01 / Your starting data
Use historical transactions, claims or administrative files with known investigation outcomes. Select the information available at review time and define the categories with your investigators.
FRAUD DETECTION SOFTWARE + CONSULTING
Fraud detection software. Explain why a case needs review.
Give banking, insurance and public-sector investigators a clear starting point: a proposed review and the data patterns behind it.
Neurytika turns structured data into classifications with inspectable supporting patterns. Build a fraud detection workflow around your historical data, your investigators and your choice of infrastructure.
Software + expert consulting · On premises or cloud
01
Transaction, claim or case
Structured fields + historical outcomes
02
Neurytika prediction
Patterns associated with fraud labels
03
An explained referral
Evidence for an investigator to examine
Your data. A prediction. Evidence you can inspect.
YOUR FIRST PILOT, MADE CONCRETE
Build your pilot around the cases your investigators know.
02 / What your team receives
Review example flagged cases alongside the combinations of fields and historical support contributing to each classification. Investigators retain the decision and can document the reasons they examined a case.
03 / How we measure value
Evaluate detected fraud cases, false alerts and cases missed on reserved historical data. Compare the findings with your current rules and the review capacity of your team.
Start with a conversation about your objective and available records. Data access, scope, schedule and fees are agreed in your proposal.
Explore a pilot on your data →Built around your dataEvidence behind each predictionDeployment with your team
FOR FRAUD TEAMS, CLAIMS OPERATIONS & PUBLIC-SECTOR CONTROL
Choosing a fraud detection solution?
You hold structured transactions, claims or case files and outcomes from past reviews. You want to identify records for investigation and make the reasons for referral visible to your reviewers.
See how we scope a project ↓What your team gets
A configured classification model, evaluation on your historical records and inspectable patterns supporting the predicted review category.
How it fits your systems
We scope how transaction, claim or case records reach the model and how the prediction and explanation are delivered to investigators in your existing workflow.
One use case. A clear objective.
Agree who uses the result, which outcome matters and what information is available when the prediction is made.
A focused pilot on your data.
Define the review category and the point of referral. Evaluate identified cases, legitimate records referred and the volume your team can investigate.
A workflow your team can use.
Implement the agreed integration, install locally or in your preferred cloud, and train your team to review predictions and their evidence.
Make every referral useful to the reviewer.
A review recommendation is most useful when the team can see what contributed to it. Neurytika exposes feature combinations, support counts and their association with known outcomes, giving investigators concrete starting points for their assessment.
Banking
Review transaction patterns
Explore combinations of transaction attributes and historical fraud outcomes. Give analysts a clear explanation to accompany a proposed review.
Insurance
Examine claims in context
Use structured claim details and resolved outcomes to identify combinations associated with fraud labels and guide an investigator’s next questions.
Public administration
Explain why a file is proposed for control
Support the selection of cases for human review with visible contributing characteristics and the observations behind them.
YOUR PROJECT, MADE CONCRETE
Build around your review capacity.
Together, we define the fraud labels, the data available at the time of a decision and the volume of referrals your team can investigate. We evaluate precision, recall, false positives and the usefulness of explanations on your data, then agree how the model fits your review process.
Discuss your projectWhat we build with you
- A model configured for your transaction, claim or case data
- A review workflow with explanations for proposed referrals
- Evaluation against historical outcomes and an agreed review capacity
- Deployment and team training on your infrastructure or in the cloud
Start with a focused pilot: agree the objective, evaluate the model on your data and define the path to operational use.
THE TECHNOLOGY BEHIND NEURYTIKA
Published science.
Inspectable evidence.
Neurytika is powered by the Evidence Ratio Classifier (ERC). Read the peer-reviewed paper for its learning rule, experimental protocols and benchmark results, including the synthetic card-fraud experiment.
Read the open-access ERC paper ↗How do we detect fraud
in our own records?
Start with structured transactions, insurance claims or administrative files and the outcomes of previous investigations. We define the review category, prepare the records and evaluate a model around the point at which your team would act.
For banking teams, this can mean identifying transactions for additional review. For insurers, it can mean flagging claims for investigation. For administrations, it can mean prioritizing files for control. Each referral brings contributing patterns and their observed support into the reviewer’s workflow.
Agree the evaluation around the review process: identified fraud cases, referrals that prove legitimate and the number of files the team can investigate. We scope a pilot around your records and review capacity.
Discuss your fraud review workflowSOFTWARE + IMPLEMENTATION PROPOSAL
What will your project include?
We prepare a proposal around your objective, data, integration and deployment needs. Your scope can cover: software licensing, preparation of investigation outcomes, model configuration and evaluation, workflow integration, local or cloud deployment and team training.
Start with a focused pilot or discuss the implementation you already have in mind.
Discuss scope and pricingMake the first conversation useful.
Tell us:
- The business outcome and team you want to support
- The records and historical outcomes you already hold
- The systems to connect and your deployment preference
- Your intended timeline and project scope
We review the use case with you and define the next step for a demonstration, evaluation or tailored proposal.
Your questions,
answered.
What should we compare when choosing fraud detection software?
Start with your transaction or case data, the outcome you need to identify, the explanations investigators need and the review capacity of your team. A Neurytika pilot evaluates the model on your records and scopes integration and deployment around that workflow.
What data do we need?
Structured historical records with known outcomes are a useful starting point. We help identify the fields, labels and preparation needed for your fraud detection project.
What does the explanation show?
The combinations of features contributing to a prediction, alongside their observed support and association with classes in the training data.
Who makes the final assessment?
Your investigators review the proposed case and its supporting evidence. We shape the software workflow around their expertise and your internal process.
Can fraud data stay on our infrastructure?
Yes. Neurytika can be installed locally so datasets and models remain in your environment. We also support a cloud setup if you prefer.
LET’S BUILD YOUR SOLUTION
Bring your use case.
Let’s shape the next step.
Tell us what you want to predict, the data you work with and your preferred deployment environment. We will discuss a demonstration and a tailored project scope.
Request a demoYou can describe your project before sharing any dataset. FormSubmit forwards your message to our sales team.
EXPLORE MORE
START WITH YOUR BUSINESS QUESTION