Integrate identity, device, behavior, channel, repayment, customer service, and external data sources.
AI Risk
Make Dimens Copilot the core of intelligent decisioning
Dimens Copilot does not leave AI as an isolated feature. It embeds intelligence into data ingestion, feature engineering, model scoring, rules, strategies, gray release, monitoring, review, and post-loan operations.
Build user profiles, relationship networks, device fingerprints, behavior-shift features, and channel-quality features.
Support onboarding, credit decisioning, anti-fraud, limits, in-loan alerts, and post-loan strategies.
Support rule combinations, strategy orchestration, gray release, version management, and rollback.
Track model performance, asset quality, strategy hit rates, and operating results.
Agentic AI risk management
The risk Agent does not evaluate risk in isolation. It shares operating context with acquisition, marketing, operations, and post-loan Agents
In financial services, risk is not confined to the moment of approval. Channel quality, marketing promises, user behavior, customer service touchpoints, and post-loan feedback all affect asset performance. Dimens Copilot connects these data points through multi-agent collaboration.
Cross-stage risk awareness
When the acquisition Agent detects abnormal channels and the marketing Agent identifies abnormal conversion, the risk Agent can raise onboarding thresholds or trigger review.
From recommendation to strategy flow
Recommendations from different Agents enter Dimens Copilot, where rules, models, permissions, and human review determine execution.
Post-loan feedback loop
The post-loan Agent feeds outreach, promises, repayments, and complaints back into the system to improve risk models and marketing strategies.
Why AI Matters
Digital lending in emerging markets needs explainable, monitorable, and iterative intelligent decisioning
Dimens Copilot places model scores, rules, strategy release, and performance monitoring in one decisioning system, so every approval, rejection, and strategy adjustment has a clear basis.
Sparse data
Where traditional credit data is limited, device, behavior, relationship, channel, and operating data help better understand users.
Fast-changing fraud
Fraud rings, channel quality, and user behavior can change quickly, so models and strategies must keep iterating.
Operational complexity
Multi-country, multilingual, multi-team operations need systems and AI to stabilize service quality.
Capability Modules
Put Dimens Copilot into real business workflows, not just model demos
Dimens Copilot
AI Decisioning Core
Place identity, fraud, credit decisioning, in-loan alerts, post-loan outreach, and model governance into one strategy flow with continuous monitoring and review.
Identity verification
Combine document recognition, liveness detection, device fingerprints, and local data sources to improve onboarding efficiency.
Abnormal relationship detection
Identify abnormal devices, fraud rings, channel anomalies, and high-risk behavior combinations.
Limit and pricing
Use scoring models, rules, and real-time decision flows to support limit, term, and pricing decisions.
Risk alerts
Monitor behavior shifts, asset quality changes, and strategy performance to identify risk earlier.
Intelligent outreach
Improve post-loan operations through customer service, outbound calls, task flows, and quality assurance.
Auditable models
Manage model versions, strategy changes, gray release, and performance review so decisions are traceable.