Banks must resolve explainability and “black box” risk governance challenges to succeed with AI post-pandemic, says Economist Intelligence Unit report supported by Temenos
- Artificial Intelligence , Banking , Data , Risk Management
- 16.07.2020 08:23 am
Data bias, “black box” risk, and lack of human oversight are the main governance issues for banks using AI, according to 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 will be discussed on the webinar “Rules of the game changer – governing AI in banking” on 23 July, with CWB Financial Group, TSB Bank and Temenos.
The report highlights that AI is a top priority for technology investment for banks and reveals that 77% of banking executives believe that AI will separate winning from losing banks. AI is expected to retain its importance after the pandemic as banks look to new technologies to help them adapt to changing customer needs and compete with new market entrants. The EIU report reveals that ensuring ethical, fair and well-documented AI-based decisions will be vital for banks deploying AI technology.
The EIU report highlights key governance challenges and distils regulatory guidance for banks using AI, including:
• Ethics and fairness: banks must develop AI models that are ‘ethical by design’. AI use cases and decisions should be monitored and reviewed and data sources regularly evaluated to ensure that data remains representative.
• Explainability and traceability: steps taken to develop AI models must be documented in order to fully explain AI-based decisions to the individuals they impact.
• Data quality: bank-wide data governance standards must be established and applied to ensure data accuracy and integrity and avoid bias.
• Skills: banks must ensure the right level of AI expertise across the business in order to build and maintain AI models, as well as oversee these models.
Prema Varadhan, Chief Product Architect and Head of AI, Temenos, commented: "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.”
The EIU review cites data bias that leads to discrimination against individuals or groups of people as among the most prominent risks for banks using AI. Commenting in the EIU review, Prag Sharma, Senior Vice President, Citi Innovation Labs, said: “Bias can creep into AI models in any industry, but banks are better positioned than most types of organizations to combat it. Maximizing algorithms’ explainability helps to reduce bias.”
Pete Swabey, Editorial Director EMEA – Thought Leadership, The Economist Intelligence Unit, said: “AI is seen as a key competitive differentiator in the sector.