What fraud and risk teams in india must prepare for in 2026

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Platform

Solutions

Resources

About Us

Alternate Data

Risk decisions on the applicants your current models can't score.

60% of applicants arrive without the bureau data traditional models need. Bureau's ML models read the signals every identity leaves - across telco, device, email, network, and behavioral dimensions - and produce calibrated risk scores for onboarding fraud, credit risk, mule detection, and payment decisions.

See Bureau Alternate Data in Action

80%

Synthetic identities caught at onboarding

80%

Synthetic identities caught at onboarding

80%

Synthetic identities caught at onboarding

80%

Fraud ops time saved

80%

Fraud ops time saved

80%

Fraud ops time saved

76%

Faster onboarding

76%

Faster onboarding

76%

Faster onboarding

RISK INTELLIGENCE

Engineering trust from every digital signal

Most alternate data vendors return raw signal attributes. Bureau delivers the out-of-box and custom ML models trained on diverse signals, confirmed fraud and default outcomes, and calibrated risk scores with full explainability - turning fragmented behavioral signals into decisive, audit-ready risk outputs.

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Every identity leaves a signal trail.

Phone numbers, email addresses, device patterns, and network connections exist for virtually every user. Bureau processes these across six signal dimensions into 200+ engineered model features, validated for consistency and cross-source alignment before they enter any scoring pipeline.

Telco & email intelligence - phone tenure, porting history, domain age, and cluster patterns separate real identities from burner accounts
Digital footprint - presence on platforms to validate an active digital presence and expose synthetic identities
Network intelligence - Relationships with risky user clusters based on user-provided identifiers
Identity consistency - cross-source alignment of name, phone, and address flags synthetic identities at input

De-Risk Your Thin-File Lending Strategy.

Accurately score thin-file and new-to-credit applicants for lending or credit worthiness.

A structured, risk-based approach to credit underwriting.
Categorized into low, moderate, and high risk. Helps you improve the experience for the good user.

Network-level screening for profiles

With our Graph Identity Network, assess identity trust, detect potential risks, and map interconnections between identities leveraging feedback data from BFSI, Fintech, and other relevant industries.

Multi-level checks - Start with level 1 and go deeper to level 2 checks to identify potential ring operations.
Stop risky users with suspicious behavior before they become a part of your system.

Predict economic reliability

Affluence score acts as a solid economic indicator before deciding to onboard a user.

Strong correlation between affluence score and potentially risky accounts
Automatically filter out cohorts of users that don’t meet your requirements.

00
01

Every identity leaves a signal trail.

Telco & email intelligence - phone tenure, porting history, domain age, and cluster patterns separate real identities from burner accounts
Digital footprint - presence on platforms to validate an active digital presence and expose synthetic identities
Network intelligence - Relationships with risky user clusters based on user-provided identifiers
Identity consistency - cross-source alignment of name, phone, and address flags synthetic identities at input

De-Risk Your Thin-File Lending Strategy.

Accurately score thin-file and new-to-credit applicants for lending or credit worthiness.

A structured, risk-based approach to credit underwriting.
Categorized into low, moderate, and high risk. Helps you improve the experience for the good user.

Network-level screening for profiles

With our Graph Identity Network, assess identity trust, detect potential risks, and map interconnections between identities leveraging feedback data from BFSI, Fintech, and other relevant industries.

Multi-level checks - Start with level 1 and go deeper to level 2 checks to identify potential ring operations.
Stop risky users with suspicious behavior before they become a part of your system.

Predict economic reliability

Affluence score acts as a solid economic indicator before deciding to onboard a user.

Strong correlation between affluence score and potentially risky accounts
Automatically filter out cohorts of users that don’t meet your requirements.

The Network Differentiator

Bureau’s cross-merchant intelligence network connects signals across institutions, geographies, and merchants. Combined with Bureau’s unified decisioning engine, this gives risk teams a view of coordinated fraud rings that no single-platform tool can replicate.

Talk to a Fraud Expert

How it works

Flexible Models for Complex Risk Decisions

Ready on day one. Sharper with every confirmed outcome.

Bureau builds domain-specific models - each trained on a distinct fraud type. Three deployment tiers to let you start scoring immediately, then improve discrimination as confirmed fraud labels accumulate from your own portfolio.

Unsupervised tier - pre-trained risk scores using alternative data for immediate assessment

Supervised tier - trained on confirmed fraud and default labels

Client-supervised tier - your institution's fraud labels layered on Bureau's network intelligence, calibrated to your portfolio

use cases

Know every risk. Act on every signal.

Bureau's alternate data models are built for risk and data science teams across financial services, fintech, e-commerce, and digital platforms - wherever traditional scoring leaves the majority of applicants unscored.

New-to-Credit Underwriting

Onboarding Fraud Prevention

Money Mule Detection

Chargeback & Payment Fraud

MSME Credit Assessment

Lead Scoring & Pre-Qualification

Customer STORIES