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Financial decision-making has always been about having the right information. What has changed for businesses and individuals in recent years is the speed, volume, and complexity of available information. Even with access to a detailed history of transactions, current market data, and financial performance information in the form of dashboards and reports, a business still faces a complex decision-making process.
AI-powered tools can increasingly analyze large amounts of data to identify patterns and connections, enabling financial technology to shift from information provision to decision-making support and helping individuals and businesses make better financial decisions.
While traditional financial analytics provide decision-makers with regular reports, structured data, and defined metrics in the form of financial dashboards, decision-makers must interpret the data.
AI-powered decision-support systems add another layer on top of financial information and examine much larger datasets to identify patterns and connections that might not be immediately apparent. For example, they can estimate abnormal spending, cash flow requirements, and different forecasting scenarios. Instead of working through every variable manually, users of AI-powered decision-support systems can focus on what matters most: deciding which conclusions to investigate further.
AI does not replace financial analysis; it supports it by making it easier to work with. AI identifies connections and helps us question the data at hand.
Information needs to be provided within the appropriate context, especially in financial matters. Even though two people or companies might have similar financial figures, their different goals, circumstances, and risk tolerances will dictate different courses of action.
At the same time, there is growing interest in how AI-enabled technology can support personal financial planning and advisory services. Tools such as an AI financial planner can help users organize financial information around their goals, evaluate different choices, and better understand how individual decisions may affect their broader financial situation.
Within organizations, financial information will be structured to support key decisions such as where to allocate capital, control costs, evaluate investments, and respond to changing market circumstances. The information will be highly relevant, enabling decisions to be made more quickly and with greater certainty.
Scenario planning, which involves preparing financial statements or models that reflect alternative circumstances such as a decline in sales, an increase in operating expenses, or an increase in financing costs, has in the past required significant modeling. Financial information is typically kept in large databases, such as accounting or general ledger systems. These databases contain hundreds of thousands of lines of historical financial information. Rather than starting from scratch every time a user wants to build a model to analyze alternative scenarios, the system can build on what already exists.
Adding a model to the analyst’s toolset lets users instantly evaluate countless possible future scenarios and assess how different input variables affect their desired outputs. For example, financial teams can use a model to compare the impact of different revenue forecasts on the company’s liquidity over time.
The analysis does not attempt to predict the future, and many factors outside the model’s control can affect future financial conditions. The main benefit of scenario analysis is that it enables decision-makers to consider different possible outcomes and plan for multiple scenarios.
The way people interact with financial systems is also changing. In the past, users had to sift through various reports, apply filters, or ask an analyst to create customized reports or run models and answer queries such as, “What are the biggest increases in expenses over the past quarter?”
Natural Language Interfaces to Analysis Tools also enable individuals who are not analytics experts to perform analysis. They can also ask complex questions to better understand the underlying data and generate reports for distribution to interested parties.
Additionally, insights generated by AI can provide value to a wider audience within an organization. Instead of being restricted to financial specialists, managers from other departments can also generate financial models and perform financial analysis with only limited training. Financial specialists can then review, validate, and deeply analyze the models and generated reports.
Further, even though models are based on data, certain limits apply to the data, the variables that are included, and the assumptions made in the model. All models therefore have limitations, so results should not be accepted automatically.
As mentioned, users must be wary of the limitations and associated assumptions in any model to ensure AI is used for its intended purpose and that the resulting recommendations match the analysis.
AI can be combined with human judgment in many ways to make better financial decisions. As a first step, AI can process large amounts of data much faster than humans can and highlight factors or scenarios a human may have missed.
Financial technology is now more about interpretation than making data available. For most organizations, the amount of financial data already stored means technology now helps make sense of it to support timely financial decisions.
A tool that supports financial decision-making does not have to process all available data either. It should organize it and check whether a certain assumption has the required effect. The possibilities need to be explored. The system should support the decision-making process and make complex data easy to question.
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