Machine Learning in Banking
Banks are using machine learning to increase top and bottom line through gaining competitive advantages, reducing expenses, and improving efficiencies. They are optimizing all areas of their business from risk analysis and fraud detection to marketing, in order to make data-driven decisions that lead to increased profitability.
Use cases in Banking
Blockchain
As a relatively new financial system, blockchain is particularly vulnerable to security threats. Build and deploy machine learning algorithms that can detect anomalous behavior anywhere along the chain.
See the use caseCredit Card Fraudulent Transactions
The cost of credit card fraud is billions of dollars per year. By accurately predicting which transactions are likely fraudulent, banks can significantly reduce these illegal transactions while providing card holders an excellent customer experience.
See the use caseCredit Default Rates
Individuals or businesses often need loans. Making accurate judgments on the likelihood of default is the difference between a successful and unsuccessful loan portfolio.
See the use case