Payment Risk and Payment Fraud: Data Science and Analytics

Focused View

Sean Li

8:43:38

32 View

1. Introduction

1. Introduction.mp4

04:00

2. Course Outline.mp4

02:39

2. Payment

1.1 Payment Overview v5.pptx

1. Payment Overview.mp4

05:24

2.1 Payment -- Card V5.pptx

2. Card Transactions.mp4

09:55

3.1 Payment -- ACH v3.pptx

3. ACH Transactions.mp4

06:04

4.1 Payment -- Chargeback and Refund v2 (1).pptx

4. Chargeback and Refund.mp4

08:34

3. Payment Risk

1.1 Payment Risk v3.pptx

1. Payment Risk Overview.mp4

04:59

2.1 Consumer Risk - ATO v3.pptx

2. ATO Introduction.mp4

08:28

3.1 How to identify ATO v3.pptx

3. How to identify ATO.mp4

11:27

4.1 Consumer Risk - SF&NSF v3.pptx

4. Stolen Financial and NSF overview.mp4

07:51

5.1 How to identify SF and NSF v3.pptx

5. How to identify SF and NSF.mp4

10:57

6.1 Family Fraud 101.pptx

6. Family Fraud Overview.mp4

02:51

7.1 How to identify Family Fraud.pptx

7. How to identify Family Fraud.mp4

03:19

8.1 Merchant Risk Overview v3.pptx

8. Merchant Risk Introduction.mp4

04:26

9.1 How to identify merchant risk v3.pptx

9. How to identify merchant risk.mp4

10:07

4. Statistic and ML

1.1 Stats Outline.pptx

1. Stats outline.mp4

00:49

2.1 Hypothesis.pptx

2. Hypothesis.mp4

01:21

3.1 Sampling.pptx

3. Sampling.mp4

06:56

4.1 How to calculate sample size.pptx

4. Sample Size Calculation.mp4

02:00

5. Confusion Metrix.mp4

09:56

6.1 ML 101.pptx

6. ML Basic.mp4

04:10

7.1 Linear Regression 101.pptx

7. Linear Regression 101.mp4

03:51

8.1 Linear Regression 102.pptx

8. Linear Regression 102.mp4

04:33

9.1 Linear Regression 103.pptx

9. Linear Regression 103.mp4

01:29

10.1 Linear Regression 104.pptx

10.2 Linear Regression.xlsx

10. Linear Regression 104.mp4

04:34

11.1 Logistic Regression 101.pptx

11. Logistic Regression 101.mp4

06:12

12.1 Logistic Regression 102.pptx

12. Logistic Regression 102.mp4

06:50

13.1 Decision Tree 101.pptx

13. Decision Tree 101.mp4

01:41

14.1 Decision Tree 102.pptx

14. Decision Tree 102.mp4

10:11

15.1 Random Forest 101.pptx

15. Random Forest 101.mp4

01:59

16.1 Random Forest 102.pptx

16. Random Forest 102.mp4

05:34

17.1 Gradient Boosting 101.pptx

17. GBDT 101.mp4

01:19

18.1 Gradient boosting 102.pptx

18. GBDT 102.mp4

06:43

19.1 xgboost 101.pptx

19. Xgboost 101.mp4

01:46

20.1 Xgboost 102.pptx

20. Xgboost 102.mp4

08:14

21.1 Model Testing and Validation.pptx

21. Model testing and evaluation.mp4

03:05

5. SQL

1. How to run SQL in this class.mp4

01:22

2.1 Section 3 SQL Example.xlsx

2. where our sql examples are and how to play with them.mp4

03:20

3.1 SQL - Select.pptx

3. Select.mp4

01:52

4.1 SQL - Select distinct.pptx

4. Select distinct.mp4

02:22

5.1 SQL - Where Clause.pptx

5. where clause.mp4

01:49

6.1 SQL - Group By.pptx

6. group by.mp4

02:32

7.1 SQL - Aggregate Functions.pptx

7. aggregate function.mp4

02:21

8.1 SQL - Max Min Function.pptx

8. Maxmin function.mp4

02:00

9.1 SQL - Having Clause.pptx

9. Having Clause.mp4

02:46

10.1 SQL - Join Clause.pptx

10. Join.mp4

09:56

11.1 SQL - In Operator.pptx

11. In operator.mp4

02:23

12.1 SQL - Not Equal Operator.pptx

12. Not equal operator.mp4

02:42

13.1 SQL - Date Function.pptx

13. date function.mp4

02:55

14.1 SQL - case statement.pptx

14. case when statement.mp4

02:52

15.1 SQL - Cast.pptx

15. cast function.mp4

02:03

16.1 SQL - Limit and Offset Clause.pptx

16. Limit and offset function.mp4

03:37

17.1 SQL - Window Functions.pptx

17. window function.mp4

08:31

18.1 SQL - Subqueries.pptx

18. Subquery.mp4

07:38

19.1 SQL - Complex Join.pptx

19. Complex Join.mp4

05:00

20.1 SQL - Join and aggregate functions.pptx

20. Join and aggregate functions.mp4

04:01

21.1 SQL - combine having and where.pptx

21. combine having and where.mp4

05:54

22.1 SQL - Duplicates.pptx

22. Duplicates.mp4

05:56

23.1 SQL - nth.pptx

23. Nth number.mp4

07:46

24.1 SQL - Previous Date Record.pptx

24. Previous Daterecord.mp4

07:25

25.1 SQL - Query Efficiency.pptx

25. Query Efficiency.mp4

02:37

6. Python

1.1 Python input, output and import.pptx

1.2 Python.xlsx

1. Python input and output.mp4

01:32

2.1 Python Statement, Indentation and Comments.pptx

2. Python Statement, Indentation and Comments.mp4

04:30

3.1 Python Data Type.pptx

3. Python Data type.mp4

05:21

4.1 Python Function.pptx

4. Python functions.mp4

01:58

5.1 Python Operators.pptx

5. Python operator.mp4

02:50

6.1 Python if..else.pptx

6. Python if else.mp4

03:03

7.1 Python for loop.pptx

7. Python for loop.mp4

05:57

8.1 Python while loop.pptx

8. Python while loop.mp4

03:35

9.1 Python List 101.pptx

9. Python List 101.mp4

07:39

10.1 Python List 201.pptx

10. Python List 102.mp4

10:02

11.1 Python tuple 101.pptx

11. Python Tuple 101.mp4

06:06

12.1 Python Tuple 201.pptx

12. Python Tuple 102.mp4

05:38

13.1 Python Set 101.pptx

13. Python Set 101.mp4

05:41

14.1 Python Set 201.pptx

14. Python Set 102.mp4

08:16

15.1 Python dictionary 101.pptx

15. Python Dictionary 101.mp4

03:39

16.1 Python Dictionary 201.pptx

16. Python Dictionary 102.mp4

05:02

17.1 Python numpy 101.pptx

17. Python numpy 101.mp4

06:42

18.1 Python numpy 201.pptx

18. Python numpy 102.mp4

07:16

19.1 Python numpy 301.pptx

19. Python numpy 103.mp4

08:46

20.1 Python numpy 401.pptx

20. Python numpy 104.mp4

05:42

21.1 Python Pandas 101.pptx

21. Python Pandas 101.mp4

03:12

22.1 Python numpy 201.pptx

22. Python Pandas 102.mp4

10:23

23.1 Python Pandas 301.pptx

23. Python Pandas 103.mp4

10:16

24.1 Python Pandas 401.pptx

24. Python Pandas 104.mp4

12:17

25.1 Python Pandas 501.pptx

25. Python Pandas 105.mp4

10:41

26.1 Python Matplotlib 101.pptx

26. Python matplotlib 101.mp4

01:13

27.1 Python Matplotlib 201.pptx

27. Python matplotlib 102.mp4

04:11

28.1 Python scikit-learn 101.pptx

28. Python scikit-learn 101.mp4

01:25

7. Case study

1.1 First Case Overview.pptx

1.2 Nashville housing house details.xlsx

1.3 Nashville housing sale.xlsx

1.4 Nashville housing seller info.xlsx

1. First Case Study Nashville Housing Overview.mp4

01:51

2.1 Nashville housing thinking process.pptx

2. Thinking process.mp4

01:31

3.1 Nashville housing overall trend.pptx

3. Nashville Housing Overall Trend.mp4

07:05

4.1 Nashville Housing Analysis.xlsx

4.2 Nashville housing details.pptx

4. Nashville Housing analysis.mp4

17:22

5.1 Nashville housing summary.pptx

5. Nashville Housing Summary.mp4

01:56

6.1 Second Case Overview.pptx

6.2 Section 6 Subscription Details Python - customer info.csv

6. Second Case Study Subscription business model analysis.mp4

01:51

7.1 Second Case Overall Business Performance.pptx

7.2 subscription business analysis revenue.xlsx

7. Overall business performance.mp4

09:28

8.1 Second Case Dataframe Analysis.pptx

8.2 Section 6 Subscription Details Python.xlsx

8.3 subscription data analysis.zip

8. Load Subscription business data into dataframe.mp4

06:54

9.1 Second Case Decision Tree.pptx

9.2 subscription data decision tree model.zip

9. Build a decision tree model in Python to improve business performance.mp4

15:04

8. Congratulations

1.1 payment risk ds congratulations.pptx

1. Congratulations!.mp4

01:49

Description

we will learn modeling and coding (SQL/Python) knowledge for data science and data analytics in payment risk

What You'll Learn?

Understand how payment works in general
Understand how fraudsters work, the different payment fraud types and corresponding risky signals
Understand the statistic and ML basics
Understand the SQL basics
Understand the Python basics
Complete one case study to build a decision tree model with Python to solve fraud problem
Complete one case study to

Who is this for?

Beginners who want to start a career in Payment & Payment Risk

Beginners who want to do payment risk and fraud analytics

Beginners who want to do payment risk and fraud data science

Anyone who is passionate about mitigating risk and catch fraud with data

What You Need to Know?

No experiences needed, we will learn everything from this course

More details

Description
Hi, this is Kangxiao, I have many years of working experience with industry leaders like Paypal, Google, and Chime. Throughout my entire career, I have used data to do analysis, build models, and solve key business problems.
When I learn online, I often run into two issues:
The course offers in-depth knowledge, but it doesn't have very broad coverage. In reality, we don't need to be experts for everything. But it will give us a huge advantage if we know the basics for a lot of things.
The course focuses too much on the technical side. I find a lot of the courses focus entirely on either coding like how to write Python codes, or stats like the math behind different kinds of ML models. And there are very few courses that link payment risk/fraud, modeling, and coding together to solve real-world problems.
In the payment and payment risk industry, people have come to the conclusion that we have to rely on data-driven solutions to fight against the bad actors. This makes data science and data analytics super important for payment risk and payment fraud.

Thus, In this course, I want to share my knowledge of data science and analytics in payment risk by offering very broad coverage of payment and payment risk basics, data science, statistics, modeling, and coding, and using case studies to connect data, coding, and stats together. Thatâ€™s exactly what we do in the real world, in our day-to-day work. The best talents I observe in Paypal, Google, and Chime are the ones who are really good at connecting these dots together to solve complicated problems.

I hope this course can help set you ready for your future success in payment and payment risk. Please join us, If any of these interests you. Let's enjoy this journey together!
Who this course is for:
Beginners who want to start a career in Payment & Payment Risk
Beginners who want to do payment risk and fraud analytics
Beginners who want to do payment risk and fraud data science
Anyone who is passionate about mitigating risk and catch fraud with data

User Reviews

Rating

average 0

Total votes0

Focused display

I am a Certified Fraud Examiner. I have an MS in Financial Risk Management from the University of Connecticut and years of experience in data/risk/fraud analysis with market leaders like PayPal and Google in Payments. In my day to day life, I work as a senior risk manager who designs the risk structure/strategies, fights against fraudsters and keeps the payment system safe. Now let me take my knowledge and experience to share with you.All courses come with lifetime access, friendly support if you get stuck and a 30 day money-back guarantee so there's no risk to get started.

Udemy

View courses Udemy

Students take courses primarily to improve job-related skills.Some courses generate credit toward technical certification. Udemy has made a special effort to attract corporate trainers seeking to create coursework for employees of their company.