Global banks and financial services businesses are considering the role of Generative AI (GenAI) in the finance function. We believe that using a metrics-driven approach is important for evaluating potential use cases. The most promising use cases are those that focus on creating value and enhancing productivity and have a strong likelihood of successful execution.
This blog expands on this recommendation and provides a high-level framework to start the process of evaluating and prioritising GenAI use cases in the finance function:
This is the first consideration. What value can be created?
To determine the potential value of GenAI use cases from a CFO’s perspective, consider these key areas as a rich source of value-creating opportunities:
GenAI can automate repetitive and time-consuming tasks, such as data entry, report generation, and compliance monitoring. By reducing reliance on manual processes, banks can significantly lower operational costs. For example, automating the preparation of regulatory reports can save people costs and reduce the risk of errors and costly reworking or re-runs of reporting cycles.
Potential Use Case: Automating financial report generation, streamlining compliance reporting, and reducing the need for manual data entry.
GenAI enhances risk assessment and management capabilities, allowing banks to better evaluate their portfolios' risk profiles. This approach can reduce the capital reserves required to cover potential losses. Additionally, improved cash flow forecasting and liquidity management ensure capital is utilised more efficiently, freeing up funds for more profitable uses.
Potential Use Case: AI models can analyze market data, economic indicators, and company performance to predict risks and suggest optimal capital allocation strategies to mitigate these risks. This capability is particularly useful when combined with existing risk modelling systems.
Advanced GenAI analytics can provide in-depth insights into the profitability of different products, services, and individual customers. By analyzing more detailed data and a wider data set more quickly than before, banks can better understand which product lines are most profitable and which are underperforming, and the reasons behind it. This allows for more informed decision-making about resource allocation, pricing strategies, and cost management. For example, profitability analysis can show that particular customer segments are more expensive to serve than others, prompting a strategic shift in focus or pricing adjustments to improve margins.
Potential Use Case: Enhanced Predictive Models: GenAI can refine revenue forecasting models by incorporating more sophisticated algorithms that analyse a broader set of variables, including macroeconomic indicators, competitive landscape, and customer sentiment analysis.
GenAI can transform the financial planning and analysis (FP&A) function by automating complex forecasting and budgeting processes. AI-driven tools can provide real-time insights into financial performance, enabling more accurate and timely decision-making. Scenario analysis capabilities allow banks to model different financial outcomes based on varying assumptions, helping to prepare for potential future scenarios. For instance, AI can quickly generate multiple budget scenarios, allowing finance teams to adjust plans dynamically in response to market changes or internal shifts.
Potential Use Case: Mergers and Acquisitions (M&A) Analysis: AI can evaluate potential M&A opportunities by analyzing financial statements, market conditions, and strategic fit, aiding in better decision-making..
Next in the framework is to understand the impact on productivity. These productivity metrics help gauge the efficiency gains from GenAI adoption:
GenAI significantly reduces the time needed for various financial tasks by automating repetitive processes. For instance, generating financial reports and preparing regulatory filings can be done faster and with minimal human intervention. This allows employees to focus on strategic tasks that require human insight and decision-making.
Potential Use Case: Replace existing report production with a GPT. This will work for operational reporting or validation, but not for reports that are regulatory, or need to be highly formatted. Implementing a GPT-powered solution for on-the-fly report generation can transform how financial reporting is done in banks, making it more responsive, user-friendly, and insightful.
GenAI enhances the accuracy and consistency of financial outputs by minimising human error. Automated systems can rapidly process large volumes of data, ensuring that financial reports, risk assessments, and compliance documents are accurate and reliable. This reduces the need for corrections and rework, saving time and resources.
Potential Use Case: Predictive Error Correction: Using historical data, GenAI can predict and correct common errors before they impact reports. For example, if certain types of transactions are frequently misclassified, the AI can learn to identify and correct these errors automatically.
GenAI optimises various operational aspects by automating routine tasks, improving data analysis, and providing actionable insights. This leads to more efficient resource use, reduced operational costs, and better decision-making. For example, AI-driven chatbots can be deployed as an alternative to publishing internal management reports. Users can “ask” for analysis and draw their own charts on the fly.
Potential Use Case: Natural Language Generation (NLG): Use NLG techniques to generate comprehensive reports in natural language. The GPT model can provide textual explanations, insights, and summaries based on the data analysis.
Finally, we need to explore the execution risk and the ability to deliver a successful outcome. We recommend finance teams assess at least the following metrics to ensure the practical implementation of GenAI solutions:
When developing GenAI capabilities, a decision is similar to the "rent, buy, or build" dilemma. The main principle remains valid: a company should invest in a GenAI capability to gain a proprietary advantage and utilise existing services for those that are more like commodities.
We’ve borrowed McKinsey’s language here because we think it’s excellent. Finance leaders can think through the implications of these options as three archetypes:
Every type of technology has its associated costs that leaders must consider. While advancements like more efficient model training methods and decreasing costs of graphics processing units (GPUs) are lowering costs, the complexity of the Maker approach means that few organisations will adopt it shortly. Instead, most will likely use a combination of the Taker model to access a standard service quickly and the Shaper approach to develop a custom capability on top of existing models.
The illustration below highlights The Shaper approach, which we believe has the most potential for finance teams and strikes the right balance between value, cost and effort.
Several approaches exist to ranking ideas, and this framework provides a granular approach to ranking potential GenAI initiatives across the finance function.
Assign scores on a scale of 1 to 5 for each sub-criterion, where 1 indicates low potential and 5 indicates high potential. Multiply each score by its weight and sum the results to get the total score for each use case. Use the total scores to rank the use cases. Prioritise those with the highest scores for pilot testing and potential full-scale implementation.
Finance leaders should recognise the transformative potential of GenAI. By using a structured framework to assess and prioritise GenAI use cases, finance leaders can unlock significant value, improve operational efficiencies, and enhance strategic decision-making. We encourage finance teams to begin exploring GenAI applications now, test the most promising use cases, and incorporate successful solutions into their overall operational strategies. The future of finance is being influenced by AI – make sure your organisation is leading this revolution.
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