Credit Card Fraud Detection: Leveraging Advanced Techniques
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Join Deepika in this insightful presentation on credit card fraud detection. Explore the latest techniques and methodologies used to identify and prevent fraudulent activities. Understand the role of machine learning and artificial intelligence in enhancing detection accuracy. Learn about real-world applications, challenges, and best practices to safeguard financial transactions effectively. Perfect for students and professionals aiming to deepen their knowledge in cybersecurity and fraud prevention for more information visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
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lyze data to support decision making find success. Information has power, and understanding that power will help
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■ T h e B u s i n e s s B e n e f i t s o f
Da t a Wa re h o u s i n g
■ Pe r fo rm i n g B u s i n e s s A n a l y s i s
w i t h Da t a M a rt s
■ U n c ove ri n g Tre n d s a n d Pa tte rn s
w i t h Da t a M i n i n g
■ S u p p o rt i n g D e c i s i o n s w i t h
B u s i n e s s I n te l l i ge n c e
SECTION 6.2
Business Intelligence
■ T h e B u s i n e s s B e n e f i t s o f
H i g h - Q u a l i t y I n fo rm a t i o n
■ S to ri n g I n fo rm a t i o n U s i n g
a R e l a t i o n a l Da t a b a s e
M a n a ge m e n t Sy s te m
■ U s i n g a R e l a t i o n a l Da t a b a s e
fo r B u s i n e s s Ad va n t a ge s
■ D ri v i n g We b s i te s w i t h Da t a
SECTION 6.1
Data, Information,
and Databases
C
H
A
P
T
E
R
O
U
T
L
IN
E
Data: Business Intelligence 6
C H A P T E R
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The Kavi Global team will present a cloud native, wizard-driven AI anomaly detection solution, enabling Citizen Data Scientists to easily create anomaly detection models to automatically flag Collective, Contextual, and Point anomalies, at the transaction level, as well as collusion between actors. Unsupervised methods (Distribution, Clustering, Association, Sequencing, Historical Occurrence, Custom Rules) and supervised (Random Forest, Neural Network) models are executed in Apache Spark on Databricks.
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What’s in IT for me?
This chapter introduces the concepts of information and data and their relative importance to business profession-
als and firms. It distinguishes between data stored in transactional databases and powerful business intelligence
gleaned from data warehouses. Students who understand how to access, manipulate, summarize, sort, and ana-
lyze data to support decision making find success. Information has power, and understanding that power will help
you compete in the global marketplace. This chapter will provide you with an overview of database fundamentals
and the characteristics associated with high-quality information. It will also explain how the various bits of data
stored across multiple, operational databases can be transformed in a centralized repository of summarized infor-
mation in a data warehouse, which can be used for discovering business intelligence.
You, as a business student, need to understand the differences between transactional data and summarized infor-
mation and the different types of questions you could use a transactional database to answer versus a data ware-
house. You need to be aware of the complexity of storing data in databases and the level of effort required to transform
operational data into meaningful, summarized information. You need to realize the power of information and the
competitive advantage a data warehouse brings an organization in terms of facilitating business intelligence. Armed
with the power of information, business students will make smart, informed, and data-supported managerial decisions.
■ T h e B u s i n e s s B e n e f i t s o f
Da t a Wa re h o u s i n g
■ Pe r fo rm i n g B u s i n e s s A n a l y s i s
w i t h Da t a M a rt s
■ U n c ove ri n g Tre n d s a n d Pa tte rn s
w i t h Da t a M i n i n g
■ S u p p o rt i n g D e c i s i o n s w i t h
B u s i n e s s I n te l l i ge n c e
SECTION 6.2
Business Intelligence
■ T h e B u s i n e s s B e n e f i t s o f
H i g h - Q u a l i t y I n fo rm a t i o n
■ S to ri n g I n fo rm a t i o n U s i n g
a R e l a t i o n a l Da t a b a s e
M a n a ge m e n t Sy s te m
■ U s i n g a R e l a t i o n a l Da t a b a s e
fo r B u s i n e s s Ad va n t a ge s
■ D ri v i n g We b s i te s w i t h Da t a
SECTION 6.1
Data, Information,
and Databases
C
H
A
P
T
E
R
O
U
T
L
IN
E
Data: Business Intelligence 6
C H A P T E R
bal76825_ch06_201-240.indd 201bal76825_ch06_201-240.indd 201 11/15/10 3:23 PM11/15/10 3:23 PM
Confirming Pages
202 Chapter 6 Data: Business Intelligence
opening case study
Business Intelligence Software’s Time Is Now
With restaurants stretching from Seattle to the Osan Air Base in South Korea, the
Chili’s Grill & Bar chain gets many visitors. For parent company Brinker International,
that makes many Triple Dippers to keep track of.
Big Data Retail Banking
Big Data Retail Banking Sandeep Bhagat Nicolas has a vision of opening a French restaurant using his grandmother's recipes. He is discussing a loan with his banker. The banker not only offers the loan but also provides valuable business insights using data analytics. The banker examines demographic and spending data to recommend the best locations and price points for Nicolas's restaurant. This illustrates how banks can leverage big data to generate new revenue streams by providing business insights to customers.
Gmid associates services portfolio bank
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The Kavi Global team will present a cloud native, wizard-driven AI anomaly detection solution, enabling Citizen Data Scientists to easily create anomaly detection models to automatically flag Collective, Contextual, and Point anomalies, at the transaction level, as well as collusion between actors. Unsupervised methods (Distribution, Clustering, Association, Sequencing, Historical Occurrence, Custom Rules) and supervised (Random Forest, Neural Network) models are executed in Apache Spark on Databricks.
An innovative aggregation framework converts probabilistic fraud scores and their probabilities into a meaningful and actionable prioritized list of suspicious (a statistical outlier) and potentially fraudulent transaction to be investigated from a business point of view. The AI Anomaly Detection models improve over time using Human-in-the-Loop feedback methods to label data for supervised modeling.
Finally, The Kavi team overviews the Anomaly Lifecycle: from statistical outlier to validated business fraud for reclaim and business process changes to long term prevention strategies using proactive audits upstream at the time of estimate to prevent revenue leakage. Two client success stories will be presented acros Pharmaceutical Rx and Transportation industries.
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sector has been at the forefront of adopting AI and
machine learning technologies. AI has enabled these
industries to automate processes, reduce costs, and
improve the customer experience. With the advent of
digitization and the increasing amount of data available,
banking, financial services, and insurance companies have
been leaders in using AI and machine learning.
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• New Mexico: 7 - This is 10.6% of all NM cases.
• Kansas: 1 - This is 2.7% of all KS cases.
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Credit Card Fraud Detection: Leveraging Advanced Techniques
- 2. Project Overview: BlockerFraudCompany specializesindetecting fraudinmobilefinancial transactions. Thecompanyaimsto expandin Brazil,leveraging anaggressive pricingstrategy basedontheirfrauddetection accuracy.Theprojectseeks to develop arobustmachinelearningmodelto accuratelypredictfraudulent transactions. Thismodel willbedeployed viaAPIfor real-timeclassificationof customertransactions, ensuringenhancedfrauddetection capabilitiesand supportingthe company'sexpansion •Objective:Developamachinelearningmodeltodetectfraudulenttransactions. •Company:BlockerFraudCompany •Expansion:TargetingtheBrazilianmarket •Method:UsingarobustmachinelearningmodelanddeployingviaAPI.
- 3. Here'sastep-by-stepbreakdown: DataExploration ConductthoroughExploratory DataAnalysis(EDA): •LoadthedatasetintoaJupyter notebook. •Uselibrarieslikepandasfor datamanipulationandmatplotlib orseaborn forvisualization. •Explore basicstatistics andidentifyanymissingvalues. •Visualize thedistributionof key features andidentifypatterns in fraudulentvs. non-fraudulenttransactions.
- 4. FeatureEngineering Createrelevant features: • Derivenewfeaturesthatmighthelpindetectingfraud. • UsedomainknowledgeorinsightsfromEDA tocreatethesefeatures.