How can you improve your forecasting accuracy with large data sets in finance?

Before you look at the data, you need to understand your business. What are the current workflows, what is the business strategy and the related goals. Have they changed over time and especially in light of the pandemic? Understand what your business drivers really are. When you understand your business, then you start critically evaluating your data. Before you can understand your data, you need to discover the relevant data sources and what they mean. Does all your data come from a single source? Do they conform across the board? Do you need a Data Warehouse to ensure that the data you ultimately consume in your forecast model, is universally accepted as being valid. "One Versio0n of the truth" as defined by the company!

Pour améliorer l'exactitude de vos prévisions dans le marché(surtout l'assurance) avec de vastes ensembles de données, vous pouvez utiliser des outils tels que des logiciels d'analyse de données avancés comme Python avec des bibliothèques telles que Pandas qui va permettre de l'analyse de séries temporelles, la modélisation prédictive et l'analyse des données non structurées peut également être bénéfique.

To boost your forecasts in finance's dance, With large datasets, give these steps a chance: First, clean the data, make it pristine, Outliers and errors, swiftly intervene. Next, choose models with wisdom and tact, Consider the trends, don't overreact. Split your data wisely, train with care, Validation helps, it's a duo affair. Ensemble methods, a powerful crew, Combine predictions, let them accrue. Feature engineering, a skill profound, Create new insights, let patterns be found. Constantly evaluate, don't be shy, Adjust your models as time passes by. In this financial ballet, data's the star, Follow these steps, and you'll go far.

To improve forecasting accuracy in finance with large datasets, focus on data preprocessing (handling missing values, normalizing data), feature engineering (creating and selecting relevant features), and applying advanced statistical or machine learning models (like time-series analysis or ensemble methods). Utilize big data technologies for efficient data handling and ensure rigorous validation through techniques like cross-validation and backtesting. Regularly update models with new data, integrate domain expertise for insightful feature engineering and adhere to regulatory standards. Balancing these elements, while acknowledging market unpredictability, enhances forecast reliability.

To understand the data we need to bring that in a position which can help us to extract some important decision making things out of it.. here is how to bring it to that position..Cleanse, validate, and explore your data to identify patterns, trends, and potential biases. Ensure data quality through regular checks and eliminate outliers that might skew results. -Feature engineering: Craft relevant features from raw data based on your specific forecasting problem. This might involve combining data points, calculating ratios, or transforming data using statistical methods.

In finance, selecting the right forecasting model is key. For instance, if we're tracking short-term sales trends, moving averages might be our go-to. But if we're predicting long-term market behavior, we might lean towards regression or machine learning methods. Just keep it simple and consider our resources.

The right model depends on the account cost in accounting, because they have different usage pattern Different department also have different usage pattern

Don't blindly apply a single model. Explore various options like time series models, regression models (linear, logistic), machine learning models (neural networks, random forests), and ensemble methods (combined models).

Utilizing a model that is appropriate for financial forecasting is of the utmost importance. Take, for instance, the use of moving averages for the purpose of monitoring sales patterns over the course of the following several months. On the other hand, when it comes to forecasting the behavior of the market over the long term, we can use regression or machine learning methodology. Simplify the situation and take stock of what we already possess.

Forecasting even depends on the model which you have selected. The model should be simple and easy to comprehend. The model must help you in achieving the results which you are aiming to achieve.

Test check basis is very important in analysing the loopholes. The pitfalls must be re aligned and necessarily the model should be upgraded looking at the updations!

Para acertar nas previsões financeiras, é essencial testar e validar os modelos com cuidado. Isso significa não misturar os dados de treino com os de teste, pra não cair na armadilha do sobreajuste. Manter o modelo atualizado com dados novos e ajustes conforme o mercado muda também é chave. Usar técnicas como validação cruzada e backtesting ajuda a ver se o modelo tá no caminho certo. E comparar com outros modelos ou padrões do mercado é uma boa pra saber onde melhorar. Resumindo: é um jogo de ajustes constantes pra manter as previsões precisas.

When we find out the history, automatically we also find out the model / the pattern, we need to validate / test only if the model not related with current situation I think this point for initial in making forecast

Never use the same data for training and testing. Separate your data into training, validation, and testing sets. Evaluate model performance on the unseen test set to avoid overfitting and get a realistic estimate of accuracy.

Consider having a process, whether automated or manual in place for updating the model. When we neglect input from other business units, we might risk the robustness of the model. Aim for the model's forecast to be true as of today to start thinking about what can be automated. Aim for having the most current inputs as of today in order to help teams make timely business decisions. Especially for what is driving your revenue streams.

When we're wrapping up our forecasting, make sure to chat with the team. Show our predictions using charts and tables, and explain how we reached them. Mention any uncertainties and be open to questions or changes. For instance, if we're forecasting product sales, use visuals to highlight trends, considering seasonal fluctuations.

Communication helps the stakeholders to know their forecasts and make appropriate decisions. Dashboards can be an effective tool for communicating the forecast. Risks and opportunities should also be included in the communication.

In my opinion, Improving forecast accuracy in finance with extensive datasets involves steps like comparing forecasts with actual outcomes, ensuring data quality, selecting key features, choosing suitable models for fine tuning, comparing forecasted results with actual performance to maintain accountability, uncovering underlying patterns using trend analysis, and continually monitoring and updating data to adjust to the dynamics/economic conditions.

Effectively communicate forecasting and budgeting results by tailoring your message to the audience's understanding, emphasizing key insights, and outlining implications. Be transparent about assumptions and uncertainties, presenting scenarios to illustrate potential impacts. Offer actionable recommendations and encourage dialogue for clarity and consensus. Use visual aids for simplicity and engage with digital tools for interactive presentations. Document all data and assumptions for transparency and future reference.

A melhor forma de expressar seus resultados é se comunicando. Então Apresentar os resultados de forma gráfica, clara, com tabelas e painéis é uma forma de compartilhar o sucesso de pequenas conquistas.

Forecasting can be done on a monthly basis to consider new data available. Comparisons of forecasts should be done versus actual and reasons for variations should be documented. This can help to enhance the forecasting process.

Continuously monitor the accuracy of your forecasts. Analyze discrepancies between predicted and actual outcomes. Learn from mistakes and refine your approach. Adapt to changing market conditions and incorporate feedback.

Monthly forecasting might be done to take newly available data into consideration. Predictions and actuals should be compared, and the reasons for discrepancies should be documented. This could lead to better prediction ability.

Sempre somos eternos aprendizes. Estamos aqui para mais para aprender do que para ensinar. Então a vida é uma eterna escola. E cada lição é um aprendizado que devemos compartilhar e passar para frente.

Different types of business dictate different approach and levels of forecasting. The main point is to be very clear from the get go on the goal or expectation of the forecast and let that influence the model you need to build. For example, if you are launching a new product and want to be able to track and measure performance against expectations, you need to forecast on product level. For an established business with core products/functions, you can take a more "big picture" approach.

ChatGPT can aid in improving forecasting accuracy with financial datasets by offering insights into data characteristics, suggesting advanced modeling techniques, providing guidance on feature selection and engineering, recommending suitable algorithms, and assisting with hyperparameter tuning. It can also answer domain-specific queries, offer code assistance, provide information on monitoring and updating models, and share knowledge on emerging trends.

Connect with the Business heads for their inputs, since it is them who have to achieve the forecast. This cross functional meeting will also help you to understand the business and to know what direction the business is heading as per the Business Heads.

- Understand Your Data: Explore data characteristics, sources, and dependencies. Identify outliers and ensure reliability. - Choose the Right Model: Select based on data, objectives, and constraints. Use simple methods for short-term stability, complex methods for long-term dynamics. - Validate and Test Your Model: Ensure robustness with techniques like cross-validation, backtesting. Regularly update with new data. - Communicate Your Results: Present forecasts clearly. Explain assumptions, methods, and limitations transparently. - Learn From Your Experience: Monitor outcomes, collect feedback, identify strengths and weaknesses. Stay updated on industry trends for ongoing improvement. 📊🚀

To enhance forecasting accuracy in finance with large datasets, utilize advanced machine learning algorithms such as neural networks or ensemble methods to uncover complex patterns. Regularly update models with new data, incorporate relevant external factors, and employ feature engineering techniques to extract meaningful insights. Conduct rigorous validation and fine-tuning processes for optimal performance.

Consider the following as it is useful i) understand the business ii) make sense of the historic trends iii) pay attention to the business key drivers iv) Do sensitivities based on key drivers