Jacob Kosoff is the head of model risk management and validation at Regions Bank. The opinions expressed in the presentation are statements of the authors’ opinion, are intended only for informational purposes, and are not formal opinions of, nor binding on Regions Bank, its parent company, Regions Financial Corp. and their subsidiaries, and any representation to the contrary is expressly disclaimed.
Many banks buy modeling tools from vendors to help detect new areas of risk, fraud and profitability without realizing their existing systems are capable of doing the same.
Many new artificial intelligence and machine learning methods used for underwriting are not fully equipped to predict defaults.
Banks in the U.S. should take note of these requirements before opening their systems to third-party developers.
Financial institutions need to be aware of the platform model, strategic and reputational hazards that come with granting access to their networks.
Granting a third party access to a bank's systems in exchange for more advanced technology can help prevent fraud, but it can also attract cyberattacks.
Still trying to figure out whether to build gateways to your bank's data and systems? Here’s why you should transition.
There are several steps bankers can take to help create an environment that supports women in tech, an area historically dominated by men.
Banks need to mitigate potential bias in algorithmic predictive models using artificial intelligence, as regulators are weighing how to oversee the emerging technology.
Banks need to foster greater knowledge and collaboration across all employee teams to put artificial intelligence to good use.
There are a number of actions financial institutions should take to improve their application of machine learning and data analytics technologies.