Megaladata Scorecard Modeler

Management

Megaladata Scorecard Modeler automates the creation of scoring models to assess borrower creditworthiness, reduce default risk, and lower associated costs for financial, collection, and insurance companies.
Scoring is the industry standard for credit risk analysis, enabling organisations to quantitatively evaluate client reliability and credit worthiness .

Over 90% of banks use specialized tools to build and update these models regularly.

How it works:

Load borrower data from files, databases, or business applications, using any number of characteristics available for modelling
Perform lifecycle analysis,including vintage analysis, migration matrix construction, and account status determination
Select significant factors through correlation analysis and binning ( automatic and manual)
Build the scoring model, generate the scorecard and calculate scoring points
Evaluate model quality using metrics like GINI, ROC curve and KS
Continuously monitor the scorecard for stability and drift using PSI and other indicators

Megaladata Scorecard Modeler is especially suited for:

Banks and credit institutions needing to build and update internal scoring models
Microfinance and leasing companies aiming to reduce default rates and improve portfolio quality
Insurance providers assessing client risk profiles using data-based scoring
Collection agencies prioritizing and segmenting debt recovery strategies

By automating the entire scorecard development process: from sample preparation to model monitoring, Megaladata Scorecard Modeler reduces the workload for underwriters and credit analysts while improving the consistency and quality of credit decisions.

View website

Sampling Preparation & Life Cycle Analysis

Loads borrower data from files, databases, and business apps with unlimited characteristics, then runs full lifecycle analysis: delinquency auditing, vintage analysis, migration matrices, and train/test splits.

Feature Selection & Modelling

Identifies significant variables through correlation analysis and automatic/manual binning, builds a credit repayment probability model, generates the scorecard, calculates scoring points and converts regression coefficients into scores.

Model Quality Assessment & Monitoring

Model quality is assessed using GINI, ROC curve, KS, and other industry-standard metrics. Ongoing scorecard monitoring tracks the Population Stability Index (PSI), score distribution relative to threshold, and changes in Gini and other indices over time.

Features

Calculation of Auxiliary Borrower Characteristics

Megaladata supports integration with multiple data sources used in credit scoring, including credit bureaus, anti-fraud services, government databases and social networks.
Custom Scoring Model Development

Megaladata's data scientists can develop bespoke scoring cards tailored to specific business needs and client segments.
Integration with Decision Pipeline

Scoring cards built in Megaladata Scorecard Modeler can be embedded directly into business processes using Megaladata Decision Maker.
Low-Code Configuration

The scoring system can be configured and modified independently, without hard coding skillsIncreasing operational efficiency and scalability and minimising the impact of human bias.
Optimal Credit Risk Management

Enables the development of unique client strategies based on calculated risk values, with the ability to adapt to changing market conditions quickly.
Lower Default Rates & Higher Revenue

Improves credit portfolio quality through the application of ML-based and data mining borrower assessment models.
Reducing delinquency rates and raising income

Improving the quality of the loan portfolio through the use of borrower assessment models based on machine learning, data mining and use of historical data.
Train/Test Split & Class Balancing

The system automatically splits borrower datasets into training and test sets and applies class balancing techniques, ensuring scoring models are built on statistically sound and unbiased samples.

Vendor

Deployment time: Demo available on request; implementation tailored to business needs and client segments.

Megaladata is a low-code data analytics platform based in Yerevan, Armenia. The company provides tools for businesses and data professionals seeking speed, flexibility, and efficiency in data management Megaladata Scorecard Modeler is a specialized solution built on the Megaladata platform, designed for credit risk management, scorecard development, and financial analytics.

More info