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. While a powerful tool in terms of generating financial insights, banks should use XAI to complement their existing interactions with customers--not replace them.

Although we are still in the infancy of the AI revolution, there's not much artificial intelligence can't do. From business dilemmas to societal issues, it is being asked to solve thorny problems that lack traditional solutions. Possessing this endless promise, are there any limits to what AI can do? Yes, artificial intelligence and machine learning (ML) do have some distinct limitations. Any organization looking to implement AI needs to understand where these boundaries are drawn so they don't get themselves into trouble thinking artificial intelligence is something it's not.

The development of neural networks is not a new thing. In fact, neural networks have been around since the 1940s, according to MIT News. No one has really been interested in the application of this technology until now. To begin, let's define a neural network. According to the definition by Investopedia: "A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. Neural networks can adapt to changing input; so, the network generates the best possible result without needing to redesign the output criteria."