In a recent post by Velexa, the company highlighted the differences between complexity and function relating to AI in WealthTech.
The landscape of artificial intelligence (AI) is becoming increasingly complex and interconnected, paving the way for remarkable strides in various practical technology applications. The explosive growth in autonomous machine learning and reasoning is set to augment numerous functions once deemed only achievable by humans.
This evolving technological environment has led to a pivotal shift in our approach to system architecture, construction, testing, and auditing. Outdated methods are giving way to innovative solutions such as model-based software development, sophisticated automated testing, and somewhat paradoxically, the application of AI to verify AI-driven systems. Consequently, we find ourselves increasingly reliant on systems whose full workings may escape our understanding.
A crucial dialogue centres on the intricate balance between the complexity of a system and the breadth of function it can effectively perform. Consider autonomous vehicles: while the technology is mature, the risks associated with system failure prevent us from seeing our streets teeming with driverless cars.
In the WealthTech sector, this risk-reward equation is similarly applicable. For instance, the use of Generative AI is unlikely to usurp the role of the Wealth Manager in the near future. Instead, it’s projected to operate as an auxiliary tool – a navigator rather than the driver.
Furthermore, the complexity of use cases is a key determinant of the scope of AI’s application. Much like it’s daunting to entrust an autonomous vehicle with passenger safety, it’s unrealistic to expect AI to fully automate the wealth management process. The more practical approach involves utilising AI to automate certain manual tasks, providing timely, accurate insights from relevant datasets.
Nonetheless, it’s crucial to underscore that the strategic technology plan for most firms will likely feature an AI component. Those who fail to do so may find themselves lagging behind their competitors. The challenge for each firm lies in identifying how Generative AI can enhance each unique process within their operations and assessing the aggregate value of these improvements.
Read the full post here.
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