Strategic Predictions: Generative AI in Financial Institutions

We believe that GenAI will have a significant impact on productivity in the areas of general communication, customer satisfaction and dealing with technical debt. To fully realise the potential and value of Gen AI, we see the need for financial institutions to upskill their organisations. This will enable them to handle the quality assurance responsibilities and credibility of Gen AI challenges and outputs.

Financial institutions can unlock their promise of increased productivity by addressing the prerequisites and dilemmas associated with Gen AI implementation and still ensure human oversight. Keeping a human in the loop will increasingly be important to maintain ethical decision-making.

Certain prerequisites need to be addressed to fully leverage the benefits of Gen AI in improving communication and customer satisfaction.

Update communication practices

Financial institutions should start by identifying areas where their current communication practices fall short. The goal should be to understand how Gen AI can bridge the gaps and enhance communication effectiveness.

Establish Sandboxes

To explore further, they will need to create safe learning environments, such as sandboxes, as this becomes crucial for testing and mastering Gen AI tools. Sandboxes allow employees to experiment and become proficient in utilising Gen AI. This will foster creativity and innovation, encouraging employees to explore new ways of incorporating Gen AI into their communication processes and toolsets.

Experimenting and training

Developing a workforce that comprehends and critically evaluates Gen AI outputs is of the essence. Hence, employees should receive training to understand the capabilities and limitations of Gen AI. This will enable them to assess the outputs generated by the technology in alignment with the financial institution's values, ethical standards, and governance policies. A human in the loop will be essential in some Gen AI applications.

To mitigate the risk of overreliance on Gen AI, employees need to understand the tool's capabilities deeply and be trained to think critically and make ethical decisions when utilising Gen AI outputs. Employees can effectively leverage Gen AI to enhance communication while maintaining content and communication credibility by improving critical thinking and ethical decision-making skills.

Many financial institutions face the challenge of technical debt, which can hinder progress and is a growing risk and cost. Technical debt refers to the accumulated consequences of suboptimal design and decisions made during the software development process. It can manifest in various forms, such as outdated code, inefficient systems, and inadequate documentation.

Gen AI can explain old code and frameworks, highlighting potential pitfalls and suggesting improvements. This empowers developers to make informed decisions when working with legacy code, leading to enhanced maintainability and more security. This helps reduce technical debt and enhances the overall performance and stability of the software systems. GenAI can also assist in streamlining code production processes. Automating repetitive tasks and suggesting best practices enables developers to focus on more critical aspects of their work. This works especially well with boilerplate and standardised code.

As discussed in the previous section, the risk of overreliance on Gen AI and the trade-off between automation and human expertise is crucial. Developers need to understand how the tools work and know its limitations. Quality control and code review should be done by other developers, and automated code review tools should be in the pipeline.

Gen AI brings new requirements and challenges to the organisation

As AI becomes more integrated into financial institutions, there is a need to balance existing roles with new responsibilities. As the knowledge, familiarity and capability to interact with Gen AI tools increase, your organisation must consider what structural elements must be introduced to foster and govern the growth of Gen AI capabilities and threats.

Due to the growth in misinformation, we see increased costs and resources needed to handle regulatory pressure and attack surface expansion. We believe we will see a new set of corporate leaders with specialised responsibilities and roles, such as Chief Data Officer and Chief Generative AI Officer.

Financial institutions must define these roles and ensure they have the authority and resources to fulfil their responsibilities effectively.

Conclusion

In conclusion, the strategic implementation of Generative AI in financial institutions holds great potential for transforming the industry. By leveraging Gen AI, financial institutions can enhance communication, improve customer satisfaction, and address technical debt. However, to fully realize the benefits of Gen AI, it is crucial for financial institutions to address the dilemmas associated with workforce reliance and to redefine roles.

Financial institutions must adopt a collective approach and provide the necessary training and upskilling to navigate these challenges. By doing so, they can responsibly integrate Gen AI into their operations, ensuring customers and employees achieve their goals with higher satisfaction levels.

As we move forward, it is essential for financial institutions to remain vigilant in maintaining human oversight. By fostering a culture of continuous learning and critical thinking, financial institutions can harness the full potential of Gen AI while upholding their values and ethical standards.

By embracing this technology and addressing the associated challenges, financial institutions can pave the way for increased productivity and improved customer experiences, contributing to sustainable growth in the industry.

Strategic Predictions: Generative AI in Financial Institutions

Generative Artificial Intelligence (Gen AI) holds immense potential for transforming the banking and financial industry.

Gen AI brings new requirements and challenges to the organisation

Conclusion

Sondre Strande Dahl

Data Analyst

Christer Løvaas

Head of Business Design

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