Today, banks are under siege from a horde of fintech companies who are more nimble than the large financial institutions. But the big banks have deeper expertise and more data, and they can win by harnessing their data and leveraging the power of artificial intelligence (AI) and machine learning to reduce costs, increase revenues, and more efficiently comply with regulations.
Syncrasy’s Data Discovery Platform provides a powerful enterprise-ready data integration, curation, data warehousing, visualization and analytics platform to underpin AI initiatives.
DataRobot’s Automated Machine Learning platform empowers existing business teams to solve analytic problems rapidly. From prospecting new corporate customers to more nimble model risk management, the banks that leverage automated machine learning will be the ones that survive the Fintech invasion.
Syncrasy, DataRobot AI and Banking
As an ever increasing number of fintech companies make an already competitive market even more so, banks are being forced to look for ways to improve the effectiveness and efficiency of their business. Syncrasy and DataRobot AI helps banks improve their bottom line.
The tip of the iceberg
Machine Learning and Artificial Intelligence technologies are only “the tip of the data processing iceberg”.
The deep substance is what is below the water line.
Before Machine Learning and Artificial Intelligence technologies can usefully be deployed, enterprises need to access, curate and store all of their internal and external data sources, including previously disregarded data.
Unfortunately, for most of large enterprises meaningful raw data tends to reside in line of business systems across multiple business units, and often with overlapping and conflicting information. In addition, legacy databases, ETL and storage systems are not technologically consistent with Machine Learning and Artificial Intelligence technologies due to an explosion of data types and volumes, as well as the commercial requirement for actions based on real-time information.
To be able to think about deploying Machine Learning and Artificial Intelligence enterprises must first establish “the deep substance” - the underlying databases and applications capable of delivering the data to Machine Learning and Artificial Intelligence processes together with the computing and storage architectures to support it.