AdalFi is API-first and pre-integrated with leading core banking platforms, including Oracle FLEXCUBE, Temenos, and Symbols. Our SDKs and drop-in UIs embed seamlessly into your mobile, web, and branch channels. Deployment can be on-premise, in your private cloud, or a regulated in-country public cloud environment, ensuring full compliance with data residency and security requirements.

Because of our pre-integration with core systems and ready-made UI components, lenders can launch in weeks, not months. Minimal IT intervention is required, and integration can be phased to match operational priorities.

All data is encrypted in transit (TLS 1.2+) and at rest. Personally Identifiable Information (PII) never leaves your systems. We operate under strict principles of least privilege access controls, maintain immutable decision logs, and design for ISO 27001 compliance.

No. In federated learning, no raw transaction data, customer information, or identifiable bank data ever leaves your environment. Only anonymized model updates are shared, ensuring compliance with all privacy and data protection regulations.

It ingests data from core banking systems, Open Banking APIs, credit bureaus, and third-party sources, processing billions of transactions across millions of customers. AAA hosts our AI scoring models, feature library, and decisioning engine, providing explainable outputs for compliance, automated underwriting aligned to lender policies, and precise credit limit assignment.

It also closes the loop by incorporating repayment outcomes and account activity from the Optimize stage, enabling continuous model retraining and score refinement. The AAA can be deployed on-premise, in a lender’s private cloud, or a regulated in-country public cloud, ensuring full compliance with data residency and security requirements.

• Inner Loop: Runs on-premise within each lender, learning from daily transaction data and monthly repayment outcomes to refine scoring for that specific portfolio.
• Outer Loop: A federated learning layer that aggregates anonymized model updates from all AdalFi partner banks to improve model performance across the partners — without sharing identifiable data.

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Unlike traditional credit scoring, AdalFi leverages bank transaction data to discern income patterns, spending velocity, balance trends, and other such features to evaluate creditworthiness. This enables accurate scoring for thin-file, new-to-credit individuals without traditional bureau histories, and SMEs with highly variable cash flows.

AdalFi has three major families of models: Salaried Consumers, Non-Salaried Consumers, and SMEs. Each model family is tuned with features specific to the economic and financial realities of the prospect segment.

For prequalified customers, the full Assess → Activate → Disburse process can be completed in under a minute. Scoring and decisioning typically occur in <300 ms P50 latency, even at scale, ensuring a real-time customer experience.

Yes. Our models dynamically score SMEs using transaction data to assess cash flows and enable digital onboarding, real-time risk assessment, and automated credit limit assignment tailored for highly variable business cycles.

Optimize continuously monitors deposit account activity, cash flow changes, and repayment patterns. Early warning indicators such as sudden balance drops or spending shifts trigger risk reviews and proactive borrower engagement. All outcomes feed back into the AdalFi Analytical Architecture for model retraining and score refinement, making future credit decisions more precise.

Lenders retain complete control over credit policies, thresholds, and limits. AdalFi’s decisioning engine combines AI scoring with your own business rules, affordability caps, and compliance requirements to ensure every decision aligns with your risk appetite.