Three Ways AI is Changing Financial Planning and Reporting and Data Management for Enterprise Organisations
The financial sector is undergoing a significant transformation as artificial intelligence (AI) becomes more integrated into financial planning and reporting.
AI's capabilities in data processing, pattern recognition, and predictive analytics are proving to be game-changers, providing financial professionals with powerful tools to enhance accuracy, efficiency, and strategic decision-making.
In Deloitte’s most recent survey of 2000 businesses deploying Generative AI (GenAI), the percentage reporting they were already achieving their expected benefits to a “large” or “very large” extent is 18%–36%, depending on the type of benefit being pursued.
However - a word of warning from our experts.
While AI is an incredibly powerful tool to wield, its results are only as good as the raw material - the quality of the data - it is interrogating.
Poor quality data can translate into costly errors, inaccurate insights, and compromised decision-making processes: IBM has estimated that bad data costs the U.S. economy around $3.1 trillion dollars each year.
Remember, if you input poor-quality data, you will get poor-quality results.
Let’s take a look at how AI is reshaping the FP&A landscape and our team’s perspective on how you can work to ensure you’re ready to deploy it for maximum value in your organisation.
- Data Cleansing: Enhancing Accuracy and Efficiency, Reducing Risk
For the organisations surveyed by Deloitte in the report mentioned above, the most common objective—at least in the short term—of Gen AI was improved efficiency and productivity (56%).
Financial data is often messy, with inaccuracies, duplications, and inconsistencies that can lead to poor decision-making. Traditionally, data cleansing is a time-consuming task requiring meticulous attention to detail. However, AI is revolutionising this process - and some organisations are leading the way for its adoption.
JPMorgan, for example, has invested heavily in AI across multiple business functions, from customer experience through to forecasting and automations. Through funding research and holding an annual AI awards programme, the company has established itself a leader in the space.
One example is their Contract Intelligence (COIN) platform, which leverages AI to review and interpret legal documents. In doing so, it has reduced the time spent on data cleansing from 360,000 hours to just seconds, drastically improving efficiency.
This platform also helps identify discrepancies and errors in financial documents, ensuring that the data used for planning and reporting is reliable.
Financial institutions like JPMorgan depend on effective risk management. To address this ongoing challenge, JPMorgan's quantitative research team alongside a specialist AI team from IBM developed ‘Morpheus’, a platform aimed at reducing model risk in trading and other areas.
Morpheus can analyse large amounts of data to identify risks associated with trades and financial modelling data. This has helped JPMC to manage risk more effectively, providing better protection to customers and improving the firm’s own financial stability.
By precisely monitoring models, the company can comply with standards while gaining a competitive edge. This approach balances risk management with profitable AI insights. For more on strategies for risk management and competitive advantage, explore this article.
2. Trend Identification: Unlocking Deeper Insights
FP&A departments control much of the data their business draws on, but if they can’t analyse it quickly, then that data isn’t offering as much value to the organisation as it could.
AI excels at analysing vast datasets to uncover trends and patterns that might be missed by human analysts. This capability is invaluable for financial planning and reporting, where understanding market and business trends can lead to more informed decisions.
AI is especially good at drawing meaningful insights from vast and complex datasets.
Ernst & Young (EY) has integrated AI into its audit processes, using machine learning to analyse large volumes of financial data for anomalies and patterns even in unstructured data, such as contracts, invoices, and images. This would previously have been an immensely time consuming task and impossible for EY teams at scale.
From uncovering hidden patterns and trends to identifying potential risks, these AI-driven analysis tools drive efficiency and unlock insight.
3. Forecasting and Simulation : Strategic Planning for the Future
Predictive analytics powered by AI enables companies to forecast future financial performance with greater accuracy. This foresight allows businesses to plan strategically, allocate resources efficiently, and stay competitive.
Traditional forecasting methods require analysts to spend time on data collection instead of value-added analysis and collaboration. AI solutions can generate baseline forecasts, freeing analysts from mundane tasks and enabling deeper understanding of operational drivers.
Take CapitalGains Investments, which integrated AI technologies to overhaul its investment strategy formulation.
The firm developed a proprietary AI platform that utilised machine learning algorithms to analyse and predict market trends with high precision. Using quantitative and qualitative analysis, the platform analysed vast datasets, including historical price data, economic indicators, and news articles.
This dynamic approach allowed CapitalGains to adapt strategies in real-time based on changing market conditions, providing a competitive edge. With the implementation of the AI-driven platform, CapitalGains Investments achieved a 20% increase in annual returns for its clients.
Simulation and forecasting are related, but they are not the same. In simulation, you can create "digital twins" to observe how a single change affects a model's outcomes. This allows for an in-depth exploration of specific KPIs or decision trees.
For example, you can examine how various investment strategies might perform over time.
By creating digital twins of different investment portfolios, you can compare them under a variety of conditions, such as supply shortages, emerging markets, and black swan events. This helps assess how each strategy withstands different scenarios.
AI in FP&A - Only The Start of the Journey
As one Chief analytics officer in financial services said in response to the Deloitte survey, “We are in the first inning of a thousand-inning game and there’s so much to be figured out.”
When it comes to large-scale deployment and value creation for AI in enterprise organisations we are only at the beginning and the tech itself is ever-evolving.
Concerns flagged in the report’s executive interviews reflect much of the concerns about AI in the broader debate:
- Generative AI outputs that can be unpredictable and subject to inaccuracies (i.e., “hallucinations”)—which undermine trust, particularly when combined with lack of transparency and explainability
- Potential loss of control over what Generative AI apps are being used within the organisation and who is using them
- Worker resistance to using AI due to lack of familiarity or concerns about being replaced
As Deloitte states, “Organisations should be actively developing sustainable processes and policies for enabling ubiquitous but responsible Generative AI use and managing the associated risks at scale.”
If your organisation is at the start of its journey with AI, before racing to integrate it across your business, first ensure your processes, systems and models are planned properly to mitigate some of the challenges above.
And as, according to Forrester, 70% of employees will have to heavily use data by 2025, training your team should be a priority to minimise data quality concerns for your large organisation.
Finally, remember the fundamental rule: if you’re putting garbage in, you will only get garbage out.
For data governance and data insight gold, you need robust foundations.
That’s where our financial management consultancy can support. Book a call with our friendly team at: info@insightmodels.co.uk
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