Event Name:The Alumni Talk Series	Event Date:13.10.2023
Faculty Coordinators:Ms. Neetu Kamra	Venue:Seminar Hall, Lloyd Business School, Greater Noida
Event Timings :3:15 PM -4:45 PM	No. of Participants:110

Objective:

The objective of the session was to provide participants with an in-depth understanding of how artificial intelligence techniques are applied in credit risk assessment within the financial services sector. The session aimed to explain how Al models improve the accuracy, efficiency, and reliability of credit evaluation and lending decisions. Another objective was to expose students to the technical, ethical, and practical considerations involved in using Al for financial risk management.

Detailed Report:

The institution organized an expert lecture on "Credit Risk Assessment Using Artificial Intelligence," delivered by Dr. Nalla Karuppan M. K., Assistant Professor at the School of Information Technology and Engineering, Vellore Institute of Technology, who shared his academic and research-based insights into the application of Al in financial risk analysis.
Dr Karuppan began by explaining the concept of credit risk and its importance for banks and financial institutions and also described how traditional credit scoring methods rely on historical financial data and predefined rules, which may be limited in capturing complex borrower behavior and emerging risk patterns.
Dr Karuppan then introduced artificial intelligence and machine learning as advanced tools capable of analyzing large volumes of structured and unstructured data to detect hidden patterns, predict default probability, and support more accurate credit decisions.
She explained various Al techniques such as supervised learning, classification models, neural networks, and ensemble methods used in credit risk modeling.
Dr. Karuppan discussed data sources used in credit assessment, including transaction data, repayment history, demographic information, and alternative data such as digital footprints and behavioral indicators. He emphasized the importance of data quality, preprocessing, feature engineering, and model validation in building reliable Al systems.
The session also addressed ethical and regulatory concerns such as data privacy, bias, fairness, explainability, and compliance with financial regulations. Dr. Karuppan stressed the need for transparent and responsible Al to ensure trust and accountability in financial decision-making. She concluded by highlighting emerging trends such as real-time risk assessment, integration of Al with fintech platforms, and the future scope of Al-driven financial services.
The lecture was followed by an interactive discussion, where students asked questions about Al tools, datasets, career opportunities, and research directions in financial technology.

Learning Outcomes:

Participants gained a comprehensive understanding of credit risk concepts and the application of artificial intelligence in financial risk assessment. They learned about Al models, data requirements, and evaluation techniques used in credit scoring. The session enhanced their awareness of ethical, regulatory, and governance considerations in Al deployment. Overall, participants developed insight into how technology is transforming financial decision-making and risk management.
Prepared by: