Unleashing the Power of Gen AI: A Practical Guide for Financial Services

Generative AI (GenAI) is poised to revolutionize the financial services industry. Its ability to create new content, automate tasks and personalize experiences presents a wealth of opportunities. But, with so many potential applications, where should businesses begin? 

This article outlines a practical GenAI approach for financial services firms. It will help them to identify high-impact business areas for the technology, ensuring alignment with business goals and a smooth integration for maximum value. 

The application of GenAI needs to be conducted in multiple phases: The first phase should start with a full understanding of all of the information needed to begin the process of identifying the use case. 

Phase 1: Identifying opportunities

• Understand your business pain points and opportunities: Begin by collaborating with business and operations stakeholders across your organization – it’s important to understand their current challenges and frustrations. Are there repetitive tasks that take up valuable employee time, such as generating personalized reports or compliance documents? Is there difficulty in extracting insights from unstructured data, like customer reviews or social media sentiment? For example, wealth management advisors spend a significant amount of their time crafting personalized investment reports for each client. This process is time-consuming and limits their ability to focus on client relationship building. The process could be run in two steps: The first step would be to run an awareness campaign about the initiative and encourage the lines of business to submit their challenges and/or potential areas of opportunity; in the second step, the respondents and/business leadership team can sit down together, to filter, refine and enrich these areas of opportunity.
• Assessing the market landscape: Research existing GenAI applications in your financial services vertical. Understand how it’s being leveraged to join up new and innovative services in the market and in your area of business. What are the potential areas of opportunities that can help you gain competitive advantage in the market? Look for success stories where similar challenges have been tackled with generative models. For instance, some banks are using GenAI to automate fraud detection by analysing historical transaction patterns and identifying anomalies. This can spark new ideas and provide valuable benchmarks for your own implementation strategy, e.g. a leading insurance company is currently using GenAI to analyze customer reviews and social media sentiment to identify potential areas for improvement in their claims processing procedures. This research should be done as a hybrid pod, with participants from product management and research and innovation teams. 
• Run ‘design thinking workshops’: In your final step, you should schedule several design thinking workshops with line of business leaders and AI innovation teams to showcase GenAI's strengths. Generative models can create human-quality text, translate languages, and even generate creative content like marketing materials or personalized financial product descriptions. The first part of your workshop should focus on fully grasping capabilities, while the second part should focus on mapping these capabilities to address the pain points and opportunities you’ve identified in the previous stages. For example, a GenAI system can analyse a customer's transaction history, investment holdings, and risk tolerance – and based on this data, craft a personalized investment proposal, outlining asset allocation, performance metrics, and potential investment opportunities tailored to their specific needs and risk tolerance levels.

Phase 2: Prioritization and alignment

• Impact assessment: Evaluate the potential impact of GenAI in each identified area. Consider factors like cost savings, efficiency gains, improved customer experience, and potential revenue generation, through more targeted marketing campaigns. Quantify the impact (whenever possible) to build a strong business case. For example, estimate the cost savings associated with automating report generation or the potential revenue increase from personalized product recommendations.
• Align it with your strategy: Ensure your GenAI initiatives align with your overall business strategy. Is there a focus on client acquisition, wealth management, or regulatory compliance? Are their specific product or market placement plays? Choose GenAI applications that directly support these strategic goals. If your firm is prioritizing wealth management, focus on GenAI solutions that can personalize investment advice or streamline portfolio management tasks. For example, if a bank is aiming to improve customer acquisition, they might prioritize GenAI solutions that can personalize marketing content based on a customer's demographics and financial needs.
• Data availability and quality: Generative models thrive on large, high-quality datasets. Assess the availability of relevant data within your organization and address any data quality issues before you deploy. Financial services firms often have vast troves of customer data, transaction histories and market trends. However, ensure the data you use is clean, accurate, and properly labelled for optimal GenAI performance. For example, a GenAI system designed to personalize loan applications might require historical customer data labelled with relevant information, such as income, credit score and loan type. This data needs to be accurate and complete to ensure the GenAI system generates correct and unbiased recommendations.

Phase 3: Implementation and integration

• Proof of Concept (PoC): Start with a small-scale PoC to test the feasibility and value proposition of your chosen GenAI application. This will allow for early course correction and minimize risk. For example, develop a PoC that focuses on automating a specific type of report generation or personalizing a marketing campaign for a limited customer segment.
• Phased rollout: Once the PoC has proven successful, plan a phased rollout to minimize disruption. This allows for user training and feedback collection throughout the process. A phased rollout allows for adjustments and refinements based on user experience, ensuring a smoother overall integration. You can learn more about the journey and take homes here.
• Change management and compliance: GenAI implementation requires change management to ensure comprehensive user adoption and success. Communicate the benefits clearly and provide adequate training to address potential anxieties. Highlight how GenAI can augment human expertise, not replace it. Employees will be much more receptive to the technology if they understand how it can improve their work and free them from onerous tasks. It’s also important that you fully grasp the regulatory and risk implications of running an AI project. 

Check out our guide here.

Conclusion

By following this structured approach, financial services firms can identify high-impact business areas for GenAI. Remember, the key lies in understanding business needs, aligning with strategic goals, and ensuring a smooth integration process. Generative AI has the potential to unlock significant value in the financial services industry; firms that adopt careful planning, followed by disciplined execution, will leapfrog the competition.