Indra Reddy Mallela

Indra Reddy Mallela is the VP of Model Risk Management at MUFG Bank, bringing extensive expertise in quantitative analytics, data science, and model validation. With a strong foundation in machine learning and statistical modeling, he excels at extracting actionable insights from large datasets. Indra has a proven track record in leading teams to address complex challenges in compliance, fraud, and financial crime analytics, particularly in anti-money laundering (AML) and sanctions. His technical skills encompass big data technologies like Hadoop, SQL, and various business intelligence tools, enabling him to manage and validate sophisticated machine learning models, including large language models. Indra's experience spans roles at New York Community Bank and GE Capital, where he developed predictive models for credit risk and performed rigorous statistical analysis. He holds a Master’s degree in Management Information Science from Texas Tech University and has contributed to significant projects, such as analyzing crime data for urban areas and assessing credit card decline rates. A thought leader in the field, Indra actively shares his insights through speaking engagements and industry events, showcasing his commitment to advancing the integration of AI in financial services. Indra Reddy Mallela is an accomplished quantitative finance professional with extensive experience in model risk management, machine learning, and financial crime analytics. Currently serving as Director and Quantitative Finance Manager (Model Risk) at Bank of America in Plano, Texas, he oversees model validation, governance, and risk assessment to ensure regulatory compliance and robustness in financial models. Previously, he held the position of VP-Model Risk Manager at MUFG Bank, where he specialized in compliance, fraud detection, anti-money laundering (AML/BSA), OFAC sanctions, and the implementation of large language models (LLMs) and generative AI models. With a strong foundation in risk management, he also worked as a Senior Model Risk Management Analyst at New York Community Bank (NYCB) and as a Quantitative Lead Analyst at GE Capital, gaining deep expertise in stress testing, prepayment and funding models, and credit risk (PD, LGD, EAD). His technical proficiency spans predictive modeling, statistical analysis, time series forecasting, regression techniques, clustering algorithms, neural networks, and hypothesis testing, using programming languages such as R, Python, and SAS. Indra’s expertise extends to visualization tools like Tableau and Qlik View for effective reporting and analytics. His strong background in model validation, documentation, backtesting, and alternative model development ensures regulatory compliance and enhances financial risk assessment. He also has experience developing fraud detection models for credit and debit transactions, applying machine learning techniques like logistic regression, random forests, and XGBoost. In addition to his professional career, Indra has contributed to academic research, including crime analysis and credit card fraud modeling, demonstrating his ability to apply data science to real-world problems. He holds a Master’s degree in Management Information Science (Data Science) from Texas Tech University - Rawls College of Business and an Applied Data Science Certification from Syracuse University. Additionally, he earned a Bachelor of Technology in Chemical Engineering from Jawaharlal Nehru Technological University. His technical acumen is complemented by certifications in Big Data and Hadoop Essentials. Indra’s comprehensive skill set in quantitative finance, regulatory compliance, AI-driven risk modeling, and statistical analysis positions him as a key contributor in the field of model risk management. His ability to develop and validate sophisticated financial models while maintaining rigorous governance and compliance standards has made him a valuable asset in the banking industry. His leadership in independent validation and peer review of complex predictive models ensures adherence to best practices and regulatory guidelines. With experience in alternative modeling techniques, champion-challenger model development, and advanced sensitivity analyses, Indra has played a critical role in refining risk assessment methodologies. His expertise in transaction monitoring, customer risk rating, and fraud detection, along with his hands-on approach to machine learning and AI-driven analytics, underscores his ability to navigate the evolving landscape of financial risk management.