Financial services can win with AI by putting trust first
IBM’s May 27 Data and AI Virtual Forum puts an industry’s re-invention in focus
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 improving business performance with end-to-end intelligent automation and radical differentiation with hyper-personalized services.
Those on the fast-track to compete with new cloud-native contenders are set to deploy AI models to accelerate their adaptation amidst post-pandemic client expectations.
Register here for the May 27 Data and AI Forum focused on financial services to learn how to adopt and infuse AI responsibly to build on trusted governance and transparency with well-defined guidance that brings together privacy and security.
IBM’s PoV: Gain a trust advantage
As financial services organizations aim to reinvent themselves, IBM asserts that a trusted architecture based on data and AI solutions is the foundation for both end-to-end digitization of core operations and the deployment of new digital services.
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 data quality issues are the number one challenge in their organization.
While many have identified trust as one of the potential impediments to success with AI, IBM has gone further to outline the need to establish trust in three areas for any organization to compete and deliver lifetime client value while staying compliant.
- 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: 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 that’s based on trusted platform advantages.
During the Data and AI Forum’s keynote session IBM’s Global Chief AI Officer IBM, Seth Dobrin, Ph.D., will outline the four principles of governed data and AI technologies within an open and diverse ecosystem.
He will also share actions IBM has taken to participate in 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 the IEEE Trusted Data and Artificial Intelligence Systems (AIS) Playbook for Financial Services.
This resource provides practical tools based on 20 high value use cases, plus ethical concern that appear in each.
A follow-on discussion with Forrester principal analyst Brandon Purcell and experts from UBS, State Bank of India (SBI) and Regions Bank will cover how to put trust in data, AI, processes, business models into practice at scale
Then, choose among tracks that cover how FSS can best harness data and AI with sessions featuring Associated Bank, Citi, ING, CAIXA, CIBC, KPMG and TSB.
- Managing risk and compliance by identifying new patterns and mitigating emerging threats while also eliminating the manual process of reviewing charts and spreadsheets.
- Ensuring your AI is trustworthy by automating model lifecycle governance.
- Unlocking the value of data to navigate market disruption and drive business agility.
- Optimizing workflows to deliver superior customer experiences, support credit underwriting, loan and credit card default management and claims adjustments.
- Scaling AI quickly across any on-premises or hybrid cloud environment to advance insurance claims processing, customer segmentation and enterprise banking.
Learn how your organization can use data and AI to gain a competitive advantage by reducing risk, unlocking the value of data, optimizing workflows and scaling for reinvention.
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