investicijska platforma HEP - an interface for data analysis and capital allocation forecasting

Data-driven optimization

Analytical platform for capital allocation decisions

investicijska platforma HEP processes a company's real-time financial and operational data and generates recommendations based on statistically significant patterns, with a publicly available record of past performance.

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The dashboard view shows aggregated portfolio indicators, risk range by scenario and recommendation history by reporting day.

The problem

Manual analysis does not keep track of the volume of data generated by the business

  • 1 Business owners usually analyze cash flows in tables, with a time lag of several weeks.
  • 2 Decisions about excess cash are made on gut feeling, not on comparing scenarios.
  • 3 Risk is assessed retroactively, after the loss has already occurred, rather than in advance.
According to an internal analysis of platform usage patterns, companies with more than three sources of income need an average of more than ten working hours per month just to consolidate data before making a capital allocation decision.
investicijska platforma HEP - a team that analyzes financial indicators on screens

Methodology

How an algorithmic engine processes company data

The process is divided into three phases that are executed continuously, without the need for manual input after the initial connection of the data source.

01

Data collection

The system connects accounting and banking sources and normalizes transaction data into a standardized format suitable for statistical processing.

02

Model processing

The neural network compares current cash flow patterns with a historical data set and calculates a range of likely outcomes for a defined horizon.

03

Recommendation and report

The platform generates a specific allocation recommendation with a specified confidence level and records it in a log available for retroactive verification.

Platform module

Three functional units for monitoring and risk assessment

Each module works on the same data set, but answers a different question: what is happening now, how risky it is, and what would happen under different conditions.

Monitoring

Real-time monitoring

Continuous updating of indicators of liquidity and inflow of funds, with warnings when the deviation from the usual pattern exceeds a defined threshold.

Risk

Risk mitigation mechanism

The model assigns a risk exposure score to each proposed allocation, based on income volatility and concentration of funding sources.

Simulation

Automated scenario testing

Comparing multiple hypothetical allocation scenarios before making a decision, showing a range of outcomes instead of a single isolated estimate.

Verifiable results

A public record of performance open to the user community

Presentation of the relationship between recommended and actual allocation by weekly period, aggregated by groups of users with a comparable income profile.

Verification methodology

Each recommendation is recorded before the user implements it, thus preventing retroactive adjustment of results. Deviations are displayed without removing negative outcomes from the log.

The record is available to platform users for independent verification of algorithmic precision over time, not just at the time of publication.

Record updated: last working day of the week

Business owner questions

Security, integration and model accuracy

How is the company's financial data protected?

The data is transmitted via an encrypted connection and stored separately from the user's identification data. Access to analytical modules is limited to the account holder, without selling data to third parties.

How long does the integration with the existing accounting system take?

Connecting standard data sources usually takes several business days, depending on the number of bank accounts and the format of existing reports. After the initial connection, the processing is automated.

How is the accuracy of model recommendations measured?

Accuracy is calculated by comparing the predicted range of outcomes with the actual recorded outcome for the same period, and the difference is published in the public record without selective omission of data.

Access to analytics begins with an insight into your company's available data

After signing up, the integration team checks which data sources are available for connection and defines the initial scope of monitoring before the first report.

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