AML Modelling- Assistant Manager

OVERVIEW

KPMG entities in India are professional services firm(s). These Indian member firms are affiliated with KPMG International Limited. KPMG was established in India in August 1993. Our professionals leverage the global network of firms, and are conversant with local laws, regulations, markets and competition. KPMG has offices across India in Ahmedabad, Bengaluru, Chandigarh, Chennai, Gurugram, Jaipur, Hyderabad, Jaipur, Kochi, Kolkata, Mumbai, Noida, Pune, Vadodara and Vijayawada.

KPMG entities in India offer services to national and international clients in India across sectors. We strive to provide rapid, performance-based, industry-focused and technology-enabled services, which reflect a shared knowledge of global and local industries and our experience of the Indian business environment.

Roles And Responsibilities

• Carrying out detailed quantitative review of ML models development and validation processes. Documenting all work performed in a clear, concise, and re-performable manner. Tracking and closing model-related findings.
• Good understandings on statistical concepts on classification techniques like logistic regression, XGBoost, Adaboost, decision tree etc.
• Providing subject matter expertise on models and model risk to teams globally.
• Conducting continuous monitoring activities to identify and assess emerging and existing model risks.
• Strong knowledge of anti-money laundering (AML), fraud model, transaction monitoring models, customer risk rating models, feature engineering, hyperparameter tuning.
• Strong understanding of regulations and guidelines like SR 11-7 or other equivalent guidelines for model risk management.
• Review outcome, impact, or benchmark analysis, or develop/ validate a benchmark model (as applicable).
• Assess model risk, perform model robustness analysis, and identify and evaluate model limitations.
• Proven experience in model development, validation, or audit across fraud analytics models.
• Strong knowledge of regulatory expectations, model risk governance, and financial risk management practices.

Qualifications:

• Bachelors, Masters or PhD in Statistics, Financial Engineering, Quantitative Finance etc. preferably with a first degree in Stats, Engineering etc.
• Strong Knowledge of machine learning techniques, classification models, decision tree.
• Strong interpersonal skills, excellent written and verbal communication skills and very strong analytical and problem-solving abilities.
• Proven experience in model validation, quantitative analysis, or a similar role within the financial services industry.
• Strong analytical skills and proficiency in statistical software/tools (e.g., Python, R, SAS).

Equal employment opportunity information

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