Machine Learning Is The Future Of Credit Underwriting
Machine learning has taken the digital world by storm. With its immense possibilities, machine learning has the potential to transform the ways most industries function. This is especially true of the finance industry, which has seen recent changes with the explosion of internet banking and multiple online-payment options. So, before we proceed, let us understand what machine learning is and how it can help in credit underwriting for peer-to-peer lending companies.
Machine Learning at a Glance
Machine learning is a branch of artificial-intelligence based on the idea that systems are capable of learning from data to identify patterns and make decisions with minimal human intervention. An algorithm is equipped to analyze and glean insights from millions of consumer-behavior patterns evident online. The system scans the information being recorded in an online database to identify patterns of behavior, analyze risk-assessment, make well-informed decisions, and undertake necessary actions in seconds.
Examples of machine learning could be the top-picks-for-you list that your music-app comes up with based on your listening history, or how Amazon comes up with product recommendations based on your purchase and browsing history.
Machine Learning in Credit Underwriting
Machine learning has diverse applications in the finance sector. The vast amount of information required to make financial decisions range from the consumer’s transactional history, online purchasing behavior, job profile, salary, investments, social-media activity, patterns of behavior, and the like. It is especially helpful in assessing the creditworthiness of a potential customer by a lender or a financial service, also called credit underwriting. Credit underwriting for peer-to-peer companies helps in finding if a potential borrower can return the money to the lender. In order to avoid defaults, it is very important for peer-to-peer lending companies to accurately judge the borrower’s intent and willingness to return the invested amount within the stipulated time.
Modern peer-behavior to-peer lending companies like RupeeCircle undertake extensive research involving more than two-hundred consumer touchpoints to verify and include borrowers on their platform. The touchpoints include personal, professional and social data, online activity, and behaviour patterns of the borrowers. These are then used to prepare the Proprietary Credit Rating Score Model which sorts borrowers into risk-categories ranging from A to F—A being the strongest and F being the weakest. The interest rate is then assigned according to the risk-credit-score.
Benefits of Machine Learning in Credit Underwriting
Machine learning makes the process of credit underwriting easier in many ways. It not only makes the process faster and easier to manage but also helps the lender in taking quick decisions regarding the borrower’s credit-worthiness. This also implies that borrowing becomes an easier and quicker process. This also reduces the operational costs of the company, paperwork load, and a significant amount of time and effort of the employees. Minimized human intervention also removes the possibility of human errors in data input, calculation, and other such tasks, thereby increasing the overall productivity of the company. This not only translated into increased revenues but also leaves the customers satisfied.
Machine learning system is well-equipped to handle the security concerns associated with fintech companies. The learning algorithms detect frauds and any uncommon or suspicious activity linked to the borrower’s account in a fraction of a second. It is a highly efficient system capable of detecting fraudulent transactions, spotting and foiling money laundering techniques, identifying patterns of a cyber threat, and nullifying them—all in real time and with high precision. It will enhance network-security and contain any form of cyber threats.
Though machine learning comes with a plethora of pros, it is always a good idea to back it up with human resources. At RupeeCircle, we offer the human resources that facilitate the optimum functioning of machine learning. As a fresh fintech start-up, we at RupeeCircle understand the need for human resources in machine learning in the finance sector to address the shortcomings of the digital algorithms. We provide in-house tele-calling, field-verification, and collections teams to minimize the risk of defaults and frauds. Contact us today for the best combination of machine learning and human resources for all your borrowing and investing needs.