Artificial Intelligence applications in the Finance sector

An overview of some of the critical applications of AI in finance including Credit Risk Assessment, Fraud Detection, Algorithmic Trading, Portfolio Optimization as some of the challenges and risks associated with the use of AI in this sector and some future trends.

1. AI in Finance
MDSSB-DSAI-02 Artificial Intelligence in Business and Society
Suraj Karakulath

2. Introduction
●Applications of AI in some select aspects of
finance:
○Credit Risk Assessment
○Fraud Detection
○Algorithmic Trading
○Portfolio Optimization

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3. Credit risk assessment




●Evaluating the creditworthiness of individuals
or businesses seeking credit, such as loans
or credit cards.
●Goal is to predict the likelihood of a borrower
defaulting on their credit obligations.

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4. Credit risk assessment


Traditional approach relied on limited data points,
such as credit scores, income, and a borrower's
credit history.

●Limited Data: Relatively small set of financial
data, may not provide a comprehensive view
of a borrower's financial situation.
●Human Bias: Human underwriters may
introduce bias into the assessment process
●Static Models: Couldn't adapt to changing
financial circumstances.


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5. Credit risk assessment


How AI Enhances Credit Risk Assessment:

●Rich Data Sources: Not only traditional financial data but also non-traditional sources
like social media activity, transaction history, and more = More comprehensive
understanding of a borrower's financial behavior.
●Machine Learning Models: Predictions based on historical data.
●Alternative Data: Rental payment history, utility bill payments, and even data on a
borrower's smartphone usage.
●Predictive Analytics: Estimate the likelihood of default or late payments. They can
assign a credit score that more accurately reflects a borrower's risk profile.
●Automation: Streamlines the process, reduces human error, and increases efficiency,
enabling faster lending decisions.
●Reduced Bias: More objective and less prone to bias, resulting in fairer lending
decisions.


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6. Fraud detection and prevention


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●Identify and mitigate various forms of
fraudulent activities – unauthorized
transactions, identity theft, account takeover
etc.
●Goal is to detect fraudulent activities as
quickly and accurately as possible to prevent
financial losses and protect customers.

7. Fraud detection and prevention


Traditional methods relied on rule-based systems
and manual review processes. These had some
limitations:

●Effective for known fraud patterns but could
not easily adapt to emerging fraud schemes.
●Human analysts reviewed flagged
transactions – time-consuming and prone to
errors.
●Limited capacity to analyze large volumes of
data in real time – less effective in identifying
complex fraud patterns.

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9. Fraud detection and prevention


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How AI Enhances Fraud Detection and Prevention:

●Real-time monitoring, analyze vast amounts of data to identify suspicious patterns
●Learn and establish a baseline of normal user behavior - and then quickly detect
deviations from this baseline
●ML models can uncover complex, multi-dimensional fraud schemes that may go
unnoticed by rule-based systems.
●Predictive analytics and automated response – assess the likelihood of a transaction
being fraudulent, automated response such as blocking a suspicious transaction
●Reduced false positives – more accurate in identifying true fraud cases
●Scalability – handle large volumes of data, suitable for rapid processing
●Continuous learning: AI systems adapt and evolve over time as they learn from new
data, improving their detection capabilities.

10. Algorithmic trading



●To automate the process of buying and
selling financial instruments
●Make trading decisions based on a set of
rules, strategies, and parameter
●Speed: Execute trades in ms or microsec
●Efficiency: Handle large volume of trades
without manual intervention.
●Precision: Execute trades with minimal
human error.
●Diversification: Implement a wide range
of trading strategies simultaneously.


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11. How AI Powers Algorithmic Trading:

●Process vast amounts of historical and
real-time market data to identify patterns
and trends.
●ML models:
○Supervised: E.g. predict whether
the price will go up or down.
○Reinforcement: Make decisions,
execute trades, and learn from the
outcomes
○Unsupervised: Identify clusters,
which can be used for market
segmentation and anomaly
detection.
Algorithmic trading
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13. Portfolio optimization


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●Constructing an investment portfolio to achieve
the highest possible returns while managing and
minimizing risk.
●Involves selecting a combination of assets (e.g.,
stocks, bonds, real estate) that collectively
offers the best risk-reward trade-off
●Traditionally done Modern Portfolio Theory
(MPT) – math models considering historical
returns and risk metrics
●Limitations
○Simplistic assumptions that don't fully
capture the complexities of real-world
markets
○Static models
○Inadequate data processing

14. Trading and Investment
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How AI Enhances Portfolio Optimization:

●Advanced data analysis – Vast datasets, including market prices, macroeconomic
data, and alternative data sources, i.e. more comprehensive analysis of assets and
markets.
●Dynamic – Adjust the portfolio based on real-time data and changing market
conditions (particularly valuable in volatile markets)
●Diversification: AI models identify assets that are less correlated, enabling diversified
portfolios (helps spread risk and reduce the impact of underperforming assets)

15. Trading and Investment
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16. Challenges and Risks
●Data Privacy and Security - since AI systems handle sensitive financial data
●Transparency and Explainability: AI models, especially complex ones, can be hard to
explain, making it challenging to understand how they arrive at decisions.
●Algorithmic Bias: AI algorithms can inherit biases from historical data, potentially leading
to unfair or discriminatory outcomes, especially in lending or hiring decisions.
●Fair Lending: There's a need to ensure AI-driven lending models don't unfairly
discriminate against certain groups, as this could violate anti-discrimination laws.
●Overreliance on AI: Risk of overreliance on AI, neglecting the valuable expertise of
human financial analysts.
●Black Swan Events: AI models may not predict rare, unpredictable events, leading to
significant financial losses.
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17. Future Trends
●Quantum computing for solving complex
problems that classical computers struggle
with.
●Explainable AI (XAI) for increasing
transparency and helping users, regulators,
and stakeholders understand why AI systems
make specific decisions.
●AI-Powered Robo-Advisors for Personalized
Wealth Management – offering personalized
investment strategies tailored to individual
financial goals and risk tolerance.