SentiLink Protects Fintechs from Synthetic Identity Fraud with Custom ML

Rishad Al Islam

•4 min read
a man looking at a computer screen with a dollar sign on it

System Overview

What it is: SentiLink developed a proprietary machine learning model to identify and prevent synthetic identity fraud. The system analyzes application data patterns, detects anomalies, and flags suspicious accounts, helping fintech companies dramatically reduce fraud losses.

Core capabilities

  • ML model trained on millions of identity data points
  • Detection of synthetic and manipulated identities
  • Real-time fraud scoring during account applications
  • API integration with onboarding and KYC systems
  • Continuous retraining for evolving fraud tactics
  • Dashboards for fraud team alerts and case management
  • Compliance-ready audit logs for regulatory oversight

Business problems solved

  • Rising risk of synthetic identity fraud in financial services
  • High costs of fraudulent accounts and charge-offs
  • Difficulty detecting fraud with traditional rule-based systems
  • Compliance risks from onboarding bad actors
  • Manual reviews slowing down customer onboarding

Industries served

Fintech, banking, insurance, lending, payments, digital identity verification.

Actor Identification

  • Primary actor: Applicant submitting an account or loan application.
  • Secondary actors: SentiLink ML fraud detection engine, fintech onboarding system, KYC/AML services, fraud analysts.

Want to ensure legitimate applicants get instant approval while blocking fraudulent accounts? See how AI can help.

Actor Goals

  • Applicant (legitimate): Get approved quickly without unnecessary delays.
  • Fraud Analyst: Focus on high-risk flagged cases instead of reviewing all applications.
  • ML Model: Detect synthetic identities in real time with high accuracy.
  • Onboarding System: Maintain seamless customer experience while reducing fraud risk.

Context and Preconditions

  • ML model integrated with fintech onboarding APIs
  • Historical fraud data used to train detection system
  • KYC/AML tools connected for identity validation
  • Fraud scoring thresholds defined for auto-approval, review, or rejection
  • Compliance approval for automated decisioning workflows

Basic Flow (Successful Scenario)

  • Applicant submits identity and account details via onboarding system.
  • SentiLink ML engine analyzes data patterns for anomalies.
  • If identity is genuine, application is approved instantly.
  • If synthetic fraud is detected, application is flagged for manual review.
  • Fraud analysts review flagged cases with model-provided insights.
  • Fraud outcomes logged for model retraining and compliance audits.

Outcome: Fintech companies reduce fraud by up to 90%, speeding onboarding for real customers while blocking synthetic identities.

Want to cut fraud by 90% while onboarding genuine customers faster? Let us show you how.

Alternate Flows

  • A1: False positive: If a genuine customer is flagged, manual review resolves and model adjusts.
  • A2: False negative: If fraud passes undetected, case is logged and model retrains to improve.
  • A3: API downtime: If fraud engine is unavailable, onboarding defaults to stricter manual checks.
  • A4: Regulatory change: If compliance requirements shift, detection workflows are updated accordingly.

Even in exceptions or system issues, SentiLink ensures fraud detection remains reliable - see how your platform can stay protected.