Fraud Detection in Credit Lending: A Machine Learning Approach

In today's digital age, fraud detection in credit lending has become a critical challenge for financial institutions. With the increasing volume of online transactions and sophisticated fraudulent activities, traditional methods of detecting fraud are no longer sufficient. In this webinar, the speaker will explore how machine learning models can be leveraged to effectively identify and prevent fraudulent transactions in the credit lending sector.

Participants will gain insights into various machine learning techniques, including supervised and unsupervised learning, anomaly detection, and ensemble methods. The session will cover practical aspects such as data preprocessing, feature engineering, model selection and evaluation metrics, and model monitoring using real-world examples and case studies. Attendees will leave with a solid understanding of how to implement and deploy machine learning models for fraud detection, ensuring enhanced security and trust in credit lending processes.

Prateek Jain is a petroleum engineer turned data scientist specializing in credit lending services, with over 6 years of industry experience. He has graduated from IIT Kharagpur in 2018 and is currently the Lead Data Scientist at Aplazo, a leading Buy Now Pay Later fintech firm in the LATAM region. At Aplazo, they have made significant strides in the Mexican market, achieving a breakthrough with our highly sophisticated fraud detection models, unmatched by any other player in the region.

Before joining Aplazo, he worked with OLA in their BNPL segment, where he played a crucial role in developing collections models, fraud detection models, and underwriting models, contributing to the entire user life cycle. In his free time, he enjoys moderate-level trekking and playing badminton and squash. I am also passionate about engaging in critical discussions about the tech landscape in India and globally. You can reach him on LinkedIn.