debjwubdowsfbswf

1. NAME – ARYAN GOUR
SECTION – K21RS
ROLL NO – RK21RSB72
REG NO - 12108932
ARTIFICIAL INTELLIGENCE IN FINANCE
int– 404

2. AGENDA
Key Points which is discuss in Presentation are as:
• INTRODUCTION
• DIFFERENT APPLICATIONS OF AI IN FINANCE
• IMPACT OF AI IN FINANCE
• METHODOLOGY
• FUTURE SCOPE
• CONCLUSION

3. INTRODUCTION
• Artificial Intelligence (AI) is a rapidly evolving technology that is
increasinglybeing used in the finance industry to improve the efficiency
and accuracy of financial operations. AI refers to the ability of computer
systems to perform tasks that would normally require human intelligence,
such as recognizing patterns, learning from experience, and making
decisions.
• In finance, AI is being used for a wide range of applications, from fraud
detection and risk management to algorithmic trading and personalized
financialinstitutions to identify trends and patterns in large amounts of
data, make more accurate predictions, and automate processes that were
previously done manually.
• Artificial Intelligence (AI) has been increasingly adopted in finance,
and one ofits most promising applications is fraud detection. With the rise
of digital transactions and complex financial systems, traditional methods
of fraud detection have become outdated, and AI offers a more efficient
and accurate approach.

4. • AI systems use machine learning algorithms to analyze vast
amounts of financial data and identify patterns that could indicate
fraudulent activity. Thesealgorithms can detect anomalies in
transactions, such as unusual behavior or deviations from expected
patterns, and alert financial institutions to investigate further.
• In addition, AI can help reduce false positives, which can be a
significant problem for traditional fraud detection methods. By
continuously learning fromhistorical data, AI systems can refine
their detection algorithms and improve their accuracy over time.
• Overall, the introduction of AI in finance fraud detection has the
potential to greatly enhance the efficiency and effectiveness of fraud
detection efforts, ultimately helping to protect consumers and
financial institutions from financiallosses.

5. What is fraud detection? How does it work?
Fraud detection is a process designed with the goal of identifying anomalies in the
data and such prevent unauthorized financial activities, i.e., unauthorized payment
transactions.
In general, every payment you make with your debit card is vetted by the fraud
detection process to ensure the legitimacy of the payment. It goes through a ruleset
and possibly also an ML model.

6. APPLICATION OF AI IN FINANCE
• AI-powered fraud detection systems are designed to analyze large
amounts ofdata from various sources to identify fraudulent activities in
real-time. These systems use machine learning algorithms to identify
patterns of fraudulent behavior and flag suspicious transactions.
•AI algorithms can analyze large volumes of transaction data in real-time
to identify unusual patterns that may indicate fraudulent activity.AI has a
wide range of applications in fraud detection in the finance industry.
Some of thekey applications are:
•Real-time transaction monitoring
•Pattern recognition
•Machine learning
•Identity verification
•Transaction Monitoring
•Fraud detection
•Customer experience

8. IMPACT OF AI IN FINANCE
• Artificial Intelligence (AI) has a significant impact on fraud detection in
finance. The use of machine learning algorithms and other AI techniques has
enabled financial institutions to identify and prevent fraudulent activities in real-
time, making the detection process much more efficient and effective.
• The impact of AI in finance fraud detection has been significant and
transformative. AI has enabled financial institutions to detect and prevent fraud
in a faster, more accurate, and more cost-effective way than traditionalmethods.
• One of the primary ways AI is being used in finance fraud detection is using
machine learning algorithms. These algorithms can analyze large volumes of
data to identify patterns and anomalies that mayindicate fraud.
• Another way AI is being used in finance fraud detection is through the use of
natural language processing (NLP) algorithms. These algorithms can analyze
unstructured data sources such as emails, chat logs, and social media posts to
identify potentially fraudulent activity.

9. We can also learn and adapt to new types of fraud as they emerge, making them more effective
over time. Here are some of the ways. AI has impactedfraud detection in finance:
 Improved Accuracy: AI algorithms can process large volumes of data, identify
patterns and anomalies that human analysts may miss,and detect fraudulent
activities with greater accuracy.
 Real-time detection: AI-based fraud detection systems can monitor transactions
in real-time, and detect and respond to fraudulent activitiesinstantly, reducing the
risk of financial losses.
 Reduced False Positives: AI-based fraud detection systems canreduce the
number of false positives, which can save financial institutions time and
resources by reducing the need for manual reviews.
 Better Risk Assessment: AI-based fraud detection systems can identify and
assess the risk associated with each transaction, customer,and account, allowing
financial institutions to take proactive measuresto prevent fraudulent activities.

10.  Improved efficiency: AI-powered systems can automate repetitivetasks and
provide real-time analysis, leading to more efficient operations and faster
decision-making.
 Better risk management: AI algorithms can analyze vast amounts ofdata to assess
risk and identify potential opportunities, leading to better-informed decision-making
and reduced risk exposure.
 Enhanced fraud detection: AI-powered fraud detection systems can analyze large
volumes of data and identify potential fraudulent activityin real-time, leading to
better fraud prevention.
•
 Improved customer experience: AI-powered chatbots and virtual assistants can
provide personalized recommendations and insights, leading to better customer
experience and satisfaction.
 Increased profitability: AI can predict market trends and identify thebest
investment opportunities, leading to more profitable investments and increased
returns.
 Cost savings: AI-powered systems can automate tasks and reduce theneed for
manual intervention, leading to cost savings and improved profitability.

12. Drivers of AI in Finance(Contd.):

13. FUTURE SCOPE
• The future of AI in finance fraud detection is promising and there areseveral
ways in which AI can enhance fraud detection in the finance industry. Here are
some potential future applications of AI in financefraud detection.
• One of the primary ways AI is being used in finance fraud detection isusing
machine learning algorithms. These algorithms cananalyze large volumes of
data to identify patterns and anomalies that may indicate fraud. They can also
learn and adapt to new types of fraud as they emerge, making them more
effective over time.
• Another way AI is being used in finance fraud detection is using natural
language processing (NLP) algorithms. These algorithmscan analyze
unstructured data sources such as emails, chat logs, and social media posts to
identify potentially fraudulent activity.

14. The future scope for AI in finance is vast and varied. AI has thepotential to transform
many areas of finance, from investment.
management to risk assessment to customer service. Some of thepotential
applications of AI in finance include:
• Biometric Authentication
• Fraud Detection
• Customer Service
• Risk Management
• Automated Investment Management
• Advanced Machine Learning Algorithms: Machine learning algorithms can be used to
identify fraudulent patterns in financial data. With advancements in AI technology, more
complex algorithms can bedeveloped that can detect subtle patterns that may go unnoticed
by human analysts.
• Natural Language Processing: Natural Language Processing (NLP)can be used to analyze
text data, such as emails and chat logs, to detectfraudulent activities. NLP can identify
specific keywords and phrases that may indicate fraudulent activities.
• Predictive Analytics: Predictive analytics can be used to detect fraudulent activities before
they occur. AI algorithms can analyze pastdata and identify potential risks, allowing
financial institutions to takeproactive measures to prevent fraud.

17. CONCLUSION
• In conclusion, AI has played a significant role in detecting and preventing fraud in the
finance industry. With the advancements in machine learning anddeep learning
algorithms, AI has enabled financial institutions to analyze vastamounts of data and
identify patterns that may indicate fraudulent activities.
• AI has significantly impacted the finance industry, particularly in the area offraud
detection. With the increasing volume and complexity of financial transactions,
traditional methods of fraud detection are becoming less effective, leading to the need
for advanced technologies such as AI.
• By utilizing machine learning algorithms, AI can analyze large volumes of data and
identify patterns that may be indicative of fraudulent activity. This allows financial
institutions to quickly detect and respond to potential fraud, reducing the financial
losses and reputational damage that can result from fraudulent activity. While AI is
not a silver bullet for fraud detection and prevention, it has proven to be an effective
tool when combined with humanexpertise and ongoing monitoring. As AI continues
to evolve, it is likely thatit will play an increasingly important role in the fight against
financial fraud.

19. REFERENCES
 Binner, M. J., Kendall, G., & Chen, H. S. (2004). Advances in Econometrics Vol
19: Applications of Artificial Intelligence in Financeand Economics. Bingley, The
UK: Emerald Group Publishing Limited.
 Campbell, T. D., & Cook, D. T. (1979). Quasi-Experimentation:Design &
Analysis Issues for Field Settings. Boston, The US: Houghton Mifflin.
 Cartea, A., Jaimungal, S., Penalva, J. (2015). Algorithmic and High- Frequency
trading. Cambridge: The UK. Cambridge University Press.
 Dale, R., & Reiter, E. (2000). Building Natural Language Generation Systems.
Series: Studies in Natural Language Processing. Cambridge,The UK: Cambridge
University Press.
 Dietrich, D., Heller, B., & Yang, B. (2015). Data Science & Big Data Analytics:
Discovering, Analyzing, Visualizing and Presenting Data. Indianapolis, The US:
John Wiley & Sons, Inc.