Incharge provides an AI-based Scoring and Action Engine solution for credit scoring and collections management. Their system scores and segments users in real-time based on various metrics to provide early warnings regarding potential defaults. The platform automates follow-up actions, such as notifications and warnings, while also suggesting manual actions for operational teams.
Funding
Funding not disclosed
Founders
Product
Problem
Mobility platforms and lenders often lack real-time visibility into rider behavior, leading to delayed identification of payment risk, safety concerns, and operational inefficiencies. Traditional credit scoring models are static and require extensive manual data processing, which hampers timely intervention and increases default rates.
Solution
Incharge delivers an AI-driven Scoring Engine that ingests transactional and operational data to segment riders instantly on metrics such as payment performance, safety incidents, and effort levels. The engine generates early‑warning signals that flag emerging risk before defaults occur. Complementing the scoring layer, the Action Engine automates follow‑up actions—including targeted notifications, warnings, and training prompts—to guide riders toward corrective behavior. Both engines operate on a continuously updated data lake, enabling real‑time analytics and reducing the manual effort required for collections management. The solution integrates via APIs and a web dashboard, allowing platform operators to monitor risk indicators and adjust intervention rules without code changes.
Target Audience
The primary customers are e‑mobility fleet operators, ride‑hailing platforms, and financial institutions that provide credit to rider partners and need automated risk management at scale.
Features
- AI Snapshot template that ingests rider data and returns scoring insights within 60 seconds
- Real‑time segmentation model that scores riders on payment, safety, and effort metrics
- Early‑warning alerts delivered through configurable thresholds and risk scores
- Automated action workflows that trigger notifications, warnings, and training modules based on risk triggers
- Centralized data lake architecture for continuous data refresh and historical trend analysis
- RESTful API and web dashboard for rule configuration, monitoring, and reporting
- Role‑based access controls and encryption to ensure data security and compliance