Wie ist eine sofortige Auszahlung technisch möglich?

Ein separat verwalteter Liquiditätspuffer hält einen Teil des Kapitals in kurzfristig verfügbaren Positionen. Auszahlungsanfragen werden zunächst aus diesem Puffer bedient, wodurch Auszahlungen ohne Mindesthaltedauer möglich sind, unabhängig vom Status laufender Anlagepositionen.

Wie werden meine Daten geschützt?

Alle personenbezogenen und finanziellen Daten werden verschlüsselt gespeichert und verarbeitet. Die Plattform ist auf die Anforderungen der DSGVO ausgelegt, einschließlich Zweckbindung, Löschfristen und dokumentierter Zugriffsrechte.

Wie zuverlässig sind die KI-Prognosen?

Prognosen basieren auf statistischen Modellen und historischen Mustern, sind jedoch keine Garantie für zukünftige Entwicklungen. Jede Empfehlung wird mit den zugrunde liegenden Annahmen ausgewiesen, damit Sie die Grundlage der Einschätzung selbst prüfen können.

SaxumFin — Decision Intelligence

Real-time decision intelligence for your capital

SaxumFin combines AI-powered diversification models with instant liquidity. You receive continuously validated recommendations for action and retain access to your capital at all times - with no minimum holding period and no waiting times for payouts.

Request access See how it works

For professionals who want to control multiple sources of income based on data.

SaxumFin Visualization of real-time data analysis and portfolio control

Ø Analysis cycle4.2 sec.

Liquidity100% accessible

Data points/day2.8 million

Initial situation

Why classic analysis processes reach their limits

Information overload — relevant market signals are lost in the mass of available data before a decision can be made.
Capital commitment — many classic investment models require fixed holding periods, which limit short-term flexibility.
Reaction speed — manual evaluations take hours, while market conditions change within minutes.

Professionals with multiple sources of income face a double problem: They need reliable analysis in a short time, but at the same time they also need access to their capital when priorities shift. Classic models are designed for inertia — long verification processes, fixed terms, delayed payouts.

SaxumFin addresses exactly this gap: analysis and liquidity are not separated, but treated as a continuous process.

About the platform

Data-based decisions, prepared in a comprehensible manner

SaxumFin structures market data from multiple sources into a unified pipeline. Each recommendation is traceable to the underlying data points, so you can understand how an opinion was formed.

The models are continually checked and adjusted based on real market developments. Deviations between the forecast and the actual course are immediately incorporated into the next calibration.

SaxumFin working environment for evaluating financial market data

How it works

Three building blocks for data-supported capital management

AI-powered forecast models

Neural networks extract patterns from historical and current market data and derive scenarios for different investment horizons. The models continuously optimize weights instead of sticking to static rules.

Processing structured and unstructured data sources
Ongoing recalibration based on new market data
Scalable application to different portfolio sizes

Forecast model - key figures

Recalibrationevery 6 hours

Data sources14 categories

Model versionongoing

No minimum holding period

Payouts are made without blocking periods. A technical liquidity buffer ensures that requested amounts remain available regardless of the current investment cycle. You decide when capital remains tied up and when it is released.

Immediate liquidity without fixed holding periods
Transparent display of the available withdrawal amount
No contractual penalties for early withdrawal

Liquidity status

Minimum holding period0 days

Processing timeimmediately

Availability100%

Dynamic risk management

The risk exposure is not determined once, but is continually recalculated. Shifts in volatility or correlation between asset classes result in automatic weighting adjustments before thresholds are exceeded.

Ongoing recalculation of the exposure
Early adjustment when volatility increases
Diversification across multiple asset classes

Risk monitoring

Check interval15 mins

Asset classesdiversified

Threshold testingautomated

Methodology

From data point to validated decision

Aggregation of global data streams

Market, economic and company data from different regions are continuously recorded and converted into a uniform format.

Pattern recognition by neural networks

The processed data goes through multi-layered models that identify connections and anomalies that would be difficult to detect manually.

Validated recommendations for action

Results are checked against risk parameters and provided as a concrete, comprehensible recommendation - including the underlying assumptions.

Use cases

Application scenarios for different strategies

Market analysis

Automated market observation

Continuous evaluation of relevant market segments so that changes are recognized as soon as they become apparent in the data.

Portfolio management

Precise diversification

Distribution of capital across multiple asset classes, adapted to individual risk tolerance and time horizon.

Hedging

Strategic hedging

Model-based suggestions for reducing cluster risks in the event of greater market volatility.

Frequently asked questions

Answers about liquidity, security and model accuracy

How is an immediate payout technically possible?

A separately managed liquidity buffer holds part of the capital in short-term positions. Withdrawal requests are initially served from this buffer, allowing withdrawals without a minimum holding period, regardless of the status of current investment positions.

How is my data protected?

All personal and financial data is stored and processed in encrypted form. The platform is designed to meet the requirements of the GDPR, including purpose limitation, deletion periods and documented access rights.

How reliable are the AI forecasts?

Forecasts are based on statistical models and historical patterns, but are not a guarantee of future developments. Each recommendation is presented with the underlying assumptions so that you can verify the basis of the assessment yourself.