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What is Scienaptic?
Our system provides plug-and-play APIs that seamlessly incorporate both traditional and non-traditional credit data sources, enabling faster data assimilation for improved accuracy in credit evaluations. It boasts an extensive predictor library, rooted in deep credit knowledge, and includes pre-set attributes that optimize the credit decision-making process. Our unique AI and machine learning credit modeling techniques are fully transparent and significantly enhance results. Users have the ability to conduct multiple champion-challenger analyses concurrently, facilitating the comparison of different credit strategies within one cohesive framework. The rollout of new credit models and strategies is both rapid and effective. Our AI-based credit underwriting frameworks are designed to be not only transparent and compliant with FCRA regulations but also exceptionally dependable. They feature automated and straightforward reasoning for negative actions, ensuring clear communication. Thorough documentation is available, outlining the rationale behind the models, their strengths, and any constraints they may have. Each model's attributes undergo stringent disparate impact evaluations to ensure fairness in their construction. Additionally, our AI credit models provide an extensive array of explanations for negative actions, which guarantees that users are well-informed about the decision-making rationale and its consequences. This comprehensive suite of features ultimately equips organizations to execute fair and well-informed credit decisions while also fostering trust in the process.
What is Modeller?
In the current era of machine learning, software designed for model development leverages over three decades of expertise in credit risk modeling. Modeller stands out as a versatile, clear, and interactive tool that empowers organizations to maximize the potential of their analytical teams. It provides a wide range of methodologies, facilitates the swift creation of robust models, ensures comprehensive explanations, and nurtures the growth of junior team members.
Users can select from diverse modeling approaches, including machine learning, to attain the highest levels of predictive precision, particularly when dealing with intricate relationships and multicollinearity. With a simple click, one can generate standard industry binary and continuous target models. The software supports decision tree modeling through CHAID trees and CART methods, along with options like logistic regression, elastic net models, survival analysis (Cox PH), random forest, XGBoost, and stochastic gradient descent.
Furthermore, it offers export capabilities to SAS, SQL, and PMML, enabling seamless integration with other scoring and decision-making applications. This flexibility ensures that organizations can easily adapt the models to fit their specific operational contexts and requirements.
Integrations Supported
Plaid
API Availability
Has API
API Availability
Has API
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Company Facts
Organization Name
Scienaptic AI
Date Founded
2014
Company Location
United States
Company Website
www.scienaptic.ai/
Company Facts
Organization Name
Paragon Business Solutions
Date Founded
1991
Company Location
United Kingdom
Company Website
www.credit-scoring.co.uk/modeller
Categories and Features
Financial Risk Management
Compliance Management
Credit Risk Management
For Hedge Funds
Liquidity Analysis
Loan Portfolio Management
Market Risk Management
Operational Risk Management
Portfolio Management
Portfolio Modeling
Risk Analytics Benchmarks
Stress Tests
Value At Risk Calculation