Model önerilerinin mantığı nasıl açıklanıyor?

Her öneri; kullanılan veri kategorisi, hesaplanan risk skoru ve olasılık aralığıyla birlikte sunulur. Kullanıcı, öneriye sadece sonucu değil hangi girdilerin bu sonuca katkı sağladığını da görebilir.

Verilerim nasıl saklanıyor ve kimlerle paylaşılıyor?

Kullanıcıya ait portföy ve tercih verileri, yalnızca öneri üretimi amacıyla işlenir ve üçüncü taraflarla pazarlama amacıyla paylaşılmaz. Erişim, hesap bazlı yetkilendirme ile sınırlandırılır.

Platform mevcut sistemlerimle entegre olabilir mi?

Kurumsal kullanıcılar için raporlama çıktıları standart dosya formatlarında dışa aktarılabilir. Daha geniş kapsamlı sistem entegrasyonu için ihtiyaçlar, iletişim ekibimizle görüşülerek değerlendirilir.

Abstract visualization of the Online Yatırım AI-powered data analytics platform

Artificial Intelligence Supported Analysis Platform

Turn Market Data into Strategic Decisions with Predictive Models

Online Yatırım processes large volumes of financial and operational data in real time; It scores risk levels and transforms these outputs into usable recommendations for both corporate planning and individual capital allocation. The system allows location-independent professionals to make data-based decisions without establishing their own analysis infrastructure.

Review Platform See Performance Records

Each suggestion is recorded with a timestamp when it is produced, and when the relevant market result is announced, the same record is updated and kept in the public diary.

Our approach

We Build Decision Support Systems with Traceable Data, Not Assumptions

Online Yatırım treats financial decision-making not as a “signal” but as a structured chain of analysis. Platform; It documents each of the data collection, model extraction, risk scoring and recommendation production steps separately and explains the logic of these steps to the user.

Our target audience is remote professionals and individual investors who make their decisions without being tied to a central office structure. Therefore, rather than simplifying complex financial terminology, the interface prioritizes making the rationale visible by preserving the terminology.

Working environment showing the Online Yatırım team's data analysis processes

methodology

How the Predictive Modeling Process Works

The logic behind the recommendations proceeds in four stages. Each stage uses the output of the previous one as input, and the final recommendation is the joint result of this chain, not a single model.

Data Collection

Market price movements, macroeconomic indicators and publicly available company data are collected at regular intervals and converted into a standard format.

Model Analysis

Probabilistic scenarios are produced by associating the collected data with past price behavior; Probability ranges are used rather than a single definitive estimate.

Risk Scoring

Each scenario is scored based on volatility and potential loss size. Outputs with high uncertainty are moved to the recommendation stage with low weight.

Suggestion Generation

Scored scenarios are combined to create an actionable recommendation and associated risk note, which is presented to the user with justification.

Risk Management Approach

The model does not present return potential independent of risk level. Every suggestion; The possible loss scenario is reported along with the volatility range and confidence level. This structure allows the user to see not only the opportunity but also the cost.

Data Source Transparency

Data categories used; publicly available market data, macroeconomic indicators and publicly disclosed corporate disclosures. The platform indicates on the recommendation screen which data category affects which recommendation.

Platform Capabilities

Four Core Modules Designed for Enterprise Use

Real Time Analysis

Model outputs are updated as market data arrives; The user can see according to which data change the recommendation was revised.

Predictive Modeling

Probabilistic scenarios are generated based on historical data patterns; The model provides range-based predictions rather than a single definitive result.

Automatic Reporting

All recommendations and their justifications within the selected time period are periodically compiled into a structured report.

Portfolio Optimization

By comparing the current asset distribution with risk scoring outputs, imbalanced concentration points are marked.

Community Verified Results

Public Performance Log

Every suggestion produced by the platform is kept in a publicly accessible log, not in a closed archive. This log shows side by side the date the recommendation was generated, its rationale, and the subsequent market outcome.

Sample Log View Time Stamped Recording

Recommendation: Sector-based risk mitigation Production Date Result Status

Recommendation: Volatility threshold warning Production Date Result Status

Recommendation: Capital distribution revision Production Date Result Status

Accuracy Tracking

The accuracy of each suggestion is determined by comparing the market condition at the time the suggestion was published with the movement in the following period and is updated in the diary.

Verification Method

Records cannot be changed retroactively once published. In this way, the comparison between the proposal at the time of production and the result is preserved intact.

Access

The log viewer can be viewed without requiring a subscription; Only detailed filtering and export functions require membership.

Usage Scenarios

Practical Application in Different Decision Contexts

Corporate Use

Corporate Strategic Planning

Teams evaluate the model output together with their existing internal data in quarterly planning cycles. Rather than setting a definitive direction, recommendations provide the decision maker with an additional reference point; The final decision always remains with the respective team.

Individual Use

Individual Capital Allocation

Users who work remotely and manage their own savings review their current asset allocation using risk scores. The platform facilitates rebalancing decisions by marking which asset class's share of total risk has increased.

Risk Assessment

Market Risk Assessment

During periods of sudden volatility, the model recalculates the sensitivity of existing positions and generates notifications in case of threshold exceedance. This reduces the need for the user to manually keep track of each data set.

Frequently Asked Questions

About Algorithms, Data Security and Integration

How is the logic of the model recommendations explained?

Every suggestion; The data category used is presented along with the calculated risk score and probability range. The user can view not only the result of the suggestion but also which inputs contributed to that result.

How is my data stored and with whom is it shared?

User's portfolio and preference data are processed only for the purpose of producing suggestions and are not shared with third parties for marketing purposes. Access is limited by account-based authorization.

Can the platform integrate with my existing systems?

For corporate users, reporting outputs can be exported in standard file formats. Needs for broader system integration are evaluated in consultation with our communications team.