The use of machine learning for credit decisioning is being driven by a desire to make smarter decisions that lead to greater expansion - and greater profits.According to our research, 90% of financial institutions believe successful big data initiatives dictate the winners of the future. Yet only 37% are actually using big data initiatives in their operations.
In the post-pandemic world, lending executives need to understand the credit decisioning concepts that drive their business and its profitability.
Our new executive briefing entitled: Is Your Credit Decisioning Right for a Changed World provides vital information that will help you better understand new tools and technologies that will enable you to make better decisions and will lead to better performance.
The report is written for non-technical decision-makers providing insights on the following:
- How to evaluate the use of machine learning and whether these models are using enough data to make good risk decisions.
- The 7 key questions you should be asking those data scientists you hired, who can make or break your business performance.
- The 10 tools that are essential to ensure that scorecards are built, maintained and tracked properly.
This informative report is written by Murray Bailey, who literally wrote the book (actually two books) on modern credit decisioning.
