop
- Pre disbursement calling
- Customer live location and digital signature
How do you plan to stay competitive in the digital lending market, and what are your future growth plans?
Manish Khera: Better credit underwriting: Our in-house created Machine Learning (ML) procedures have the capability to analyse diverse sets of indicators such as individual economic indicators, credit history indicators 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.
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 the lenders and borrowers.
Faster turn-around-time: Our developed ML algorithms can process data from multiple data sources in parallel and help 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 lending models.
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 oan applications.
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.
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