Compass Wertvale data analysis interface showing structured investment metrics

Data-Driven Decision Support

AI-Based Data Analysis for Investment Decisions, Built on Verifiable Security Standards

Compass Wertvale processes market, macroeconomic and company-level data through structured predictive models and returns risk-adjusted recommendations before capital is committed. All data is handled within an encrypted, GDPR-aligned processing environment.

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AES-256 Encryption at rest

TLS 1.3 Transport layer

EU Data residency

Metric panel reflects illustrative output structure, not live account data.

Operating Principle

A Decision-Support Layer, Not a Trading Signal Generator

Compass Wertvale was built for individuals who are approaching investment decisions without a background in finance and who are more concerned with structural risk than with short-term price movement.

The platform does not issue buy or sell instructions. It structures available data into comparable, risk-weighted output so that a decision can be made with a clearer view of trade-offs. Execution of any resulting decision remains with the account holder and their chosen regulated brokerage or custody provider.

Compass Wertvale analytical workspace used to review structured investment data

Section 01 — Security & Compliance

Security Framework and Regulatory Alignment

First-time investors frequently cite data exposure, not market risk, as their primary reservation. The following describes the technical and legal basis on which account and transaction data is handled.

AES-256 Encryption (data at rest) TLS 1.3 (data in transit) GDPR / DSGVO Aligned EU Data Residency BDSG-Consistent Retention

Encryption Architecture

Personal and financial data are encrypted at rest using AES-256 and in transit using TLS 1.3. Cryptographic keys are rotated on a fixed schedule and access to key material is restricted by role, not by individual discretion. No plaintext copies of account credentials are retained on application servers.

Regulatory Framework

Data processing is structured to align with the General Data Protection Regulation (GDPR / DSGVO) and the German Federal Data Protection Act (BDSG). This includes data minimization at intake, defined retention periods, and a documented basis for each category of data collected. Users retain standard rights of access, correction and deletion under Art. 15–17 GDPR.

Section 02 — Analytical Engine

How the Predictive Engine Structures Incoming Data

Rather than producing a single forecast, the engine breaks analysis into discrete, auditable stages. Each stage narrows the dataset before a recommendation is formed.

Data Ingestion

Structured intake of pricing, filings and macro data from defined source categories.

Normalization

Data is cleaned, timestamped and adjusted for reporting inconsistencies across sources.

Pattern Recognition

Statistical models identify correlation shifts and volatility clustering across the dataset.

Risk Scoring

Output is expressed as a bounded risk range, not a single point prediction.

Update Cycle Continuous, event-triggered

Latency Class Sub-minute where source permits

Model Recalibration Scheduled, version-logged

Output Format Risk range, not single figure

Risk Mitigation Logic

The engine is designed to flag concentration risk, correlation between held positions, and abnormal volatility relative to a defined baseline. It does not attempt to predict short-term price direction and does not claim to eliminate loss. Its function is to make existing risk visible before a decision is made, rather than after.

Section 03 — Decision Process

From Raw Data to a Recommendation You Can Evaluate

The process below is the same sequence applied to every account, independent of portfolio size, so outcomes remain comparable across users.

Data Ingestion

Account inputs — risk tolerance, time horizon and existing holdings — are combined with market and macroeconomic datasets. No third-party marketing or behavioral tracking data is used as an input.

Pattern Recognition

The dataset is screened for correlation between assets, sector concentration, and deviation from the account's stated risk profile. This stage produces flags, not conclusions.

Tailored Recommendation Output

Flags are translated into a written recommendation with the underlying reasoning shown alongside it, including which factors drove the result. The recommendation is advisory in nature and does not execute any transaction automatically.

Section 04 — Methodology

Data Sources and Model Validation, Without Relying on Testimonials

Instead of social proof, Compass Wertvale publishes the categories of data used and the general validation approach applied to its models, so the basis for a recommendation can be assessed on its own terms.

Category	Description
Market Pricing Data	Publicly available price and volume series for listed instruments.
Macroeconomic Indicators	Figures published by central banks and national statistical offices.
Company Filings	Publicly disclosed financial statements and regulatory filings.
Volatility & Correlation Indices	Derived measures used to contextualize risk relative to broader market conditions.

Backtesting Methodology

Models are evaluated against historical data windows that were withheld from training, using out-of-sample testing to reduce overfitting. Historical performance under backtesting does not guarantee future results, and this limitation is disclosed alongside any model output rather than in a separate notice.

Algorithmic Transparency

Each recommendation is accompanied by the factor weightings that produced it. Users can review which inputs — volatility, concentration, macro exposure — contributed most to a given score, rather than receiving an unexplained output.

Section 05 — Technical & Legal FAQ

Questions Typically Raised Before Onboarding

The answers below address the questions most frequently raised by first-time users based in Germany.

How is personal and financial data protected?

Data is encrypted at rest (AES-256) and in transit (TLS 1.3). Retention periods are limited to what is required for account operation and regulatory record-keeping, and users may request access to or deletion of their data under GDPR Art. 15 and Art. 17.

Does Compass Wertvale hold or custody investor assets?

No. Compass Wertvale is a data-analysis and decision-support platform. It does not hold client funds or securities. Any transaction resulting from a recommendation is executed through the user's own regulated brokerage or custody account, which remains under the user's control at all times.

Is the platform usable without prior investment experience?

The interface is built to present risk and recommendation output in plain language, with the underlying factors shown rather than hidden. No prior familiarity with financial modeling is assumed, though a basic understanding of the account's own risk tolerance is required during setup.

Next Step

Set Up an Account and Review Your First Data-Based Recommendation

Onboarding establishes your risk profile, verifies identity in line with standard KYC practice, and connects the data categories relevant to your holdings. No recommendation is generated before this step is complete.

Estimated setup time: 10–15 minutes, subject to document readiness

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