Blocker Fraud Company
The Blocker Fraud Company is a company specialized in detecting fraud in financial transactions made through mobile devices. The company has a service called “Blocker Fraud” in which it guarantees the blocking of fraudulent transactions.
And the business model of the company is of the Service type with the monetization made by the performance of the service provided, that is, the user pays a fixed fee on the success in detecting fraud in the customer's transactions.
However, the Blocker Fraud Company is expanding and to acquire customers more quickly, it has adopted a very aggressive strategy. The strategy works as follows:
- Receive 25% of the value of each transaction that is truly detected as fraud
- Receive 5% of the value of each transaction detected as fraud, but the transaction is truly legitimate.
- Refund 100% of the value to the customer, for each transaction detected as legitimate, however the transaction is truly a fraud.
With this aggressive strategy, the company assumes the risks of failing to detect fraud and is remunerated for assertive fraud detection.
For the client, it is an excellent business to hire the Blocker Fraud Company. Although the fee charged is very high on success, 25%, the company reduces its costs with fraudulent transactions detected correctly and even the damage caused by an error in the anti-fraud service will be covered by the Blocker Fraud Company itself.
One of the most important responsibilities that a bank or financial institution has is to protect the integrity of the institution by working hard to protect the financial assets that it holds. In order to do so, the bank or financial institution must be certain to address the issue of bank fraud. Bank fraud can be defined as an unethical and/or criminal act by an individual or organization to illegally attempt to possess or receive money from a bank or financial institution. The full text can be found here
- CRISP Methodology aproach
- Machine Learning (Random Forest,XGBoost, Linear Regression)
- Data Visualization
- Predictive Modeling
- CrossValidation
- FineTuning by Random Search
- Feature Selection by Boruta
- python
- pandas, jupyter
- boruta, numpy
- sklearn, pickle
- seaborn
-Importing data and libraries -Creating helper functions -Describing data -Feature engineering -Variable Filters -Exploratory data analysis -Data Preparation -Feature Selection -Machine Learning Modeling -Hyperparameter and finetuning -Error interpretation & analisys -Deploying model to production
- My linkedin contact you can get here.
- Feel free to contact me with any questions or if you are interested in contributing!