The aim of this project is to delve into the principles of deep learning, from fundamentals to advanced applications, focusing on fraud detection. The application of deep learning in fraud detection is pertinent in various industries due to its ability to identify complex patterns in extensive volumes of data.
APPLICATION EXAMPLES:
- E-commerce fraud prevention: On e-commerce platforms, fraud such as purchases made with stolen credit cards or compromised user accounts is common. Deep learning models can be employed to analyze purchase and login behavior, learning to identify activities that are inconsistent with the user's usual pattern.
- Fraud Detection in Banking Transactions: Using deep learning models, it is possible to analyze patterns in banking transactions to identify suspicious activity that could indicate fraud. For example, the system can learn to recognize unauthorized transfers or abnormal withdrawals of large amounts of money. By processing large volumes of data, the model can detect deviations from a customer's usual transaction patterns, such as transactions at atypical times or locations.
- Insurance Fraud Prevention: In the insurance sector, deep learning models can be applied to identify fraudulent claims. By analyzing claim histories and claim patterns, these models can highlight cases that deviate from statistical norms, such as excessively frequent claims or significantly high value claims that do not align with the customer's risk profile. This allows insurers to investigate and validate suspicious claims more efficiently.
COMPLETION AND DELIVERY OF THE PROJECT:
All the prototypes generated during the project are delivered at the end of the 10th week.
