01 / DISCOVER
Identify fraud patterns
Explore relationships among available features and surface opportunities that might be missed through manual, one-variable-at-a-time investigations.
FRO / AI fraud rule generation & optimizationAI fraud rule builder.
AI fraud rule builder.
Smarter rules.
FRO is standalone AI software for building and optimizing fraud detection rules. It is designed to discover complex patterns, recommend new rules, modifications and retirements, and evaluate their historical impact—without replacing your existing fraud rule engine.
What FRO does
Built to support analysts, not replace their judgment.
Building fraud detection rules can require analysts to investigate emerging patterns, evaluate feature combinations, tune velocity windows, test rule thresholds and monitor existing rules. FRO is being developed to automate more of this analytical work while keeping the analyst in control of rule approval and production deployment.
FRO combines proprietary pattern discovery, adaptive neural learning, and a specialized rule-building engine. Its AI learns within each defined fraud problem, carries that learning into future analyses, and can recalibrate as conditions change. It also explores velocity time windows automatically. Crucially, these core optimization capabilities do not require an external large language model.
Rule recommendationsRule maintenanceHistorical simulationHuman oversight
AI fraud rule building / Practical application
Build and optimize fraud detection rules.
Build and optimize fraud detection rules.
Without changing your fraud platform.
AI-based fraud rule generation means using analytical models to discover useful combinations of transaction signals and translate those findings into rules a fraud team can review. FRO is designed to extend that workflow beyond creating rules—to modification, retirement and historical what-if evaluation.
02 / RECOMMEND
Generate rule proposals
Translate selected patterns into proposed new fraud rules, adjustments to existing rules and possible rule retirements for analyst review.
03 / EVALUATE
Simulate before deployment
Compare selected recommendations with the current landscape using historical data, while maintaining existing rule governance and approval processes.
Standalone AI, not a replacement rule engine. FRO's core optimization capabilities do not require third-party LLM APIs, and the recommendations remain subject to customer review and validation.
Read the guide: How AI-based fraud rule building works
FRO / Essential facts
What is FRO, and when does it fit?
FRO (Fraud Rule Optimizer) by Aniratech is a standalone, purpose-built AI tool under development to support fraud rule discovery, generation, modification and retirement. It is designed for organizations that already have a rule engine and want to improve how rules are developed and maintained.
What does FRO produce?
Recommendations for new rules, modifications to existing rules, and potential rule retirements; analysts review the proposals before implementation.
Does it replace the fraud rule engine?
No. FRO is intended to complement existing decisioning systems. Approved rule changes are deployed through the customer's established workflows.
How are recommendations evaluated?
By comparing a fixed current landscape with a historical future-landscape simulation that reflects the analyst's selected proposals.
Does the AI require an LLM?
No external generative AI service is required for FRO's core fraud rule optimization. Neural learning and proprietary optimization are purpose-built for this task.
Availability and integration details should be confirmed directly with Aniratech. Historical simulations are not production results; performance gains are not guaranteed.
Inside FRO / Purpose-built AI architecture
Three layers.
Three layers.
One intelligent engine.
Purpose-built optimization
Purpose-built AI combines pattern discovery, adaptive neural learning, and proprietary rule intelligence. A continuous feedback loop helps the engine respond when measured fraud performance changes.
✳FRO / INTELLIGENCE ENGINE
CONCEPTUAL OVERVIEW01DISCOVER
LAYER ONE
Intelligent Pattern Discovery
Explores relationships across the available features, prioritizing promising combinations and filtering out less effective candidates.
02OPTIMIZE
LAYER TWO
Adaptive Performance Optimization
Refines selected patterns through iterative neural learning, with the ability to retrain when measured performance deteriorates.
03RECOMMEND
LAYER THREE
Proprietary Rule Intelligence
Converts optimized insights into recommendations for new rules, changes to existing rules, and potential rule retirements.
CONTINUOUS PERFORMANCE FEEDBACK
Built to adapt.
Not just optimize once.
After establishing a stable operating level, FRO can monitor updated performance results. If seasonality or evolving fraud attacks affect detection, its neural learning layer can automatically re-enter training and recalibration.
The objective is to recover the strongest achievable performance under current data, features, and business constraints—not to promise a fixed detection rate.
Feedback returns to Layer 02PERFORMANCE RESPONSE ADAPTIVE LOOP
Advanced on the inside. Practical on the outside. FRO is designed to help fraud teams evaluate recommendations and improve their rule strategies while retaining human oversight and their existing decisioning infrastructure.
Discuss FRO with our team
FRO / Independent AI Architecture
Intelligent by design.
Intelligent by design.
Independent by architecture.
FRO's core fraud optimization works through proprietary algorithms and neural learning—not through prompts or calls to external generative AI services.
Proprietary AI, built for fraud.
Pattern discovery, adaptive neural learning, and rule optimization are designed around structured fraud data and customer-defined objectives.
No external LLM required
Core rule generation and optimization do not depend on ChatGPT, Claude, Gemini, or other third-party LLM APIs.
Designed for your environment
FRO complements existing rule engines instead of requiring customers to replace their fraud decisioning infrastructure.
Predictable AI dependencies
No third-party LLM token fees for core optimization. Compute, integration, and operating requirements still depend on the deployment.
Purpose-built for the problem. FRO focuses on optimizing fraud rules rather than generating conversational answers. Like any predictive system, its recommendations still need testing and analyst review.
FRO / Problem-specific learning
Different fraud problems.
Different fraud problems.
Dedicated intelligence.
Fraud behaves differently across transaction types. FRO is designed to create a dedicated neural learning path for each defined fraud problem—rather than treating every problem as one generic model.
When analysts revisit the same problem with a new date range, FRO uses that problem’s latest trained state as its starting point, then refines it for the new analysis.
FRO CONTEXT-SPECIFIC INTELLIGENCE
CONCEPTUAL MODEL VIEW
EXAMPLE FRAUD CONTEXTS
A different learning path for each problem.
Select a context to explore how a later analysis builds on its previous learning.
Independent by design. Each selected fraud problem has its own learned model state.
SELECTED LEARNING PATH
Chip & PIN
Chip & PIN analysis continues from the most recently trained Chip & PIN model—not from a Mobile Wallet or Application Fraud model.
RESULT OF THE NEXT ANALYSISUpdated rule recommendationsNew rules · Rule modifications · Rule retirement candidates
Illustrative flow. The extent of recalibration depends on the data and performance conditions; improvement is not guaranteed on every run.
Continued learning, without a fresh start. A new date range can build on the prior trained state for the same fraud problem, keeping recommendations specific to the task at hand.
Explore FRO with our team
FRO / Intelligent velocity optimization
Stop guessing the window.
Stop guessing the window.
Start finding the signal.
Velocity rules are only as useful as the time periods behind them. Traditional analysis can mean repeatedly testing predefined windows—five minutes, ten minutes, fifteen minutes—until a workable interval emerges.
FRO removes the manual time-window search. It evaluates the time dimension automatically, seeking a finely calibrated window for each velocity signal against the chosen performance objectives.
PRECISION-TUNED VELOCITY WINDOWSCONCEPTUAL EXAMPLE / NOT A PRODUCT RESULT
01 / CONVENTIONAL WORKFLOW
Manually trialled.
The analyst defines a window, tests the result, adjusts it, and repeats.
Fixed, human-selected intervals
02 / WITH FRO
Intelligently calibrated.
FRO explores candidate time periods and identifies a suitable window for the defined velocity feature and objectives.
ILLUSTRATIVE SELECTED WINDOW AUTO
7 min
A meaningful interval need not be a round-number preset.
Data-calibrated rather than manually preset
Illustration only: 7 minutes is an example, not a fixed FRO setting or a guaranteed optimal window.Algorithms and selection methodology remain proprietary.
Fewer manual iterations
Reduce repeated trial-and-error when defining velocity periods.
Finer-grained calibration
Explore intervals beyond the familiar five- or ten-minute presets.
Analyst-directed objectives
Keep the focus on fraud outcomes and constraints—not manually tuning every window.
Complex optimization behind the scenes. A simpler workflow for analysts. Time-window selection is automated; recommendations still require validation against historical performance and business constraints.
Discuss velocity intelligenceA focused workflow
From data to decisions.
A structured approach to surfacing opportunities, evaluating trade-offs, and deciding which rule changes make sense.
01 / Configure
Define the analysis
Choose the relevant data, fraud labels, features, and performance criteria.
02 / Discover
Generate recommendations
Run optimization to surface proposed fraud rules and maintenance opportunities.
03 / Evaluate
Simulate the effect
See how selected proposals would have performed on historical portfolio data.
04 / Decide
Deploy on your terms
Review recommendations and choose what to implement in your existing environment.
Three ways to improveOptimization doesn't stop
Optimization doesn't stop
at new rules.
FRO is designed to help teams manage the full lifecycle of rule effectiveness.
Proposed new rules
Identify patterns that existing rules may be missing and produce new rule recommendations for analyst evaluation.
Proposed rule modifications
Compare existing rule outcomes with proposed additional or adjusted conditions to understand their historical effect.
Proposed rule retirement
Identify candidates for removal and assess the consequences before any rule is retired.
Current vs. future landscape
Contrast the historical baseline with a recalculated simulation based on selected proposals. The future landscape is a simulation—not a production forecast.
Designed around existing systems
Keep your rule engine. Improve how rules get created.
FRO is envisioned as a standalone optimization layer rather than a replacement fraud platform. Its outputs are intended to support the team that reviews, approves, and implements production rules.
An important distinction
FRO evaluates proposed changes using historical transaction outcomes. Results are decision support, not a guarantee of future detection performance, and any production deployment remains under the customer’s control.
FRO / Common questionsAI that fits
AI that fits
your workflow.
A few practical answers about how FRO is designed to operate.
Discuss your use caseWhat is an AI fraud rule builder?
An AI fraud rule builder applies machine learning and optimization to identify useful patterns in data and turn them into proposed decision rules. FRO is designed to generate, modify and retire fraud rules while leaving approval and implementation with the customer.
Can FRO build fraud detection rules without changing platforms?
FRO is designed as a standalone optimization tool. Analysts can review its recommendations and implement approved changes within their existing fraud decisioning systems, subject to the organization's integration requirements and governance processes.
Does FRO need ChatGPT, Claude, Gemini, or another LLM?
No. FRO's core fraud rule discovery, optimization, and recommendation capabilities do not require connections to third-party large language models. FRO uses purpose-built algorithms and neural learning for fraud analytics.
Does FRO replace our current fraud rule engine?
No. FRO is designed as a standalone optimization layer. It generates proposals for analysts to review and implement through their existing governance and fraud decisioning workflows.
Does using AI mean rule changes are deployed automatically?
No. FRO provides recommendations and historical what-if simulations. Your team remains responsible for reviewing, approving, and deploying production changes.
Does avoiding external LLMs eliminate all AI operating costs?
No. FRO avoids external LLM token usage for its core functions, but model training, computing, storage, and integration can still have operating costs. The requirements depend on the deployment.
Which fraud problems can FRO help analyze?
FRO's architecture is designed to maintain a dedicated learning state for each defined fraud problem. Examples include Chip & PIN, Mobile Wallet, and Application Fraud. Actual use depends on the customer's approved data, features, labels and performance objectives.
Can FRO recommend fraud rules from historical transaction data?
FRO is designed to analyze customer-provided historical data inside an agreed deployment environment, discover promising patterns, and generate rule proposals. Recommendations should be assessed using suitable unseen or later-period data before any production deployment.
Is FRO already a fully deployed enterprise platform?
FRO is under active product development. Capabilities, deployment options, evaluation scope and enterprise readiness should be confirmed with Aniratech before planning a proof of concept or commercial rollout.
Want to see what FRO could do?
Tell us about your fraud rule workflow and what you would like to improve.