Artificial Intelligence applied to financial decisions
Neves Capital uses predictive analysis models to process large volumes of market data in real time, supporting families and companies in Portugal in structuring investment decisions with reduced risk.
Schematic representation of the network of data nodes processed by the Neves Capital analytical engine.
Cost Structure
In most traditional investment platforms, a relevant fraction of the return is absorbed by transaction commissions, management fees and intermediation margins. In Neves Capital, this middle layer has been removed from the structure.
No commissions on operations. The Artificial Intelligence optimization engine operates on 100% of the available capital, without any prior deduction associated with intermediation costs.
In practice, predictive models calculate allocations and rebalancing considering the total value of the portfolio, and not a value already reduced by fixed or variable rates applied before execution.
Analytical Structure
The system processes continuous streams of market data and updates relevant indicators as new information arrives, without relying on manual or periodic updates.
The algorithms identify patterns of volatility and correlation between assets, adjusting the recommended exposure depending on the risk profile defined for each portfolio.
Statistical models trained on historical series and context data project likely scenarios, serving as a basis for recommendations presented to the user.
The same analytical architecture applies to a family portfolio or a larger corporate portfolio, without structural changes to the analysis process.
Methodology
We bring together financial, market and investor profile data on a single basis, ensuring that the analysis comes from consolidated information and not from scattered sources.
Artificial intelligence models cross historical and current variables, identifying relevant patterns for the defined time horizon.
Recommendations are adjusted to the risk profile and stated objectives, proposing an allocation that balances expected return and exposure.
The final decision remains with the investor or the family. AI works as a support layer for analysis, not as a substitute for human judgment.
Operational Transparency
Recommendations result from models trained on historical market data, periodically reviewed to reflect changes in the economic and regulatory context.
Data processing follows the principles of the General Data Protection Regulation (GDPR), with storage and processing limited to what is necessary for financial analysis.
Each recommendation is accompanied by an explanation of the factors considered, allowing the user to understand the analytical basis before deciding.
The combination of predictive analysis and the absence of commissions allows available capital to be used more efficiently, without hidden costs throughout the process.