Digital lending

What is digital lending?

Digital lending is the process of offering and managing loans through digital platforms—without the need for face-to-face interactions, bank visits, neck-breaking paperwork and documentation. In simple terms, digital lending denotes the process where everything happens online—applying, verifying, validating, and disbursing, and even repaying the loan. The availability and advancements of digital payment networks, strengthened by AI, real-time data integrations, and automation platforms, has made this happen. Digital lending examples would be instant personal loans through mobile apps, buy now pay later, and digital MSME.

Digital lending – who is it for?

Digital lending is proven effective for all types of lending institutions. Some sectors and companies that benefit from digital lending are traditional banks, FinTech startups, eCommerce platforms (BNPL), and P2P lending platforms.

Banks: Even though the lending processes have become more efficient, banks still could benefit from digital lending as they could compete with agile FinTech institutions. Many Indian banks like SBI, ICICI Insta loan, HDFC Bank, etc., have already implemented digital lending.

NBFCs: Non-banking lending and financial institutions have streamlined digital lending successfully, tapping into a wider audience who can’t receive help from traditional lenders. Some examples in Indian financial market including Bajaj Finserv, Tata Capital, etc.

eCommerce companies: Many businesses have enabled buy now pay later schemes, which is like digital lending. They allow customers to buy products without paying and pay later without or with a negligible interest. Some examples of BNPL in India is Amazon Pay Later, PhonePe Credit, etc.

Fintechs: Many fintech institutions and neo lenders like MoneyTap, KreditBee are leveraging digital lending techniques to power their app-based lending with instant borrowing options.

How digital lending works?

Here’s how digital lending platforms organize everything from loan applications to verification to repayment with the help of technology.

Initial loan application

It starts with users applying for loan through an online app or website: filling out basic details, loan amount, repayment terms, purpose, and more. There will be better guidance available through loan eligibility calculators.

Digital KYC

Then comes the verification process. The platform collects user information and auto-validates details. It pulls and verifies any document uploaded – employment proof, bank statements, credit bills, and more.

Credit assessment through AI

Traditionally, banks and lenders used to check credit histories, that too manually. But digital lending conducts instant credit assessments with more accuracy. This is because digital lenders tap on more data like bank transaction patterns, purchase behavior, social media data, and more. With these data, AI/ML models could predict credit risk & borrower’s creditworthiness – deciding if they should be given a loan or not.

Loan offer & disbursement

The platform comes up with a loan offer (interest terms, repayment term, EMI breakdown, and more) and allows users to sign an e-agreement and collect the amount directly through their bank account.

Repayment

Users can track repayment patterns through the lending app and make payments through the same. Any late payments, they would receive alerts on the same.

Why is digital lending gaining popularity?

• It is convenient. Both borrowers and lenders find it easy to deal with.
• Fast decision making and loan processing. Wait times have been brought down from weeks to days or even hours, thanks to AI-powered credit risk analysis, automation, OCR extraction, etc.
• Lenders could target younger generations that’s always digital first. This could be a great opportunity for new-to-credit users too.
• Fewer resources required, which means lower serving costs.

How does ML/AI make digital lending better?

AI/ML models are used in digital lending to automate loan processing & data extraction, evaluate risks, prevent fraud, and facilitate smoother workflows.

Credit scoring & risk

Credit risk analysis checks buyers’ creditworthiness – whether they can repay on time based on credit history, spending patterns, and other behavioral data. It could also segment users based on risk scores, offering personalized interest rates, payment plans, etc.

Cloud-based systems

Cloud systems act as the foundation and the central brain, automating and managing loan applications, applicant data, and more. With cloud-based systems, it’s easier to store millions of data points and scale efficiently. It’s the integration of data from different systems (accounting systems, core banking systems, credit portals, etc.) behind that allow real-time data movement, letting systems talk to one another.

Data extraction using OCR

Identity verification and data extraction from documents could be difficult if done manually. But, with AI-based OCR, cross-validating documents and extracting data from them can be done easily.

Fraud detection

AI/ML fraud detection solutions can play a huge role detecting fraud in real time, sensing anomolous patterns and preventing huge money loss for lenders.

Risks in digital lending

Digital lending, if not done right, could lead to many risks too—for both lenders and borrowers. Some possible risks with digital lending:

• AI/ML model bias
• Data privacy and security risks and threats
• Debt traps
• Fraud/identity theft
• Third-party data sharing risks

Financial institutions must be wary of the possible risks, assess the situation and data landscape carefully, and avoid bias risks by building explainable AI solutions.