In financial services, the competition for customer share is intense with corporations searching for any marketing advantage while countering fraud, money laundering and other challenges. Increased customer satisfaction and loyalty, reduced fraud, and reduced regulatory fines benefit businesses that make successful use of AI. For a variety of situations, including fraud detection and credit risk scoring companies use AI.
When lending money or supplying credit, banks and credit card firms use credit scores to determine potential risk. Traditional credit scoring uses a form of scorecard that weighs different variables, including payment history, debt pressure, credit history length, credit types used, and recent credit inquiries. This conventional approach is focused on large segments and, without taking into account their current condition or other extenuating factors, would refuse credit to customers.
In addition to older credit history, AI is a fantastic credit scoring solution that uses more data to include an individualized credit score based on variables like current income, job opportunity, recent credit history, and willingness to gain. This more precise and individualized approach enables banks and credit card companies to analyze each borrower more efficiently and enables them to offer loans to individuals that would have been refused under the scorecard system, including individuals with income potential, such as recent college graduates or temporary foreign nationals.
At Cyber Chasse Inc., the goal is to democratize AI for everyone so that more individuals across sectors can use AI’s power to solve business and social problems. With strong alliances with leading brands, the financial services market is a key focus for the company.
Cyber Chasse enables teams in data science to scale by significantly increasing the pace of building highly precise predictive models. Innovative features of special interest to financial services are integrated including machine learning interpretability, reason codes for individual predictions, automatic modeling of time series etc.