"Development to deployment of Fraud Detection Infrastructure", Speaker: Awantik Das: Co-Founder - edyoda (https://www.linkedin.com/in/awantik/) Talk high level summary: Credit card fraud detection is heavily used application in banks & financial institutions. In this workshop, the entire infrastructure of building complete system of credit card fraud detection will be discussed. It will include Kafka for getting transaction records, building & deploying machine learning models for this application. Finally, storing the predictions in cassandra will be explored. It will be a complete hands-on session and would also include bit of introduction of Cassandra and Kafka. Presented as part of the AI & ML meetup in NVidia: https://www.meetup.com/Bangalore-AI-ML-Meetup/events/262438475/

1. Credit Card Fraud Detection
By Awantik Das, Edyoda

2. What is Credit Card Fraud Detection
● It is important that credit card
companies are able to recognize
fraudulent credit card transactions so
that customers are not charged for
items that they did not purchase.
● Similar application is used in credit
providers end as well.

3. Objective

4. Technology
Stack
● Cassandra
● Scikit / TensorFlow /
PySpark
● Kafka
● Flask

5. Kafka
● Horizontally, distributed & scalable
streaming platform.
● Highly Available
● Commodity hardware

6. Cassandra
● Horizontally, distributed scalable storage.
● Highly available
● Commodity Hardware

7. Solution

8. Understanding Dataset
● This dataset presents transactions that occurred in two days, where we have 492
frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive
class (frauds) account for 0.172% of all transactions.
● Due to confidentiality issues, we cannot provide the original features and more
background information about the data. Features V1, V2, ... V28 are the principal
components obtained with PCA, the only features which have not been
transformed with PCA are 'Time' and 'Amount'.
● Feature 'Time' contains the seconds elapsed between each transaction and the
first transaction in the dataset. The feature 'Amount' is the transaction Amount,
this feature can be used for example-dependant cost-sensitive learning. Feature
'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.

9. Study Materials
● Complete Data Science Code - https://github.com/zekelabs/data-science-complete-tutorial
● Credit Card Project Code - https://github.com/zekelabs/ai-project-fraud-detection
● Credit Card Project Videos - https://www.edyoda.com/course/1432
● More AI courses - https://www.edyoda.com/category/artificial-intelligence

10. Edyoda Journey

11. Contact
Awantik Das
awantik@edyoda.com
www.edyoda.com