Machine Learning and Artificial Intelligence

As a leading provider within planning and performance management Hypergene see that the use Machine Learning (ML) and Artificial Intelligence (AI) for financial and strategic planning, analysis, and reporting holds great potential. Predictive analytics techniques such as regression analysis, time series analysis, and machine learning can be used in financial planning to forecast trends, identify opportunities and risks, and optimize resource allocation. Natural language processing and automatic text generation can be used to analyze data, transform data into descriptive texts and, e.g., produce statements and reports.

By leveraging AI technologies, we can deliver more accurate, timely, and actionable insights. Hypergene is built on a foundation of structured data and solid APIs, enabling our customers to connect with and use external AI tools as an integrated solution within the Hypergene platform.

Machine learning and Artificial Intelligence can streamline planning processes and analysis in the following areas, among others:

Predictive Planning and Analysis

Hypergene already offers extensive support for streamlining planning processes within the business, including preloading proposal data into forecasts. Proposal data can be based on historical planning or outcome information, which in turn can be adjusted - for example, based on assumptions about revenue or cost price development. Similarly, price and volume assumptions can be made when using driver-based planning models.

Mathematically and statistically enhanced models can be linked to this, such as models that create proposal data series or benchmark-related comparison series via regression or statistical interpolation. Benchmarks against internal or external data sets are currently managed by several customers, including in the real estate industry, where comparisons are made both internally (against other units) and against publicly available comparison data (externally).

In addition, integrated ML can further enhance the precision of planning processes and the ability to analyze large amounts of data.

Data Interpretation and Machine-Generated Textual Information

AI can also add significant value to machine-generated textual information (generative AI) based on data interpretation. This can include textual descriptions of larger or smaller data series - for example, outcome data compared to target or planning data and trend data. The interpretation and communication of information can be significantly automated, not least within Hypergene's Performance Reporting solution.

One more example is textual descriptions of performance information compared to planning data - where AI can both summarize the financial analysis and - in the text - describe the cause of deviations. By using generative AI, considerable time can thus be saved when writing analyses.