Interpretable and resilient AI for financial services
Financial services are increasingly deploying AI services for a wide range of applications, such as identifying fraud and financial crimes. Such deployment requires models to be interpretable, explainable, and resilient to adversarial attacksregulatory requirements prohibit black-box machine learning models. Jari Koister shares tools and infrastructure has developed to support these needs.
| Talk Title | Interpretable and resilient AI for financial services |
| Speakers | Jari Koister (FICO ) |
| Conference | Strata Data Conference |
| Conf Tag | Big Data Expo |
| Location | San Francisco, California |
| Date | March 26-28, 2019 |
| URL | Talk Page |
| Slides | Talk Slides |
| Video | |
Financial services are increasingly deploying AI models and services for a wide range of applications in the credit lifecycle, such as credit onboarding and identifying transaction fraud and identity fraud. These models must be interpretable, explainable, and resilient to adversarial attacks. In some situations, regulatory requirements apply that prohibit black-box machine learning models. Jari Koister shares forward-looking tools and infrastructure has developed to support these needs. Topics include:
