Source – deccanchronicle.com
The lending ecosystem around the world has been at the centre of significant changes in the last decade. From financial technology disrupting the financial services sector industry with highly efficient and cost-effective processes, to stringent regulations following the 2008 global financial crisis, the growing technological intervention has played a significant role in the rapid evolution of the lending industry. One such technology is machine learning which has begun to create new and highly promising avenues in the lending market.
What is machine learning?
Machine learning is a Predictive Model Algorithm that develops Artificial Intelligence around large sets of data through different predictive statistical techniques (such as Logistic Regression, Random Forest, Decision Tree etc.) and imparts decisions/insights based on the data it processes. Machines can be taught to identify any form of data which is stored electronically such as texts, images, speech, etc. and analyse by the machine through such algorithms to identify behaviours, patterns etc. and generate similar predictions when imposed on a new dataset.
Lending and machine learning
Fintech companies are increasingly augmenting the applications of machine learning algorithms in their operations to build efficient and effective systems. Here are some of the most prominent ways in which machine learning is used in fintech lending companies:
More efficient lending process
Most global fintech companies are already leveraging machine learning to expedite the lending process and make it more efficient. Faircent.com has invested heavily in technologies that uses big data for analytics and developing machine learning algorithms for decision making. This makes loan approvals quick and easy, reduce operational costs and these savings can then be extended to customers in the form of lower rates.
Another implication of machine learning in lending is that it streamlines the process, drastically reduces the likelihood of errors and significantly cuts down the time it takes to approve a loan and disburse funds to the borrower, thereby enhancing the customer experience. Machine learning does away with the need for human intervention in determining creditworthiness to augment processes such as underwriting and origination. Lending platforms can create such algorithms to classify loan applications and approve them. Such algorithm uses the applicants’ available information and make future predictions about them.
Credit scoring is the crux of loan management. Through machine learning, an algorithm for predictive model is built to process credit scoring. Machine learning tools access data through extensive mining from various sources such as online transactional behaviour, purchasing behaviour with e-commerce, social media activity, etc. to deduce the creditworthiness of an applicant accurately. This ensures fair play, with the borrower getting listed in risk buckets or at interest rates as per their credit worthiness.
Machine learning is undoubtedly the ultimate tool in fintech to undertake better risk management. Through machine learning we can mine a lot of data sources (unlike traditional financial institutions) and develop statistical algorithms around them for decision making. Faircent.com’s credit evaluation algorithm accesses data from a number of sources – personal, financial, bureau and social – to evaluate the borrowers’ credit worthiness across more than 120 parameters. It can extract the most current and relevant information to make predictions and make decisions regarding a particular loan application. This ensures that a Lender gets a highly curated platform helping them mitigate risk.
Additionally, machine learning can be implemented by lending companies to identify underserved borrowers, and target them through effective marketing and promotional campaigns. Moreover, machine learning is highly cost-effective since the task of monitoring fintech activities such as investment and lending are undertaken by machines. With AI-driven tools being increasingly implemented by the fintech sector, these companies make for excellent allies to traditional banks to provide tech and data-driven decision-making for lending and borrowing.