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Description
Rapid and Responsible Decision-Making with Structured Data. Facilitating well-informed financial and advisory decisions through secure access and meticulous analysis of intricate data sets. What is MOGOPLUS? MOGOPLUS offers Data, Analytics, and Research solutions designed to empower organizations to make timely and informed choices. The landscape is evolving due to stringent regulations, advancing technologies, and shifting customer expectations, leading to significant transformations across various global industries. Effectively harnessing customer data to expedite credit decisions, evaluate risks proficiently, and enhance customer experiences has become a vital competitive advantage in today's data-centric economy. MOGOPLUS adeptly captures unstructured customer data, analyzes it in real-time, and reformulates it into a format compatible with any decision-making system, rules engine, or credit policy. Furthermore, all MOGOPLUS offerings are accessible through a user-friendly API or a no-integration, web-based dashboard, making it seamlessly integrable with any third-party platform. Our patented technology ensures a cutting-edge approach to data analysis and decision-making. With MOGOPLUS, organizations can stay ahead in a rapidly changing market by leveraging the full potential of their data assets.
Description
Our platform offers ready-to-use APIs that integrate both conventional and alternative credit data sources, facilitating quicker data ingestion for more accurate credit assessments. It features a robust predictor library built on extensive credit expertise, along with pre-configured attributes that enhance credit decision-making. Our proprietary AI and ML credit modeling approach is fully explainable and yields substantial improvement in outcomes. Users can simultaneously run multiple champion-challenger models, allowing for comparative analysis of credit strategies within a single streamlined workflow. Deployment of new credit models and strategies is swift and efficient. Our AI-driven credit underwriting models are not only explainable and FCRA-compliant but also designed to be highly reliable. They include automated and simplified reasoning for adverse actions, ensuring transparency. Comprehensive documentation is provided, detailing the logic behind the models, their robustness, and any limitations. The attributes of our models are subjected to rigorous disparate impact assessments to confirm the absence of bias in their design. Furthermore, our AI credit models offer a wide and varied range of reasons for adverse actions, ensuring that users have a comprehensive understanding of the decision-making process and its implications. Overall, this combination of features empowers organizations to make informed and equitable credit decisions.
API Access
Has API
API Access
Has API
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Mogoplus
Country
Australia
Website
mogoplus.com
Vendor Details
Company Name
Scienaptic AI
Founded
2014
Country
United States
Website
www.scienaptic.ai/
Product Features
Data Analysis
Data Discovery
Data Visualization
High Volume Processing
Predictive Analytics
Regression Analysis
Sentiment Analysis
Statistical Modeling
Text Analytics
Product 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