AmazonFrom a single phone number or email to a risk decision in under a second.
Platform Capabilities
Revolutionize your risk assessment
YuALT is the complete no-code platform for building robust credit risk and fraud detection models using both traditional and alternative data sources.
No-Code Platform
Build ML models with an intuitive drag & drop interface - no coding experience required.
200+ Data Signals
Access a vast library of alternative data signals to enhance your risk models.
Advanced Analytics
Visualize model performance with interactive dashboards and real-time insights.
Enhanced Risk Management
Improve credit decisioning with more accurate risk assessment and fraud detection.
Regulatory Compliance
Models are designed with transparency and explainability for regulatory requirements.
Rapid Deployment
Deploy models to production in minutes with our streamlined workflow.
Data Coverage
Alternate Data, Sampled
From a single phone number or email, YuALT detects presence across hundreds of consumer platforms to build a behavioral signal profile — powerful for thin-file and new-to-credit applicants.
E-commerce
AmazonSocial Media
InstagramMessaging
Entertainment
Financial
Travel
Lifestyle
* Detects only whether an identity is registered on a platform, via phone number or email — no personal content is read.
Signal Depth
Depth of Alternate Data Profiling
From four anchors, YuALT builds a granular profile that separates authentic applicants from fraudulent ones.
Mobile Number
Basic Information
- Validity, type (prepaid/postpaid), SIM tenure
Activity & Usage
- Porting history, carrier changes, spam detection
- Top-up patterns, last deactivation date
Network Details
- Current network, routing info
Social Connections
- Profiles on messaging, social & professional platforms
Domain Analysis
- Provider type, tenure, registrar details
- Suspicious domains, website presence
Breach History
- Number & details of breaches (platforms, dates)
Platform Connections
- Messaging, professional, travel, entertainment profiles
Reputation Indicators
- Deliverability and spam risk
Employment & Company
Employment History
- Employers, roles, tenure, LinkedIn details
Education Background
- Schools, degrees, fields of study, graduation
Company Analysis
- Size, domain reputation, employee activity, reviews
Social & Digital Connections
Breadth of Profiles
- Social, messaging (Botim, Telegram), e-commerce (Amazon, Noon)
Depth of Activity
- Last seen, privacy settings, linked networks
Professional Indicators
- LinkedIn, GitHub, tools like Adobe, Microsoft
Personal Interests
- Entertainment (Netflix, Spotify), Travel (Airbnb, Booking)
Enables robust differentiation between authentic and fraudulent behaviours by leveraging granular insights from alternate data.
Use Cases
Transform your business
See how leading financial institutions are using YuALT to enhance their risk management and decision-making.
Credit Underwriting
Increase approval rates while maintaining risk thresholds by incorporating alternative data into credit decisions.
higher approval rates, with no rise in defaults
Fraud Prevention
Detect sophisticated fraud patterns with machine learning models that adapt to evolving tactics.
reduction in fraud losses within 3 months
Customer Segmentation
Create precise customer segments based on behavioral patterns for targeted product offerings.
improvement in marketing conversion
Risk-Based Pricing
Dynamically adjust pricing based on comprehensive risk assessment of each customer profile.
increase in portfolio profitability
See these results on your portfolio
Score thin-file and underbanked applicants on your own data — no formal credit history required.
Built for the UAE
Alternate-data credit scoring, localized for the UAE
Score thin-file and underbanked UAE borrowers in under a second — from 200+ signals localized to the Emirates, with no formal credit history required.
Professional Indicators
- Owler
- Glassdoor
E-commerce Indicators
- Carrefour
- LuLu
- Deliveroo
- Namshi
Lifestyle Indicators
- Platinumlist
- Anghami
- Emirates Skywards
- Musafir
- flydubai
- Property Finder
Partners across the UAE
Case Study · UAE
The challenge
A major retail bank in the UAE relied on traditional credit models — bureau data and financial statements — to assess borrowers. But much of its base was underbanked, with no formal credit history or limited banking activity, making creditworthiness hard to judge.
Outcome
How we solved it
- 1
Trained ML models on the bank's historic data, augmented with multiple alternate-data sources for their defined target.
- 2
Assessed the model on their untagged out-of-time (OOT) data for risk ranking.
- 3
Alternate-data signals proved highly effective at capturing the bad segments, cutting onboarding risk on new customers.