Generative AI is rising as a transformative force in the financial sector. Its ability to predict demands, provide customised banking solutions, and identify fraudulent acts in real time is painting a new picture for the banking industry. In its report, McKinsey Global Institute (MGI) revealed that the adoption of generative artificial intelligence in the sector could lead to an estimated growth of 2.8 to 4.7% in its annual revenue, translating from $200 billion to $340 billion. Further, it could increase efficiency and productivity in the domain.
In the current scenario, generative AI development has brought high efficiency in bank-related functioning. It is undeniable that Gen AI presents banks with endless benefits. By scaling up the implementation of AI, banks can unlock a new era of operations—streamline transactions, solve queries, protect customer data, and prevent and address suspicious transactions. However, without strategic planning, scaling it up is quite difficult. Successful Gen AI scale-ups rely on the following factors:
Strategic Framework
Implementation of Gen AI in the banking sector amounts to a significant value for its growth. Hence, its scaling-up demands CEO intervention. Characterising AI as a CEO-level subject energises enterprises and eliminates possible bottlenecks. For instance, leaders of a financial organisation recognised the potential of AI in resolving customer queries and its influence on broadening the prospects for modifying platforms, collaborations, and economics. As an outcome, the organisation is now mindfully adapting to AI transformation. This kind of leadership can help plan out effective roadmaps, priority lists, clear goals, and partnership plans to scale up the gen AI in the banking sector.
Talent Acquisition
The rapid speed of generative AI adoption in the market has left banking seniors shorthanded with little to no time to upskill their employees or hire qualified individuals to keep up. In order to walk parallel with it, seniors need to develop a complete understanding of Gen AI. Moreover, investing in executive training and education will help them show their employees how AI can contribute to the world of banking.
Operating Structure
An operating model represents how a company operates. It consists of its structure, processes, and most importantly, people. Since there is no one-size-fits-all approach here, an essential operating model design that can mould as the organisation evolves is critical for scaling Gen AI. In order to adopt Gen AI, wealth management firms need to come up with a customised operating model, keeping in mind the risks and nuances associated with the new technology.
Risks and Controls
In addition to its ability to increase productivity, generative also presents new challenges. Some of the risks associated with Gen AI include algorithm biases leading to the generation of inaccurate data, potential infringement of patented, or legally protected intellectual properties, heightened privacy concerns, security vulnerabilities, etc. The use of AI in a responsible manner must be incorporated into the roadmap of scaling up. Adjusting large language model parameter settings, such as temperature settings, that manage the randomness of the results and looping in subject matter experts can help in mitigating some of the risk factors.
Adoption and Change Management
When it comes to adopting a new technology, how a financial institution operates can result in either its definite success or definite failure. Even the well-planned strategies can die if the employees and the customers are not trained to use them. Employees will not be able to leverage the tool if they do not understand its functionality and limitations. In today’s world, a successful adaptation of a novel technology requires a shift in perspective, beginning with the end consumers and working backward. It needs a comprehensive understanding and modification in management plans. Such plans must include a clear view of the expected goals, training sessions for employees and executives, defining explicit and implicit incentives for people to use the technology, and most importantly, being transparent and pragmatic.
Wrapping Up
The venture of generative artificial intelligence into the financial world certainly adds value to banks and other wealth management organisations. It presents an opportunity for them to grow as a whole industry by enhancing their productivity and customer satisfaction. However, walking the path of scaling up Gen AI is riddled with challenges and demands effective implementation of strategic planning and moulding existing operating models. As the banking sector navigates this journey, the strategies pointed out here can serve as a guide to align their generative AI innovations with tactical goals for maximum outcomes. It is time for banking institutions to make a push to bring generative AI to market and reap the benefits of this nascent technology.
