Financial institutions are playing a numbers game in the core of their business: they evaluate risks and opportunities of potential transactions and if the perceived benefits outweigh the associated risks they will agree to the deal. In the case of traditional banking, this is true for the credit approval process, whereas in the case of insurance companies, the correct evaluation of risk factors determines the conditions of an insurance policy. Like weather forecasts demonstrate, assessments of future events like risk factors or return expectations may occasionally be wrong.
Artificial Intelligence (AI)
Discussions about conversational AI are ubiquitous these days and virtual or cognitive agents, such as chatbots and the like, are at the forefront.
Robo-advice, also known as ‘automated advice’ refers to the provision of financial advice with as little human interaction as possible. A strand of artificial intelligence, robo-advice offers guidance on the basis of mathematical rules and algorithms rather than human intelligence. Whilst algorithmic trading may have been around for many years, the concept of a ‘robo-adviser’ has only recently become a reality.
Picture the scene: you’re in the middle of hiring for a role in your HR department. At the interview stage, some bright young candidate takes a seat. You ask the classic question, “So why should I hire you?”
Whether you crossed the automation chasm early, or are just starting your journey into digital service delivery, you’re not alone if you’ve got questions surrounding technical ecosystem choices. Generating ROI—and how exactly to go about proving the value of numerous small automation projects versus one giant re-platforming initiative—is a common challenge.
Open vs. Closed Ecosystems