Assistant Manager - Anti-Money Laundering (Compliance and Remediation)

About KPMG in India

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.

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About KPMG in India

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.

About:

Our Financial Crimes specialist teams provide solutions to BFSI clients by conducting model validation testing for AML risk models and frameworks, sanctions screening and transaction monitoring system to ensure efficiency and efficacy of underlying frameworks both functionally and statistically.

We are looking to hire colleagues with advance data science and analytics skill to support our financial crimes team. You will play a crucial role in helping clients tackle the multifaceted challenges of financial crime. By utilizing advanced analytics and deep technical knowledge, our team aids top clients in reducing risks associated with financial crime, terrorist financing, and sanctions violations. We also work to enhance their screening and transaction monitoring systems. Our team of specialized analysts ensures that leading financial institutions adhere to industry best practices for robust programs and controls. Through a variety of project experiences, you will develop your professional skills, assisting clients in understanding and addressing complex issues, and implementing top-tier solutions to resolve identified problems.

Minimum work experience: 3+ years of advance analytics

Preferred experience: 1+ years in AML model validation

Responsibilities

·    Support functional SME teams to build data driven Financial Crimes solution

·    Conduct statistical testing of the screening matching algorithms, risk rating models and thresholds configured for detection rules

·    Validate data models of AML systems built on systems such as SAS Viya, Actimize, Lexis Nexis, Napier, etc.

·    Develop, validate, and maintain AML models to detect suspicious activities and transactions.

·    Conduct Above the Line and Below the Line testing

·    Conduct thorough model validation processes, including performance monitoring, tuning, and calibration.

·    Ensure compliance with regulatory requirements and internal policies related to AML model risk management.

·    Collaborate with cross-functional teams to gather and analyze data for model development and validation.

·    Perform data analysis and statistical modeling to identify trends and patterns in financial transactions.

·    Prepare detailed documentation and reports on model validation findings and recommendations.

·    Assist in feature engineering for improvising Gen AI prompts applicable for automation of AML / Screening related investigations

·    Use advanced Machine Learning deployment (e.g. XGBoost) and GenAI approaches

Criteria:

·    Bachelor’s degree from accredited university

·    3+ years of complete hands-on experience in Python with an experience in Java, Fast, Django, Tornado or Flask frameworks

·    Working experience in Relational and NoSQL databases like Oracle, MS SQL MongoDB or ElasticSearch

·    Proficiency BI tools such as Power BI, Tableau, etc.

·    Proven experience in data model development and testing

·    Education background in Data Science and Statistics

·    Strong proficiency in programming languages such as Python, R, and SQL.

·    Expertise in machine learning algorithms, statistical analysis, and data visualization tools.

·    Familiarity with regulatory guidelines and standards for AML

·    Experience in AML related model validation and testing

·    Expertise in techniques and algorithms to include sampling, optimization, logistic regression, cluster analysis, Neural Networks, Decision Trees, supervised and unsupervised machine learning

Preferred experiences:

• Validation of AML compliance models such as statistical testing of customer / transaction risk models, screening algorithm testing, etc.
• Experience with developing proposals (especially new solutions)
• Experience working AML technology platforms e.g. Norkom, SAS, Lexis Nexis, etc.
• Hands on experience with data analytics tools using Informatica, Kafka, etc.

Equal employment opportunity information 

KPMG India has a policy of providing equal opportunity for all applicants and employees regardless of their color, caste, religion, age, sex/gender, national origin, citizenship, sexual orientation, gender identity or expression, disability or other legally protected status. KPMG India values diversity and we request you to submit the details below to support us in our endeavor for diversity. Providing the below information is voluntary and refusal to submit such information will not be prejudicial to you.
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  Bachelor’s degree from accredited university

·    Education background in Data Science and Statistics

·    3+ years of complete hands-on experience in data science and data analytics