AI Lending Platforms and the Future of Digital Credit in India

India’s lending market is growing exponentially due to an expanding middle class, advancements in technology, and a surge in non-traditional distribution channels. The competition is rapidly heating up as a surge of digital-only lenders, fintech firms, and non-banking financial companies fight for the borrower’s attention. The borrower, in turn, demands no less than a seamless lending experience.

Indian banks and lenders are reducing the loan approval process time and enhancing borrower experience through AI lending platforms. Lenders can offer frictionless lending processes at scale with automation, data, and artificial intelligence (AI). The benefits of a unified AI-driven loan origination platform include accelerated decision-making, reduced costs, and empowering underwriters with comprehensive data and automation. 

Banks and financial institutions are increasing their investments in advanced technologies such as AI to speed up loan processing time and combat fraud. This article discusses how an AI lending platform is a must-have in the lending industry and the challenges in the adoption process. 

AI Lending Platforms: Explained

The Global AI in lending market is expected to be worth around USD 58 billion by 2033, growing at a CAGR of 23.5% during the forecast period from 2024 to 2033. Several factors, such as automation of lending solutions, reduced operational costs, streamlined workflows, and a data-driven approach, are driving the adoption of AI lending platforms in financial services. 

For instance, an AI lending platform automates tasks such as scanning borrowers’ documents, analyzing spending patterns and creditworthiness, and detecting fraud and manipulations. This AI-powered digital lending platform offers several benefits for lending businesses:

• Reduces lengthy loan processing and approval time 
• Detects fraud efficiently 
• Reduces human errors

AI and machine learning (ML) algorithms analyze vast amounts of loan applications, investigate income and identity data, and detect tampering. This aids lending decisions and enhances precision in loan approval processes. The result? Borrowers and customers will experience a more seamless and accurate loan approval process, improving customer satisfaction and retention.

Lending Made Easy with an AI Lending Platform

AI lending platforms have reformed the lending ecosystem by enabling credit decisions to be more innovative, quicker, and more targeted on customer experience than ever. The following points highlight the major ways an AI lending platform is modifying modern credit evaluation and risk management.

• Machine Learning (ML) and Deep Learning

  AI lending platforms have algorithms that analyze vast amounts of data, such as credit card history, user behavior, and social media signals, to detect patterns that traditional underwriting models might miss. By learning from historical data, these AI models refine their risk predictions over time, offering more accurate and personalized lending decisions.

• Natural Language Processing (NLP)

  NLP helps AI lending platforms interpret unstructured data like customer support chats, social media posts, or even bank transaction descriptions. This analysis provides qualitative insights into a user’s financial behavior or stress, adding to the usual numbers and data. For example, an AI lending platform can assess a customer’s online interactions to assess their creditworthiness better, going beyond numerical scores to include behavioral signals.

• Real-Time Decision Engines

  AI-powered decision engines process data at lightning speed, often within milliseconds. This enables instant credit decisions, which is an essential aspect of embedded lending. For example, AI lending platforms can pull real-time data via APIs and render a decision as quickly as a user clicks a button. The use of cloud or edge computing further reduces latency, ensuring seamless and immediate loan approvals.

• Adaptive Learning and Personalization

  AI lending platforms continuously learn from user data to actively adjust credit terms based on individual behaviors. For instance, if a customer consistently makes on-time payments for a “buy now, pay later” service, AI can increase their credit limit or offer a more flexible repayment plan. This personalization ensures that credit offers evolve with the borrower’s financial situation.

• Edge Case Handling and Risk Mitigation

  AI lending platforms don’t just approve loans but also flag potential risks. By analyzing multiple data signals, AI can detect anomalies, such as unusual location changes or spending patterns indicative of fraud. It can then adjust lending criteria or request additional verification. For instance, PayPal’s BNPL uses machine learning to detect fraud by identifying unusual device usage or multiple installment plans, ensuring safer credit extensions.

• Leveraging Alternative and Real-Time Data

  AI lending platforms go beyond standard credit scores by including alternative data like mobile usage, GPS location, or browser metadata. This allows for a more nuanced, real-time evaluation of a borrower’s risk. AI can even account for macroeconomic trends such as regional unemployment rates to anticipate default risks, ensuring the latest available data inform credit decisions. 

  In short, AI uses many types of information and finds patterns quickly to help lenders make fast, personal loan decisions. This allows banks give loans safely, while also making it easier for more people, including those without long credit histories, to get the money they need.

Use-Cases of AI in Lending

AI has been improving the capabilities of various workflows and systems across the finance industry. Specifically in lending, its use extends to credit scoring, loan approval, fraud detection, collection management, and more.

AI in Credit Scoring

A credit score is evaluated by analyzing borrowing history, total income, work experience, and user behavior. The AI lending platform assesses the user’s borrowing and generates a scorecard. 

However, the traditional method of calculating the credit score has its fair share of drawbacks. 

• To begin with, a credit score is based on the customer’s credit history. 
• New customers often need help securing loans even when they are creditworthy. 
• Next, a credit score is inapplicable for new loan products created for the younger customer base. 
• It makes the financial institution short-sighted and increases the risk of making errors in their credit estimation. 

Credit scoring models powered by machine learning and credit risk analysis monitor more relevant metrics, such as current income level, potential earnings, and employment opportunities, to measure new customers’ creditworthiness. 

In short, the AI loan credit scoring system increases financial inclusion by offering credit access to a previously unattended group of people. 

AI in Loan Approval

The traditional loan approval process is exhaustive and resource-consuming. The loan officer collects documents, such as government-issued IDs, bank statements, employment proofs, salary slips, property papers, and other relevant documents. 

After compiling all the documents, the critical information is manually verified. After verification, getting the manager’s final approval could take days, if not weeks.

AI-powered document processing software compiles specific information from the relevant documents at scale. It also checks the authenticity of submitted documents and routes applications through the respective departments for approval. The result is quicker loan approval times and improved operational efficiency.

3. AI in Fraud Detection

Unauthorized transactions and phishing scams are the leading reasons for increased fraud attacks. However, identity theft and misrepresentation of financial statements are the root causes of loan fraud. AI lending platform tackles fraud detection with four solutions: fraud scores, fraud investigation, purchase profiles, and KYC. 

Bank Fraud Detection 

Faking transactions and identities has become easy today. Deloitte’s Center for Financial Services report shows that Generative AI is expected to increase the threat of fraud, which could cost banks and customers as much as USD 40 billion by 2027. 

AI-powered document processing software helps banks reduce the workload of banking staff by automating document analysis. AI algorithms effectively analyze transactions and payments to identify suspicious activities and alert respective personnel to investigate further. 

Fraud scores‍

Lenders use fraud scores to measure the legitimacy of a transaction. NLP and ML analyze data such as past transactions, fraud accidents, and risk parameters that the lender sets, and provide a fraud score. The current transaction is flagged for review or canceled, depending on the severity of the fraud score.

Fraud investigations

Artificial intelligence can scan hundreds of thousands of loan documents in real-time and give a list of flagged loan applications that a loan officer can investigate further. 

This saves digital lending platforms thousands of man-hours that would have otherwise been spent finding fraudulent loan documents. 

Purchase profiles‍

The system creates multiple purchase profiles by understanding user behavior, allowing financial organizations to segment customers into profiles and monitor transactions. 

KYC

AI-based KYC helps verify IDs and match fingerprints before authorizing any loans. 

4. AI in Collection Management 

Financial institutions analyze the bank transactions of at-risk accounts to identify customers who might default on loan repayment. AI lending platforms enable lenders to take proactive measures and create personalized loan collection strategies for these customers.

For example, lenders can use targeted communications, payment reminders, and customized repayment plans for each borrower. They can set up chatbots to automate customer interactions and provide 24/7 support. On top of this, AI phone agents can conduct personalized outreach calls, efficiently addressing borrower concerns and facilitating timely repayments.

Together, these solutions reduce default rates, improve collection management, and improve customer satisfaction levels through loan management automation. 

How can Digikred help?

Lenders and underwriters can leverage document processing AI to become more agile, efficient, and customer-centric. They can offer loans to deserving candidates, combat fraud, and prevent financial losses. However, understanding machine learning solutions, model deployment issues, and costs are some challenges stopping lending institutions from exploring the full potential of AI document processing.

Digikred is an automated lending platform that overcomes these limitations with its easy-to-use interface and quick deployment capabilities. The platform analyzes the borrower’s financial documents and historical data to evaluate credit history, income, employment stability, debt-to-income ratio, and market conditions to flag potential risks. Therefore, underwriters and lenders only need to review exceptions, which significantly reduces manual errors, enhances customer experience, and improves efficiency. 

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FAQs about AI Lending Platforms

• How can AI be used in lending?

  AI-based lending platforms leverage machine learning algorithms to streamline the lending process. AI-based lending involves using data analytics to evaluate creditworthiness, risk, and other factors that determine the probability of a borrower defaulting on their loan.

• What is a loan origination system (LOS)?

  A loan origination system is a technology that helps financial institutions generate loan documentation in a way that complies with regulatory and internal requirements. Banks and credit unions usually install loan origination software on their local servers or access it through a cloud-based platform.

• What is the loan origination process?

  Loan origination is the process by which a borrower applies for a new loan, and a lender processes that application. Origination generally includes all the steps from taking a loan application up to disbursal of funds, or declining the application.

• What are digital lending platforms?

  Digital Lending is a remote and automated lending process, mainly by use of seamless digital technologies for customer acquisition, credit assessment, loan approval, disbursement, recovery, and associated customer service.

• Is digital lending legal in India?

  The Reserve Bank of India (RBI) has published a comprehensive list of legal digital lending apps (DLAs) on its website, allowing consumers to verify whether a particular app is associated with a regulated entity.