Quais são as melhores práticas para identificar riscos e oportunidades financeiras por meio da modelagem?

Best practices for identifying financial risks and opportunities through modeling involve comprehensive data analysis, scenario planning, stress testing, and incorporating various variables. Regularly updating models, considering external factors, and seeking input from relevant stakeholders are also key practices for effective financial risk and opportunity identification.

Clearly defined objectives act as a roadmap for your modeling journey. They guide your data selection, choice of modeling techniques, and interpretation of the results. By outlining your goals upfront, you ensure the model addresses your specific needs, whether it's assessing a new product launch or evaluating market entry feasibility. Therefore this step is vital to build a reliable and efficient financial model.

Sometimes defining objectives is not straight forward. I have found it can take a number of conversations with the customer to get clarity around the exact outputs they maybe looking for, especially if there are several things they are trying to achieve. Particularly as you are discussing abstract figures at the planning stages, and there can be subtlety in the detail that the client may not always be aware of, or implicit assumptions that are overlooked. For example, an external consultant came in to try and build a budget forecast using a "machine learning" model based off "nominal" data, but hadn't realised that the customer wanted to have their own inflation assumptions, as well as other scenario levers, rather than a rigid "black box".

The best practices include thorough data analysis, visualising scenarios that may have a large impact and regularly updating models to adapt to the change in marketing conditions. Being able to analyse relevant data and ensuring its accuracy and completeness is importance. This way organisations can manage finance risk and capitalise on emerging opportunities.

Before systematizing any type of financial modeling, it is important to define clear, measurable and finite objectives, as well as aligned with the strategic mission and long-term objectives of the company. For example, as a financial manager, I set goals focused on increasing profitability and expanding market share over the next three years.

To get an upper hand in making the most of your data sets, explore how machine learning algorithms can find correlations in your data which may not be obvious. Additionally add external market data and run AI/ML models to identify core business drivers and their relationships. Of course, to do so your data sets needs to be clean and historically rich.

With larger enterprises, undertaking more detailed modelling, and using their internal data, it can sometimes take a while to understand the exact nature of the data that a modeller is dealing with, as well as assessing what level of detail is required. So it is very important to get understand how it was collected and for what purpose to verify whether the data is appropriate to use or not. In addition, it may take time to restructure the data, or make a number of agreed assumptions to use the data in a way it was not originally intended for. For example, invoice based data maybe focused on the suppliers, so to gain a customer centric view of the data may require remapping and/or aggregating codes.

The quality of a financial model depends on the quality of the data entered. Therefore, validating the reliability, source and coherence of the data used is essential. In my case, I combine historical company data with technology sector growth projections and trend analysis, ensuring that each data can be verified and relevant to our specific context.

My approach to normalization and modeling of financial data combines BI software and analytical tools. This allows me to configure dynamic visualizations to support analysis and decision making. I adjust projections for various scenarios, using sensitivity analysis evaluating impacts of changes in variables, this helps me monitor financial processes, for example; the income/profit ratio to mention one of them. I also rely on Monte Carlo simulations to anticipate scenarios and evaluate the probability of achieving our objectives. This comprehensive method bases strategies on sound economic principles, optimizing informed decision making.

Ideally have peer review in addition to checking your own work, so that someone else objective is able to test and review the model. Before a review, any error materiality levels should be agreed upon with the reviewer, as well as be provided documentation or enough description about what the model purpose function and logic is, in order to have a reference point to review where the model logic is correct. In addition to cell by cell detailed mechanical checks of unique formula, it is also important to consider higher level analytical checks. e.g. calculate and graph ratios or other metrics to see that they make sense. If deemed important enough, a parallel model build may also be useful to provide comfort around model integrity

Validate a model for our strategies from finances; It starts from ensuring accuracy, followed by sensitivity tests that reveal previously hidden critical aspects. Ensure the future relevance of the model by evaluating its stability and adaptation to different scenarios. Validation against benchmarks and real data refining with accuracy. Document the entire process, offering clarity and guidance for future adjustments. Success lies in continually updating the model, adapting to changes, which positions us to take advantage of opportunities and face challenges with agility.

One of the most important factor when communicating your findings is to understand your audience and to know the reason behind each presentation and to tailor your presentation based on Circumstances. For examples if the presentation is tailor for top executives it should be direct to the point , highlights the main factors and not lengthy. While if the presentation is for a finance or audit committee it should be detailed , included the supporting data and bit more technical than the one for top executives.

In financial modeling it is essential to present the findings effectively. This implies transforming complex analyzes into a clear and understandable narrative for recipients. For this I use BI dashboards, that is, converting data into visual stories. These visualizations summarize the model concisely and provide insights that are transformed into recommendations based on the analysis. Knowing the limitations of the model gives us margins, presenting clear caveats and assumptions to contextualize the projections. This approach ensures better understanding of the results, in addition, it gives confidence by reinforcing the proposed recommendations.