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Deep Dive: How Lendingkart Deploys Data To Increase Geographical Footprint

Deep Dive: How Lendingkart Deploys Data To Increase Geographical Footprint


When it comes to starting or growing a business, one of the major hurdles is the capital or the seed money. Over the years, small and medium-sized businesses have faced challenges in having access to sufficient and timely credit. Now, the MSME sector is considered to be indispensable with India pegged at becoming a $5 trillion economy by 2025. 

One such company that has stepped in to fill this void is Lendingkart, a non-deposit taking NBFC providing working capital loans and business loans to MSMEs across India. In order to know more about the company, we got in touch with Harshvardhan Lunia, Co-founder and CEO, Lendingkart and Saket Anand, Chief Analytics Officer, Lendingkart.



Lendingkart’s Track Record

Over the last five years, Lendingkart has grown at a healthy pace. Its revenue and loan books have been growing approximately 3 times year-on-year. From disbursing loans in a few locations to creating a strong SME customer base across 1,300 cities and towns in the country, Lendingkart has come a long way.

The company has catered to 64,000+ unique businesses and today, it has evaluated around 6 lakh applications and disbursed more than 76,000 loans. Given that a large chunk of its borrowers come from the unorganised sector, with little or no formal credit history, the company has identified alternate sources of evaluating their creditworthiness.

“We have identified various proxies for credit comfort to determine a customer’s intent to pay back a loan, the quality of his product/service, the financial health of his business, and ability to survive with competition etc. Hence, the application process is simple and requires minimal documentation,” said Lunia.

Today, on average, the company receives around 7,000 leads and 1,000-1,200 applications out of which it is able to sanction around 300 loans on a daily basis. And talking about the revenue, Lunia said, “While we had revenue growth of 168% for FY 2017-18, our last year’s revenue growth rate (2018-19) was more than 200%. Our book outstanding grew by 2.9x in 2019 alone.”

The Tech Stack

Talking about the tech stack at Lendingkart, Lunia said that the company uses microservices architecture for scalability. “Using the right tool for the right job is very important, and that is how it works here,” said Anand.

For example, Lendingkart uses MongoDB as a database for cases when the number of fields is not fixed and to store data that are single time writes and where the speed of writing data trumps transactional sanctity. Whereas, it uses MySQL in case of transactional databases such as to track the application process and for analytics. And for the data warehouse, it uses redshift. Moreover, various microservices talk to each other through Kafka to keep the statuses in sync.

Coming to the backend, the tech stack is mostly on Java using Spring Boot framework, while it uses Python for data engineering and analytics. “We also make use of widespread front-end technologies such as – HTML and Angular.js. The entire tech stack is hosted on AWS and leverages AWS infrastructure for Reliability, Availability and scalability,” said Anand.

The Role Of Data And AI

When it comes to making the best use of data science, the company has done it extensively with a team of 150 individuals. One of the best examples is that its customer origination is completely digital, which is further complemented by a machine learning-based credit decisioning. The data it collects from customers is run through its algorithms, extracting more than 8,500 data points. This helps the company discern the intent and ability of the customer to repay the loan, further aiding its loan sanctioning process.

Lendingkart’s systems also have the capability to crunch non-traditional data such as GST data, mobile data, product interaction data, social data for the purpose of credit evaluation, quality lead scoring, and product interaction, among others.

How Lendingkart uses data to reach out to the right people:

The company works out with the right customer profiles to target based on the borrowers it has already reached. Its ML-based customer segmentation model takes into account variables such as demographic data, CIBIL parameters, loan performance on its books and production interaction data to figure out the good customer profiles. “Using this segmentation, we optimise our marketing campaigns to target the right people and significantly bring down the cost of customer acquisition,” Anand explains.

See Also

How Lendingkart uses AI as a tool:

  • Lendingkart’s systems have a training capability — they guide the fulfilment team on the optimal number of interactions that need to be done in order to successfully close the leads. That’s why they are also looking to deploy chatbots and voice bots
  • AI is also used to predict accounts that are likely to go delinquent in advance.
  • It has also been used to build state-of-the-art data infrastructure, which helps capture every interaction with the customer: call logs, call transcripts, SMS, email, product interaction variables and social data, in addition to traditional LOS and LMS data.
  • The company is also working on an NLP-based system that can convert SMS data into credit variables, and also plan to use similar techniques to convert voice transcripts from calls into decisioning variables.

The Hiring Phase

Being a technology-driven company, Lendingkart gauges the potential of the candidates, over experience or qualifications. “A problem-solving attitude, with a penchant for learning, draw great dividends in such situations,” said Lunia.

To fit into the team Lendingkart, a candidate must have a learning attitude, investigating thought process, aspirations mapping to a dynamic environment, engineering approach and skills/competencies. Furthermore, the company also believes in training the candidates to become the best. “We believe that the right grooming and mentorship at an early stage in career provides the much needed optimal efficiency and productivity,” Lunia added.

The Future Direction

Lendingkart believes that its biggest strength is its ability to acquire and service MSME customers at the remotest places in India using its digital reach. Also, its technological and analytical capabilities built in-house have helped develop a robust digital origination system and automated credit decisioning, distinguishing us from the rest in the market.

Looking into the future, all set to double the book size in the coming year and continue to grow existing lines of business to cater to the different needs for financial inclusion, Lendingkart will be venturing beyond our current offerings.

“Our goal is to enable financial inclusion of the larger MSME community, delivering financial products and services to them in a cost-effective and timely manner. So those aspiring entrepreneurs and MSMEs with little or no credit history can apply for loans through a simplified, digital application process,” said Lunia in conclusion.


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