Unitforce Technologies
Consulting Pvt Ltd
Fraud flagged as it happens and credit scored at the point of decision — with reasons an auditor will accept.
AI that flags fraud as it happens and scores creditworthiness at the point of decision — trained on your transaction and repayment data, tuned to your risk appetite, and explainable enough for auditors and regulators.
Fraud patterns and credit behaviour are specific to a book of business. A model trained on somebody else's transactions will find somebody else's fraud, which is why we build on your transaction, application and repayment history rather than shipping a generic scorer.
Thresholds are a business decision, not a technical one. False positives cost customer friction and review capacity; false negatives cost money. We tune the operating point with the people who own that trade-off, and make it adjustable afterwards.
A score that arrives after the payment has settled is a report, not a control. These models are served inline — scoring within the decision window, so a transaction can be held, stepped up or declined while it still can be.
Anomaly detection runs alongside supervised scoring, because the useful signal in fraud is often the pattern nobody has labelled yet.
In lending and payments, a decision you cannot explain is a decision you may not be allowed to make. Every score comes with the factors that drove it, in terms a reviewer, a customer-facing team and a regulator can each read.
Reporting is built for audit from the start: model versions, training data lineage, threshold changes and individual decisions are all recorded, so a review months later has something to look at.
Every flagged transaction that is not auto-declined lands with a human, and the quality of that queue decides whether the model is an asset or an overhead. We design it to carry the reason for the flag and the comparable history, so a reviewer can settle a case rather than start an investigation.
Those decisions feed back as labels. A review queue that records why an analyst overruled the model is the cheapest source of training data a fraud team has, and it is normally the difference between a model that ages well and one that quietly stops earning its keep.
Yes — models are served inline so a transaction is scored inside the decision window and can be held, stepped up or declined before it completes, rather than flagged after settlement.
Each score is returned with the factors that drove it and their contribution, alongside a record of the model version, its training lineage and the thresholds in force at the time. That combination is what makes a decision reconstructable in an audit.
Yours. Fraud and credit patterns are specific to a portfolio, so models are trained on your transaction, application and repayment history and tuned to your own risk appetite.
Supervised scoring is paired with anomaly detection, which looks for behaviour that departs from the established pattern rather than matching a known label. New fraud typically shows up there first.
Agents that do the work, not just describe it — wired into the systems your team already uses.
Generative AI that answers from your facts, cites where they came from, and says nothing when it has no evidence.
An assistant inside the tool people already have open, grounded in what your company actually knows.
Whole processes automated end to end, with the exceptions routed to people instead of hidden from them.
The unglamorous half of AI — versioning, serving, monitoring and rollback — owned by us so your team doesn't carry it.