Financial Data Scientist IABAC Certification

A Financial Data Scientist examines complicated financial statistics to identify patterns, optimize strategies, and make data-driven decisions. They use machine learning, statistical modeling, and quantitative analysis to analyze risk, trade algorithmically, detect fraud, and manage portfolios, combining financial knowledge with advanced analytics.

1. FINANCIAL DATA
SCIENTIST
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2. INTRODUCTION
The finance business is becoming
increasingly data-driven.
Traditional financial analysis is changing
in tandem with AI, machine learning, and
big data.
Financial Data Scientists (FDS) are leading
this revolution.
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3. WHO IS A FINANCIAL DATA
SCIENTIST?
A professional blending finance expertise
with data science skills.
Works with vast datasets to extract
insights & enhance decision-making.
Key industries: Investment Banking,
Hedge Funds, FinTech, Risk Management,
Insurance.
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4. KEY SKILLS OF A FINANCIAL
DATA SCIENTIST
1. Technical Skills:
Python, R, SQL, Machine Learning, Deep
Learning, NLP.
Big Data Technologies (Spark, Hadoop).
2. Finance Skills:
Risk Modeling, Portfolio Optimization,
Algorithmic Trading.
Financial Statements & Market Analysis.
3. Soft Skills:
Problem-solving, Communication, and
knowledge of business.
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5. TOOLS & TECHNOLOGIES
USED
Python programming (Pandas, NumPy,
and Scikit-learn), R supports SQL, NoSQL,
and PostgreSQL databases.
whereas TensorFlow and PyTorch are ML
frameworks.
Visualization tools include Tableau, Power
BI, and Matplotlib.
Big data and cloud: AWS, Azure, Google
Cloud, Snowflake.
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6. APPLICATIONS
Algorithmic trading is the use of artificial
intelligence to predict stock movements.
Risk management includes credit risk
modeling and fraud detection.
Portfolio Optimization entails data-driven
investment methods.
Customer Insights: Personalization in
Banking and Wealth Management.
Regulatory Compliance: Automating
audits and spotting irregularities.
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7. CASE STUDY – AI IN TRADING
Problem: High-frequency trading must
require real-time analytics.
Solution: Machine learning models that
forecast price movements.
Outcome: trade execution accuracy
improved while risks were lowered.
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8. CHALLENGES & ETHICAL
CONSIDERATIONS
Challenges:
Data quality and availability difficulties.
Model interpretability for high-stakes
decision-making.
Complicated regulatory and compliance
requirements.
Ethical considerations:
Bias in AI models influences financial
decisions.
Data privacy issues in consumer analytics.
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9. THE FUTURE OF FINANCIAL
DATA SCIENCE
Increased use of AI-powered robo-
advisors.
Quantum computing enables speedier
risk analysis and pricing models.
Blockchain and AI integration for fraud
prevention.
More explainable AI models will help
obtain regulatory trust.
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10. THANK YOU
Visit: iabac.org