The article should aim to educate the audience about how Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing digital lending. It should provide a comprehensive understanding of the changes brought about by AI and ML in the lending industry, including improved risk assessment, fraud detection, and customer service.
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In recent years, financial technology or fintech have gained immense popularity as a powerful tool for promoting financial inclusion in India. Fintech innovations have revolutionized the way loans are procured and disbursed by introducing the concept of digital lending. Digital lending is a kind of loan acquisition method that eliminates the need of visiting a physical bank or financial institution by allowing people and institutions to apply for and receive loans through online platforms.
Digital lending has helped, the global alternative lending market to expand drastically, with a value of US$ 10.82 billion in 2022, and it is likely to grow at a CAGR of 2.45% to reach US$ 407.80 billion by 2027. Even in India, the lending market is growing exponentially, expanding about US$ 9 billion in 2012 to US$ 110 billion in 2019. The key reason behind the success of digital lending is leveraging data collected throughout the application and other processes, and AI helps in assessing that data.
AI and ML in digital lending can analyze the large volume of data to make informed and data-backed decision, which further aid the expansion of lending industry. Artificial Intelligence and Machine Learning improves data analysis to make the credit risk assessment more efficiently, that’s why they play an integral role in digital lending industry.
With the advent of Artificial Intelligence and Machine Learning, the Digital lending and financial industries are undergoing a massive transformation. The introduction of AI and ML in digital lending has turned the tables, and only those who know how to keep pace with changing traditions and practices can survive now. In this shifting phase, traditional lenders like banks/NBFCs need to understand how digital technologies can help them in expanding their business territories. In today’s article, we’ll see the impact of AI and ML in digital lending landscape and how effective they are in eliminating the problems faced by traditional lending institutions.
Introduce the idea of digital lending and how AI and ML are being incorporated into this sector. 3-4 paragraphs.
Many MESMs, especially micro-units, are not skilled at bookkeeping and other documentation required by Banks and formal lenders for processing the loan application and underwriting. Besides, many of them aren’t able to produce relevant collateral for financing. This inability to not provide authentic documents clearly demonstrates creditworthiness to potential lenders and becomes the biggest hindrance in obtaining formal credit.
On top of all, some MSEMs have a lack of credit and repayment history, which worsens their case and makes them more ineligible for obtaining formal credit. With no options left in their hands, these MSEMs finally move to informal moneylenders without knowing how exploitative and expensive they are. AI and ML in digital lending aim to break this vicious cycle of exploitative loan repayment.
Thanks to Artificial Intelligence and Machine Learning, global digital lending is on a fast growth track. AI and ML have become the master players in digital lending who have changed the entire game by easing the documentation process and correcting credit risk analysis. AI has data-backed credit decisions, which effectively creates user personas and identifies similar applications in the future. Let’s understand the role of AI and ML in digital lending by discussing some high-impact areas that AI and ML have influenced.
Automating loan application process – AI and ML are smart enough to assess loan applications and rule out the successful ones which are likely to be paid on time. They can help Banks and NBFCs in making more informed decisions by quickly processing customer data. Interestingly, the loan processing for MSMEs has seen drastic changes after the implementation of GST. Since the GST implementation, digital infrastructures have been used for taking proactive reforms to transform the way MSMEs are financed. With every passing second, these digital infrastructures are said to be progressing toward the next and more advanced stage of automation, which aims to make the entire process convenient and quick.
Automated underwriting – Limited credit histories are the main problem for MSMEs. AI and ML have predictive modeling, which aids customer data analysis and helps in assessing the creditworthiness of small businesses and custom tailoring the loan terms according to their needs. This makes assessing credit and managing debt easier for MSMEs. Before the advent of AI and ML in digital lending, the underwriting process had its limitations regarding data availability and therefore was done at bank branches.
Underwriting at bank branches includes manual intervention to check the data authenticity and then take credit calls. But now, the majority of data that is required for underwriting is digitally available, and all the technologically advanced banks and institutions are shifting towards the Artificial Intelligence supported automated underwriting process.
Credit scoring models – Artificial Intelligence and Machine Learning have advanced data analytics for assessing the creditworthiness of borrowers, which helps in approving more loans and offering better rates to MSMEs. The AI performs credit risk analysis by examining several data, which includes financial and non-financial data.
AI-powered financial institutions use this data to consider transaction history, cash flow, and supplier and customer relationships, which ultimately provides a wider perspective of an MSME’s creditworthiness. The traditional scoring models were particularly restricted to credit information in the past, which is not effective for providing 360-degree information on MSMEs; in contrast, the future AI-based credit scoring models are based on multiple data points such as GST, bank accounts, income tax, and many other data formats.
Personalizing lending experience – Machine Learning and Artificial Intelligence have machine learning algorithms that aid lenders in customizing their services and products depending on the needs and preferences of individual borrowers. Today, lenders use voice assistants, chatbots, and user-friendly interfaces to communicate and engage with borrowers. Customer service automation increases a personalized lending experience for MSMEs, which increases customer satisfaction and generates loyal customers. AI-based learning algorithms consider borrowers’ specific circumstances and create personalized repayment plans. It facilitates fraud detection and leads to timely loan repayment.
Customer Acquisition – There has been a dramatic change in customer buying journeys over the years, and AI can help lenders understand the behavioral footprints of customers and predict possible business outcomes based on it. The predictive analytics of AI-based digital lending tools can predict how likely a customer is willing to purchase a lending product and understand the customer’s behavior.
Derive Actionable Insights: Artificial Intelligence and Machine Learning simply analyze clickstream data, browsing, and other data to predict the intent and interest of the customer. Depending on the data, customers are categorized into must reach, requires more effort, and are not interested. Later, the categorization helps in deciding the way of approaching customers and ensuring engagement. Apart from predictive analytics and fraud detection, AI can also help in building effective marketing campaigns to create more leads and convert existing leads into customers.
Ensure Accurate Decision-making – Machine learning comes under the umbrella of Artificial Intelligence and helps in making a more practical strategy. ML uses algorithms and statistical models for real-time analysis of huge data sets. When combined and used strategically, together AI and ML can help lending institutions identify, sort, and make precise decisions based on vast research and multiple data points. Besides, ML can also figure out any anomalies in the behavior or pattern of usage after allocating credit. The decisions of ML are primarily based on the payment history, which enables it to help users make more informed financial decisions.
AI and ML in Fraud Detection – Finance and Fintech world is full of uncertainties, and fraud is one such uncertainty. Even the most precaution-taking and named fintech companies can encounter fraud cases if they don’t have a reliable fraud detection system. The AI and ML in digital lending uses multiple variables that include bureau score, behavioral pattern on a particular platform, etc. to analyze a customer before finalizing a credit decision.
With the apt use of Artificial Intelligence and Machine Learning, lenders can take down their operational expenses. AI is time savvy as it can detect and cancel redundant and fraudulent applications in no time. On top of everything, AI saves the expenses on human resources by reducing the need for human labor. AI and ML are revolutionizing the digital lending and fintech industry in many ways, and here are some possible benefits of using them.
Faster KYC – Traditional method of doing the know-your-client (KYC) process is time taking. The AI-powered chatbots speed up the process by guiding numerous customers at once. AI has not only simplified the KYC process but also made it easier by studying customer data and identifying trends in their behavior. The credit risk analysis of Artificial Intelligence helps a lot in designing loans unique to the individual needs of customers, which gives the lender access to a captive market.
Minimized Operation Costs – The impact of AI and ML in digital lending is changing the dynamics of the finance industry. Artificial Intelligence and Machine Learning are capable of minimizing operational costs by automating several manuals and redundant processes. It eliminates the need for human labor and also reduces human errors. This allows the human labor to be aligned to perform value-added tasks.
With the help of AI’s predictive analysis, lenders can accurately monitor suspicious transactions and set off response protocols. If used to their full efficiency, Artificial Intelligence and Machine Learning can successfully identify KYC frauds, BSA, AML, blocked customers, and duplicate applications, thus preventing potential losses. Furthermore, lenders can use AI for customer service automation to meet the expectation of 24*7 customer service.
Unbiased Credit-decisioning – Credit decisions done by humans are biased to some extent and vary from person to person. The decisions taken by underwriters can be flawed because he has limited capability and can only anticipate some possible scenarios. Artificial Intelligence and Machine Learning critically perform credit risk analysis and help lending institutions make unbiased decisions. AI optimizes the client’s data and behavior and offers optimal loan amounts considering risk-based pricing.
AI and ML in Credit Risk Analysis – AI and ML have so much information and data, that’s why their credit risk analysis is more precise. Banks and other lending institutions can use AI to improve credit approval, risk determination, and portfolio management. Machine Learning has predictive models based on past data, which helps in credit risk analysis.
AI and ML in Customer Service Automation – Today, customers expect 24*7 services, and AI can help lending institutions expand their customer services and fulfill the customer’s expectations. Besides, if used properly, AI can also predict the customer’s needs. Machine learning processes and analyzes the customer’s data based on browsing history, social media interaction, etc., and shows them results accordingly. This small action makes the customer feel valued and important and converts them into loyal customers.
Mimic Human Intelligence – AI and ML seamlessly mimic human intelligence and can communicate in natural language through mobile apps, chatbots, websites, etc. The bonus point of using AI and ML in digital lending is that it will copy human intelligence but won’t mimic the errors humans generally make. Using AI minimizes human errors and helps in delivering contextually accurate responses to customers. The answers given by AI are based on the data received from the customer’s accounts, social media interactions, and demographics.
Conclusion – Artificial Intelligence and Machine Learning are the future of the finance industry and banking systems. So you should better start embracing it rather than considering them just a mere possibility. In the upcoming years, AI and ML will surely become an integral part of the finance industry. It’s best to conclude this long discourse with a remark that the sooner lenders switch to AI and ML-powered systems, the higher their chances of capturing a profitable share in the industry.