- Temenos taps into explainable artificial intelligence, which is integral to its new partnership with Canadian Western Bank.
- And other banks could use similar applications to correct bias in traditional AI models and complement the customer experience.
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The banking software company is teaming up with Canadian Western Bank (CWB) to provide its new Temenos Virtual COO solution to small and medium-sized businesses (SMBs).
The product is built on top of Temenos’ omnichannel digital banking platform and utilizes explainable AI (XAI) and analytics to support financial decision-making at SMBs. By aggregating banking and business data, SMBs are able to assess their current and projected financial health through the use of XAI-powered models that simulate different business scenarios.
Banks could utilize XAI technology to rectify the black box problem associated with traditional AI models used in banking.
- What is the black box problem?: The machine learning algorithms that underpin typical AI-based conclusions are opaque and difficult to explain, especially when concerning more granular inputs. The less-than transparent structure of such models has led to a perceived influence of AI on biases in certain financial decision-making processes—in some cases, presenting potential public relations and legal issues for banks. For example, Goldman Sachs found itself in hot water last year when it was alleged that the Apple Card’s creditworthiness algorithm extended more generous credit lines to men than women.
- How can explainable AI fix it?: XAI presents its decision-making process in transparent and human-readable language, which helps identify its impact and any potential biases. In October 2020, Insider Intelligence recommended that banks should make use of XAI technology for the following reasons: It can clearly explain to the client why they received the decision they did, gain better insight on whether there are any flaws in the AI tech’s decision-making process, and more easily show regulators that its credit decisions aren’t discriminating due to accidental biases. But adoption among banks is low, as integrating the relatively new technology into a bank’s core platform is likely a time-intensive process, especially in countries like the US that don’t have standardized data sharing regulations.
While a powerful tool in terms of generating financial insights, banks should use XAI to complement their existing interactions with customers—not replace them.
Many businesses still value receiving in-person financial advice and use a relationship manager as their primary point of contact with their bank. Face-to-face meetings will likely continue to be prioritized for important issues, but banks could use XAI to better support day-to-day operations, such as documentation, loan applications, as well as digital account openings: Temenos launched several XAI models last year that address these functions.
By employing XAI to streamline procedural bottlenecks and model potential economic scenarios, banks that prioritize the relationship-based experience could help empower business owners to make financial decisions they are confident in and understand.
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