Automated Loan Underwriting platform

Monsoon's Automated Loan Underwriting Platform is a solution designed to provide banks, NBFCs, HFCs, and fintech lenders in the BFSI sector with machine learning-powered predictive models and scorecards. This platform is tailored for lenders in the banking and financial services sector, as well as credit analysts, risk managers, loan underwriters, and financial analysts. With its range of features and advanced analytics capabilities, Monsoon's Automated Loan Underwriting Platform aims to streamline loan underwriting processes and optimize risk assessment for financial institutions of all sizes.

Key Features

Application scorecards: According to the vendor, this feature utilizes Bureau data, Banking data, and/or alternate data to facilitate the quick approval of safe loan applications while identifying and rejecting risky ones. The vendor claims that this feature can help reduce delinquency rates by up to 30% and increase approval rates by up to 25%.

Behavioral Scorecards: The vendor states that this feature leverages Bureau data, transaction data, and past repayment behavior to predict the risk associated with existing loans, enabling timely intervention. By utilizing this feature, financial institutions can potentially reduce risk costs by up to 35% and uncover cross-selling opportunities.

Collection scorecards: The vendor claims that this feature leverages bureau data, past repayment data, and responses to collection efforts to predict which customers are likely to miss their next payment. By prioritizing collection efforts, financial institutions may achieve up to a 40% reduction in collection costs.

Pricing Models: According to the vendor, this feature allows financial institutions to implement risk-based pricing models to determine loan interest rates, loan-to-value ratios (LTV), and tenures based on customer risk and pricing power. The vendor suggests that lenders can optimize their return on investment (ROI) and fine-tune loan parameters based on borrower risk.

Cross-Sell scorecards: The vendor claims that this feature utilizes available data to predict which customers are most likely to accept cross-sell offers and for which products. By leveraging this feature, financial institutions may drive up to 35% more cross-sell revenue.

Customer Attrition Models: According to the vendor, this feature predicts which customers are likely to close their loan or deposit accounts months in advance, enabling proactive intervention. The vendor suggests that lenders can potentially reduce customer attrition rates by up to 24%.

Pre-approval models: The vendor states that this feature allows financial institutions to pre-approve customers from the liability base for asset-side products, helping raise book-size with minimal risk. The vendor claims that this feature enables financial institutions to approve up to 35% of the base at minimal risk.

Early Warning Models: The vendor states that these models, similar to behavioral scorecards, also account for infant-delinquency, i.e., delinquency within the first 1-2 months of disbursal. According to the vendor, lenders can effectively cut risk costs similar to behavioral scorecards by utilizing this feature.

Income estimation models: According to the vendor, this feature enables quick estimation of a customer's income based on bureau data, aiding lenders in ascertaining the true income of a customer during underwriting. The vendor suggests that this feature works in tandem with application scorecards to enhance the accuracy of risk assessment.

Pre-PD models: The vendor claims that this feature enables quick risk-bucketing of loan applications before expensive Personal Discussions or Field Investigations, allowing low-risk customers to be exempted. By utilizing this feature, financial institutions may reduce costs related to field interviews by up to 40%.