Archisman Som

Manager, Financial Crime Risk Analytics @ HSBC

About

Data Science professional with 4+ years of experience across Financial Services in banking and consulting environments. Currently working as a Manager in Financial Crime Analytics at HSBC, supporting data-driven analysis, regulatory compliance and performance monitoring across high-risk customer and transaction typologies.

Country

City

India

Industry

Financial Services

Skill

Financial Data Analytics, Data Analytics, Data Analysis, SQL, Fraud Risk Analytics, Risk Analytics, Model Validation, Regulatory Risk, Anti-Money Laundering, Compliance Analytics, Transaction Monitoring, Financial Crime Analytics, Python (Programming Language), Stochastic Modeling, Financial Risk Management, Financial Derivatives, Fixed Income Markets, Data Science, Financial Engineering, Stochastic Calculus

Experience

Manager, Financial Crime Risk Analytics

HSBC

2025-6 - Present · 1 yr 4 mos

Kolkata

1. Supported data-scientific engagements within transaction monitoring and screening frameworks, driving detection logic, alert patterns and threshold optimization. Developed python/SQL workflows to evaluate alert quality, case volume and simulate fraudulent scenarios. 2. Produced model performance MI and partnered with regulatory/tech teams to improve monitoring efficiency, model explain-ability and data-driven decisions.

Consultant

KPMG India

2024-9 - 2025-5 · 9 mos

Mumbai

1. Performed analytical monitoring of fraud models, evaluating the performance, stability and data quality using statistical techniques. 2. Applied quantitative methods to detect anomalies, generate data-driven insights. Delivered structured validation outputs and stakeholder presentations.

Project Associate

Indian Institute of Quantitative Finance

2024-2 - 2024-8 · 7 mos

Mumbai

Did capstone project for the professional certification in Financial Engineering.

Analyst - 1, Model Validation

Bread Financial

2023-3 - 2024-1 · 11 mos

Bengaluru

1. Executed end-to-end validation of fraud detection models (statistical & ML), assessing performance metrics, challenger effectiveness and score stability. Evaluated model assumptions, data quality, feature robustness, and drift patterns through quantitative tests and statistical diagnostics. 2. Developed reproducible python workflows to compute validation KPIs, back-testing metrics and confidence intervals. Produced comprehensive validation reports and presented before developers and governance, highlighting findings and remediations.

Data Scientist

Genpact

2021-11 - 2023-3 · 1 yr 5 mos

Bengaluru

1. Conducted advanced EDA on customer and transaction datasets using Python to uncover fraud patterns and early warning indicators. Built analytical pipelines and data transformations support feature engineering and rule logic enhancement. 2. Automated reporting frameworks that improved efficiency by reducing TAT across fraud analytics. Collaborated with strategy teams to convert insights into actionable improvements (enhanced detection logic, refined segmentation approach, etc.)