Data science in finance uses machine learning, analytics, and big data to improve decision-making, risk management, and fraud detection. It enables banks, investors, and enterprises to monitor patterns, forecast market moves, and optimize portfolios for improved financial performance and security.

1. iabac.org
DATA SCIENCE IN
FINANCE

2. INTRODUCTION
Data and algorithms are increasingly
driving financial decisions.
Data science is useful for risk
assessment, fraud detection,
algorithmic trading, and customer
insights.
It looks at major applications and
methodologies in financial data
science.
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3. THE ROLE OF DATA SCIENCE
IN FINANCE
Risk management: predicting credit
and market hazards.
Fraud detection: identifying odd
transactions.
Algorithmic trading: The process of
making automated data-driven
decisions.
Customer analytics: personalized
financial goods and advice.
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4. iabac.org
DATA SOURCES IN FINANCE
Market Data (Stock Prices and Indices).
Transaction data (credit card
transactions and payments).
Alternative Data (Social Media, Satellite
Pictures).
Financial Statements (Balance sheets
and income statements).

5. KEY TECHNIQUES USED
Machine learning: Predictive analytics
and classification.
Natural language processing:
Sentiment evaluation of financial news.
Time Series Analysis: Stock price
predictions.
Deep learning: Recognizing patterns
in financial transactions.
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6. RISK MANAGEMENT WITH
DATA SCIENCE
Credit scoring utilizing machine
learning (e.g., logistic regression,
random forests).
Predicting defaults with alternative
data.
Portfolio risk optimization with Monte
Carlo simulations.
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7. FRAUD DETECTION IN FINANCE
Clustering techniques are used to find
anomalies in transactional data.
Supervised learning (e.g., decision
trees) for fraud detection.
Detecting fraud in real time with
artificial intelligence.
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8. ALGORITHMIC TRADING
High-frequency trading uses data-
driven methods.
Reinforcement learning in adaptive
trading.
Sentiment analysis is used to predict
market movements.
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9. CHALLENGES IN FINANCIAL DATA
SCIENCE
Data privacy and regulatory issues
(GDPR, SEC requirements).
Bias in algorithms results in unfair
lending decisions.
In finance, models should be
interpretable and explainable.
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10. THANK YOU
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