Alternative credit scoring, fraud detection, and document intelligence — built to speed up your underwriting and risk processes while keeping a human in the loop for the decisions that need judgment.
Assess borrowers using non-traditional data signals beyond formal credit history.
Real-time flagging of transactions that deviate from normal account behavior.
OCR + LLM extraction from income statements, IDs, and bank statements.
AI handling routine inquiries so agents focus on complex cases. See AI development →
Recommend the right financial product based on behavior and life-stage signals.
Automated flagging and draft reporting to reduce manual compliance workload.
Alternative credit scoring uses non-traditional data, transaction history, e-wallet behavior, device metadata, to assess creditworthiness for borrowers without a formal credit history. AI models trained on this data can extend responsible lending to segments traditional scoring misses.
In our builds, no, AI surfaces risk signals and pre-scores applications so underwriters can prioritize their time, while final judgment calls stay with a person. Full automation is possible for very low-risk, high-volume products, but we always design with a human review path available.
Models learn typical transaction and behavior patterns for each account, then flag anomalies in real time, a login from an unfamiliar device, an unusual transfer pattern, for review or automatic blocking, depending on your risk tolerance.
Yes, document intelligence combining OCR and LLM-based extraction can read income statements, ID documents, and bank statements, structuring the data for your underwriting system automatically.
A focused model like risk pre-scoring or document extraction typically runs $10,000-$30,000. Broader AI programs across multiple use cases scale from there. See our AI Development and Fintech Software Development pages for related pricing guidance.
Tell us about your current process and we'll assess where AI can realistically help.
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