Financial services: Trustworthy AI’s promise and payoff
IBM, Forrester, UBS, Regions Bank, and State Bank of India show how to build AI responsibly
Data: It’s becoming richer, cheaper, and increasingly open in the post-pandemic world of broad digitalization. And for financial services organizations, this windfall is opening pathways to improve business performance with end-to-end intelligent automation and hyper-personalized services to satisfy customers’ expectations.
As data gets cheaper, trust grows in value
For banks, insurance companies and other financial institutions on a quest for reinvention, the pressure to play catch up, or leapfrog means fast-tracking AI deployments while balancing governance, risk and compliance needs.
According to a January 2020 Forrester Consulting study commissioned by IBM, Overcome Obstacles to get to AI at scale, 40 percent of the participants report data governance issues are a serious concern. Additionally, 58 percent of its participants indicated that data quality issues are the number one challenge in their organization.
Now the good news.
A trusted architecture based on data and AI solutions generates higher business value, according to Brandon Purcell, Vice President and Principal Analyst at Forrester who spoke at a recent Data and AI Virtual Forum keynote, Trustworthy AI: Forging the future of banking, insurance and financial markets.
Invest in building trustworthy AI: How to get started
At IBM, we haven’t merely identified this challenge, we’ve taken concrete steps to help organizations build what we call trustworthy AI. This means they can stay compliant while going for gold.
First, watch the recent IBM Data and AI Virtual Forum keynote to hear from IBM’s Global Chief AI Officer IBM, Seth Dobrin, Ph.D. on the principals of governed data and AI within an open and diverse ecosystem.
Next, keep watching to learn how trustworthy AI is put into practice. During the keynote, leaders from UBS, State Bank of India (SBI), and Regions Bank shared how they applied three dimensions of trust and how these areas of focus work together to deliver lifetime client value for each organization.
- Trust in data: How to design a digital business from end-to-end having data domains, data governance, data lineage in mind.
- Trust in AI models: How to ensure adequate risk management of AI models and business-focused governance to better foster collaboration at the crossroads between intelligent, exponential technologies and people.
- Trust in processes and business models: How to increase business productivity by infusing AI based on trusted platform advantages.
Finally, download the IEEE Trusted Data and Artificial Intelligence Systems (AIS) Playbook for Financial Services. This document was the result of an effort led by the world’s largest technical organization, IEEE, to formulate guidance for financial services as they develop new operating models around trusted data and AI ethics. Insights from IBM and other industry contributors aided in the development of this resource. It provides practical tools based on 20 high-value use cases, plus ethical concerns that appear in each.
Financial services can adopt and infuse AI responsibly to build on trusted governance and transparency with well-defined guidance that brings together privacy and security.
Learn more about how to take advantage of a trusted architecture based on data and AI solutions — the foundation for both end-to-end digitization of core operations and the deployment of new digital services.