Using Machine learning in the banking industry is more than a trend, it has become a necessity to keep up with the competition. Banks have to realize that big data technologies can help them focus their resources efficiently, make smarter decisions, and improve performance. Here is a list of Machine learning use cases in banking area which we have combined to give you an idea how can you work with your significant amounts of data and how to use it effectively.
- 1. Predict Default Rate
- 2. Financial Crime
- 3. Cash Management
- 4. Marketing
ScoopML can be used to estimate default probability, loss severity, and for loss forecasting, using past client behavior data. These predictions improve pricing for risk, credit approval, and portfolio management. ScoopML also automatically updates, making credit scoring more precise as models learn the nuances of discrete populations.
Fighting financial crime, especially money laundering and fraud, is more important than ever and is getting more challenging as criminals get ever more sophisticated. Using ScoopML banks can use their historical fraud data to accurately predict and detect suspicious activity and fraud in real time. These predictions continue to get better over time.
ScoopML can predict new loan demand, prepayment speed, and ATM cash requirements using historical data on cash inflows and outflows to improve cash management. This allows banks to have the right amount of cash on hand where and when they need it and to optimize the return on excess cash
ScoopML can predict which prospects are likely to become the most profitable clients allowing banks to prioritize leads and referrals. Banks can also gain insights for targeting their offers more precisely, predict client price sensitivity, tailor their value proposition, and estimate price-volume elasticity.