Advancements in technology have revolutionized how people carry out financial transactions. It continues to disrupt the financial services industry even today!
Consumers can now effortlessly wield digital platforms to make payments online, transfer funds, invest, and request loans from any location at any time and on any device. Studies estimate that the overall transaction value for digital payments worldwide was $7.52 trillion in 2021 and is projected to reach $8.49 trillion in 2022.
Digital lending, which includes personal loans and bank-independent credit distribution, is another sector quickly expanding in the fintech industry. The loan amounts are typically lower than those established by established lenders like banks. Today, borrowers can submit mortgage lending applications via websites or digital lending organizations’ mobile applications. The market for alternative lending is anticipated to increase at a Compound Annual Growth Rate of 2.45% to reach US$ 407.80 billion by 2027 from its forecast value of US$ 361.30 billion in 2022. Similarly, the Indian market for digital loans is surging rapidly, going from US$ 9 billion in 2012 to almost US$ 110 billion in 2019.
Artificial Intelligence is acting as a driving force in the digital lending market.
A digital lending platform relies heavily on Artificial Intelligence (AI) and Machine Learning (ML) to enrich data analysis and boost credit risk assessment’s effectiveness. Distinguished digital lending platforms employ AI and ML to analyze massive amounts of data to make lending choices backed up by data, identify fraudulent applications and conceivable defaulters, and target good clients for cross-selling and up-selling other products.
Conventional lenders traditionally assess a person’s credit using a prospective borrower’s financial evidence. However, with the development of digital platforms and solutions, particularly AI models, lenders can now assess credit with better precision by using a range of data, including digital customer behaviour, social media profiles, digital payment data, and a host of other data points. Credit managers can make more informed and explicable judgments because of the ability of AI models to assimilate massive volumes of structured and unstructured data from many sources in real time.
How does AI add value to digital lending?
Enhanced efficiency and Agility
Automatic identification of which commercial loan applications are most likely to be validated, which are least reasonable, and which require more time and human reasoning is conceivable with AI-powered machine learning models. By transforming data into useful information for the banker, you might eventually curtail the time it takes to accept a loan by up to 85%, as most applications are for straightforward loan renewals.
Clients may wait for the successive stage to occur for 90% of the time it takes to process a loan due to the sophistication of the commercial lending cycle. AI digital lending platforms speed up loan processing by up to 30% by automating workflow to keep the authorization process moving and simplifying documents to deduct the weight of testimony for borrowers.
More beneficial procedures and vigorous replies reduce the cost of furnishing service and reasonably raise customer retention and satisfaction. Relationship managers can give consumers feedback on the chances of approval or ask for further information almost immediately because the AI models are quick to recommend approval. That ends the digressive apprehensions that can hinder the lending procedure.
More productive credit risk management
AI digital lending platforms significantly reinforce the perception and accuracy of risk monitoring operations by consolidating data from multiple structured and unstructured data sources and providing alerts to potential covenant infringements and defaulters. Credit officers may scrutinize risk factors for each customer and model how revisions to credit terms will affect the borrower’s risk profile using AI score breakdowns and comprehensive guidance.
Fuel your business growth with AI digital lending platforms.
The AI model not only steers data-backed credit conclusions but also aids in developing user personas, which are crucial for finding out future apps comparable to this one. These user personas can be wielded to target promising customers, augment the quality of leads, and assist firms in fine-tuning their marketing and outreach undertakings. Besides, the AI model can update the favourable user persona through persistent examination of many client apps and the data encompassed therein. The quality of the leads is significantly improved by these ongoing upgrades, which help your business to grow and flourish in the ever-growing lending market.