What is our expertise in finance field?

Custom Credit Scoring Solutions

Conventional models often lack the granularity required for specialized applications. We develop tailored credit scoring models designed for specific use cases, such as SMEs or industry-specific scenarios, ensuring that risk assessments accurately reflect client-specific factors.

Precision Loan Repayment Predictions

Predicting loan delinquency in binary terms may not provide sufficient detail for strategic decision-making. Our models allow for more refined predictions, such as the likelihood of repayment within specific late intervals (e.g., 0-60 days), offering a more precise risk profile.

Financial Data Analysis

We apply advanced analytical techniques to financial datasets, extracting insights that not only inform key business decisions but also serve as the foundation for the development of machine learning models to predict future outcomes.

Customized Risk Models

We construct bespoke risk models that incorporate unique client data and domain-specific variables, allowing for the precise assessment of risks such as sector-specific vulnerabilities or macroeconomic factors. This approach enhances strategic adaptability.

Business problems addressed

Inaccurate Risk and Credit Assessment → Traditional credit scoring models and credit limit recommendations often fail to account for the specific financial profiles of SMEs or industry-specific risks.

Lack of Predictive Precision → Standard financial models typically offer binary outcomes, which may not provide sufficient detail for comprehensive risk management. Our models deliver nuanced predictions, such as the likelihood of loan repayment within specific time frames (e.g., 0-60 days late), predictions presented in a custom number of classes, probabilities assigned to these predictions and other options, enabling more informed decision-making.

Inefficient Data Utilization → Financial data is frequently underutilized in strategic planning. We offer in-depth analysis of financial datasets to extract actionable insights, empowering businesses to make data-driven decisions and improve financial forecasting.

Overlooked Sector-Specific Risks → Many financial models do not consider industry-specific variables that can significantly impact a company’s financial health. Our models incorporate these factors, offering a more accurate risk assessment tailored to each sector. Our models also can incorporate non-standard data, such as social media sentiments, media reports, court information and other sector-specific data sources.

Manual or Outdated Financial Processes → Many companies rely on manual or outdated methods for analyzing financial data and making credit decisions. We provide automated solutions that streamline data analysis and enhance decision-making efficiency, reducing human error and improving accuracy.

How does it work?

The client provides data of current or historic clients, suggestions for data sources or data types that should be included.

• Binary classification: when percentage of positive cases is < 10%, number of positive cases should be at least 200-300.

• Multiclass classification: when number of classes is higher (more than 8), results tend to be worse using < 4000 cases. But once size of dataset reaches 4000-5000 cases, adding more data points do not improve performance in general.

• A simple 2-class predictor should be sufficient for about 2000 observations, of which at least 10% should belong to the smaller class. There will be no scores > 90%, but it is already usable in practice.

We use this provided data to build custom credit scoring or risk assessment models, communicating with the client during the whole process to make sure that the solution reflects the needs of the business.‍

Results

The model outputs detailed insights showing the impact of each feature on scoring decision. This information can be used for further development of the model or during other scoring processes.

Such a solution could be integrated into existing client’s IT systems and business processes. We can provide a Docker file with the model available through API, which can be hosted using the client’s infrastructure or other cloud computing solutions and can be integrated into the existing system.