Payment Risk And Payment Fraud: Data Science And Analytics

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

Requirements

No experiences needed, we will learn everything from this course

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. That’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!

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Course Outline

Section 2: Payment

Lecture 3 Payment Overview

Lecture 4 Card Transactions

Lecture 5 ACH Transactions

Lecture 6 Chargeback and Refund

Section 3: Payment Risk

Lecture 7 Payment Risk Overview

Lecture 8 ATO Introduction

Lecture 9 How to identify ATO

Lecture 10 Stolen Financial and NSF overview

Lecture 11 How to identify SF and NSF

Lecture 12 Family Fraud Overview

Lecture 13 How to identify Family Fraud

Lecture 14 Merchant Risk Introduction

Lecture 15 How to identify merchant risk

Section 4: Statistic and ML

Lecture 16 Stats outline

Lecture 17 Hypothesis

Lecture 18 Sampling

Lecture 19 Sample Size Calculation

Lecture 20 Confusion Metrix

Lecture 21 ML Basic

Lecture 22 Linear Regression 101

Lecture 23 Linear Regression 102

Lecture 24 Linear Regression 103

Lecture 25 Linear Regression 104

Lecture 26 Logistic Regression 101

Lecture 27 Logistic Regression 102

Lecture 28 Decision Tree 101

Lecture 29 Decision Tree 102

Lecture 30 Random Forest 101

Lecture 31 Random Forest 102

Lecture 32 GBDT 101

Lecture 33 GBDT 102

Lecture 34 Xgboost 101

Lecture 35 Xgboost 102

Lecture 36 Model testing and evaluation

Section 5: SQL

Lecture 37 How to run SQL in this class

Lecture 38 where our sql examples are and how to play with them

Lecture 39 Select

Lecture 40 Select distinct

Lecture 41 where clause

Lecture 42 group by

Lecture 43 aggregate function

Lecture 44 Max/min function

Lecture 45 Having Clause

Lecture 46 Join

Lecture 47 In operator

Lecture 48 Not equal operator

Lecture 49 date function

Lecture 50 case when statement

Lecture 51 cast function

Lecture 52 Limit and offset function

Lecture 53 window function

Lecture 54 Subquery

Lecture 55 Complex Join

Lecture 56 Join and aggregate functions

Lecture 57 combine having and where

Lecture 58 Duplicates

Lecture 59 Nth number

Lecture 60 Previous Date/record

Lecture 61 Query Efficiency

Section 6: Python

Lecture 62 Python input and output

Lecture 63 Python: Statement, Indentation and Comments

Lecture 64 Python: Data type

Lecture 65 Python: functions

Lecture 66 Python: operator

Lecture 67 Python: if else

Lecture 68 Python: for loop

Lecture 69 Python: while loop

Lecture 70 Python: List 101

Lecture 71 Python: List 102

Lecture 72 Python: Tuple 101

Lecture 73 Python: Tuple 102

Lecture 74 Python: Set 101

Lecture 75 Python: Set 102

Lecture 76 Python: Dictionary 101

Lecture 77 Python: Dictionary 102

Lecture 78 Python: numpy 101

Lecture 79 Python: numpy 102

Lecture 80 Python: numpy 103

Lecture 81 Python: numpy 104

Lecture 82 Python: Pandas 101

Lecture 83 Python: Pandas 102

Lecture 84 Python: Pandas 103

Lecture 85 Python: Pandas 104

Lecture 86 Python: Pandas 105

Lecture 87 Python: matplotlib 101

Lecture 88 Python: matplotlib 102

Lecture 89 Python: scikit-learn 101

Section 7: Case study

Lecture 90 First Case Study: Nashville Housing Overview

Lecture 91 Thinking process

Lecture 92 Nashville Housing Overall Trend

Lecture 93 Nashville Housing analysis

Lecture 94 Nashville Housing Summary

Lecture 95 Second Case Study: Subscription business model analysis

Lecture 96 Overall business performance

Lecture 97 Load Subscription business data into dataframe

Lecture 98 Build a decision tree model in Python to improve business performance

Section 8: Congratulations

Lecture 99 Congratulations!

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