bankingciooutlook

Banks Should Resolve Explainability and "Black Box" Risk Governance Challenges to Succeed with AI Post-Pandemic, Remarks Economist Intelligence Unit Report Supported by Temenos

By: Banking CIO Outlook | Tuesday, September 08, 2020

Data bias, “black box” risk, and insufficient human oversight are the main governance obstacles for banks using AI, as per the Economist Intelligence Unit (EIU) report

FREMONT, CA: Data bias, “black box” risk, and insufficient human oversight are the main governance obstacles for banks using AI, as per the Economist Intelligence Unit (EIU) report “Overseeing AI: Governing artificial intelligence in banking”. The report is based on a review of global regulatory guidance on AI risks and governance in banking carried out by the EIU on behalf of Temenos (SIX: TEMN), the banking software company.

The report trends are scheduled to be discussed on the webinar “Rules of the game changer – governing AI in banking” on 23 July, with TSB Bank, CWB Financial Group, and Temenos.

The report emphasizes that AI is a top priority for technology investment for banks and shows that 77 percent of banking executives believe that AI will separate winning from losing banks. AI is expected to bolster its importance after the pandemic as banks look upto new technologies to help them adapt to changing customer needs and compete with new market entrants. The EIU report reveals that ensuring fair, ethical, and well-documented AI-based decisions will be vital for banks deploying AI technology.

The EIU report put emphasis on key governance challenges and distills regulatory guidance for banks using AI, including:

Ethics and fairness

Banks need to develop AI models that are ‘ethical by design’. AI use cases and decisions should be closely monitored and reviewed, and data sources regularly evaluated to confirm that data remains representative.

Explainability and Traceability

Steps taken to develop AI models should be documented to explain AI-based decisions to the individuals they impact completely.

Data Quality

Bank-wide data governance standards need to be established and applied to make sure data accuracy and integrity and avoid bias.

Skills

Banks must make sure the right level of AI expertise across the business to build and maintain AI models, as well as oversee these models.

PremaVaradhan, Chief Product Architect and Head of AI, Temenos, remarked, “AI is changing the face of the banking industry. It gives banks the ability to process more data in real time, and learn from customer behaviors, helping them to bring operating costs down and hyper-personalize their services. Banks are using AI to transform their customer experiences and back-office operations so ensuring that the technology is deployed ethically is more important than ever. “White box” models, like Temenos’ Explainable AI (XAI), can explain in simple human language how decisions are made and win the trust of regulators and customers alike. As the custodians of customer data and trusted advisors, banks have a responsibility to adopt transparent, explainable AI technology – those that do stand to gain the competitive advantage in the new normal.”

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