Artificial Intelligence · Build
We build fraud detection that combines deterministic rules with models, keeping each where it belongs rather than replacing sound rules for their own sake. Thresholds are tuned to the real cost of a missed case against a blocked good customer, because the right balance is a business judgement we make with you. We make no claim to stop all fraud, and we treat any vendor that does with suspicion; the goal is a measured reduction in loss at an acceptable false-positive rate. Because attack patterns shift, the system is monitored and retuned rather than set once and left.
In short
Fraud detection models identify suspicious transactions and behaviour, balancing losses prevented against legitimate customers blocked. The threshold is a business decision about that trade-off, not a technical default.
Start a conversationWhat we do
We study your confirmed cases to understand the patterns genuinely occurring in your business rather than generic fraud typologies. What works for another organisation may miss what is actually happening in yours. This grounds the whole design in your real exposure.
We keep deterministic rules where they are clear and defensible, and add models for the patterns rules cannot express. Rules give certainty and explainability; models catch the subtler, shifting behaviour. Combining them is more robust than relying on either alone.
We set thresholds against the real cost of a missed fraud case versus a blocked legitimate customer. This makes an explicit, reviewable business decision out of what is too often an arbitrary setting. The expected trade-offs are quantified so leadership can own the choice.
We give analysts the evidence they need to decide quickly, and feed their confirmed outcomes back into tuning. A tight feedback loop keeps the system current as fraud evolves. It also means analyst effort improves the model rather than being spent in isolation.
What you receive
Documented output your teams keep and can act on without us in the room.
When this work helps
How an engagement runs
Understand the workflow, the data available, the constraints, and what a good outcome would look like. We establish the problem precisely enough to judge whether AI is even the right instrument for it.
Review data protection, security, oversight, and feasibility for the specific use cases on the table. We report which cases are worth building, which are better solved another way, and what would have to be true to proceed safely.
Set out the approach, controls, governance, and evaluation measures before build begins. Deciding data boundaries, oversight, and how success will be measured up front keeps them from becoming retrofits.
Support implementation and evaluate performance, error modes, and control effectiveness in use. We measure the system against the criteria agreed at design, and report honestly where it falls short.
Where we work
Fidova advises on AI adoption and AI governance from Delhi, India, and works with organisations internationally, including teams that must satisfy overlapping regimes such as India's data protection law, the EU AI Act, and GDPR.
Engagements run in English, are scheduled around your time zone, and can be delivered remotely end to end, with on-site time planned where a phase genuinely benefits from being in the room.
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Questions
Straight answers to what buyers ask us before an engagement begins.
Enquiries
Your enquiry lands in the same inbox as every other on this site and is read by us. Tell us the requirement and we will reply with a practical next step.
Prefer email? contact@fidovaadvisory.com
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