How Fincover (Fintech Company)is using AI and Machine Learning to boost financial Inclusion

New Delhi (India), October 20: The Indian lending market has witnessed a growth of 11 percent and has touched Rs. 175 trillion in FY 2022. The disbursement is further expected to grow and reach Rs. 275 Trillion by FY26. The mushrooming of numerous Direct Selling Agents (DSAs) and Fintech Startups have contributed to this staggering growth. Fincover, a fintech startup based in Chennai, has quickly emerged as one of the DSAs in the Personal Loan space with their extensive use of technologies like Artificial Intelligence (AI) and Machine Learning (ML), and Data analytics within the digital lending ecosystem. 

Depsite the numerous increase in the number of private banks and NBFCs over the years, the demand for credit continues to be high, especially within the underserved segments. The availability of credit continues to be a pressing challenge for this particular segment. It is where Fincover comes into play as a Loan aggregator site. The emergence of numerous Fintech startups in the past 2 to 3 years who acted as loan aggregator, has bridged the gap. These Fintech startups are using MI, AI, and data analyticis to serve the underserved segment instead of just relying on the collateral.

 Leveraging AI & MI for their Loan aggregating operations

Launched in the year 2020 in the middle of the Pandemic, the journey of Fincover has been inspiring. Naresh Rajaram, Co-Founder of says “The main reason for the spike in the number of fintech startups is because of the huge gap left by traditional lenders. Our objective is to make sure that people from the underserved and underprivileged sections of the society get quality access to financial resources. And we are leveraging the power of technology to ensure the lending process is effective and seamless.”

While the idea was promising, they faced stiff competition from established banks and other fintech players.

Mr. Gurumoorthy, Founder of explains “Started during the middle of pandemic we’ve encountered several challenges on their path to growth aside from logical problems.  including:

Customer Acquisition: Acquiring new customers in the crowded fintech space was a daunting task.

Risk Management: Assessing the creditworthiness of customers, especially for lending services, was complex and time-consuming.

Moreover, the loan market was saturated, with numerous established players. Additionally, the startup had limited resources and couldn’t afford a large team to manually sift through countless loan products to help users find the best match. That’s when Naresh, turned to AI

Fraud Prevention: Keeping transactions secure from fraudsters was a top priority.

Personalization: Offering personalized financial advice and product recommendations was necessary for customer retention”

Integrating Artificial Intelligence (AI) and Machine Learning (ML)

Mr.Naresh emphazised on how extensive usage of technologies like a well-integrated AI, Automation, and MI models are the need of the hour when it comes to making the lending process more effective. The Lending process involves a series of activities before disbursal which determines the eligibility of the lender. Here’s how Fincover has incorporated AI and MI for various loan document process such as

Automated Data extraction: AI infused algorithms extracts relevant data from government authorized documents like Aadhar cards, PAN Cards, Bank cards, and income proofs. The Optical Character Recognition (OCR) and Natural language processing (NLP), can extract information like Name, address, and financial data eliminating the need for manually entering information each time

Document verification: Integration of AI in document verification has brought accuracy, increased efficiency, and enhanced security. AI can empower document verification for loans, benefiting both lenders and borrowers. 

Risk Management: Their ML models analyzed a wide range of factors, including credit scores, employment history, income, and more, to assess the risk associated with each loan application

Fraud Detection: AI infused verification process can detect anomalies and phish out fraud application. By comparing the documents submitted with historical data, AI powered system can show red flags; identify any discrepancies thus preventing the chances of fraud before it occurs. 

Credit Scoring: AI-driven credit scoring models assessed customer creditworthiness more accurately by considering a broader range of data points, such as social media activity and online behavior.

Results of AI & MI deployment

The incorporation of AI and ML had a transformative impact on their business operations. The results started to show weeks within the deployment

Rapid Customer Growth: Fincover saw a significant increase in customer acquisition, thanks to improved targeting and personalized marketing.

Reduced Risk and Fraud: The enhanced risk assessment and fraud detection systems reduced financial risks and improved customer trust.

Enhanced Customer Experience: Personalized recommendations and real-time support through chatbots led to higher customer satisfaction and retention.

Data-Driven Decision Making: AI and ML provided Fincover with valuable insights into customer behavior and market trends, enabling data-driven decision making

Mapping Loans: The Loan aggregator startup used AI algorithms to gather and analyze vast amounts of data related to loan products, interest rates, and borrower profiles. These AI algorithms were designed to identify the most suitable loan products for individual borrowers based on their unique financial situations and preferences. Customers were mapped to better products tailored to their profile and hence the loan distribution process became simpler

Fincover has not just kept pace but has led the charge by leveraging AI and ML, growing each day. As Naresh and Gurumoorthy look ahead, they know that their journey is far from over, and they remain committed to enhancing the financial well-being of individuals and businesses worldwide.

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