Financial Services

Machine learning has a powerful impact on financial sectors including banks, brokerage firms, real estate agents, credit unions, and others. The financial community faces challenges such as understanding client’s behavior and expectations, regulatory requirements, fraud, risk management, and more. SpeedWise® Machine Learning (SML) can help companies gain useful insights and forecasts from their data to assist with these challenges, optimize business processes, and maintain better customer relationships.

Use Cases

Unusual Transaction Amounts

Machine learning is effective and crucial for fraud detection regarding credit card payments. Our algorithms can examine historical payment data for thousands of users at once. SML can use customer and transaction data to detect anomalies for suspicious or unusual transaction amounts with a high level of accuracy.

Credit Worthiness

Customers’ past purchasing habits, payment history, and trends can be analyzed with the use of SML to determine credit worthiness or risk scores. This can help financial institutes determine insights such as if a customer is at risk for defaulting on their loans, or how much credit a customer should be given based on historical data.

Stock Market and Trading

SML’s algorithm can be used to analyze patterns in large volumes of market and stock data. These patterns can help users forecast insights that will lead to decisions on whether they should sell, hold, or buy stocks.

Managing Customer Data

Banks can collect massive amounts of data from customers. This data can be used to predict customers behavior and purchasing decisions. For example, bank representatives can use data on credit history, personal preferences, spending habits, and more to recommend the right products. This can help drive revenue and improve the banks relationship with clients.