AOne·The Enterprise Digital Experience Platform of AO Group
Detect fraud before funds leave the ecosystem.
AO Transaction Monitoring is a real-time fraud and financial-crime monitoring capability powered by Tazama Version 4. It combines transaction ingestion, rules, weighted typologies, instant decisioning, investigation workflows, analytics and AI-assisted optimisation with AO implementation and managed services. AO Transaction Monitoring gives financial institutions, payment switches, fintechs and payment providers a configurable way to evaluate transactions in real time. The platform uses current transaction data, historical participant behaviour, rules and weighted typologies to calculate risk and support immediate action. Depending on the operating model of the customer, a suspicious transaction may be blocked, held, flagged or passed into an investigation workflow. AO provides the implementation, integration, configuration, deployment, support and operating capability required to use Tazama within a production financial ecosystem. Tazama is open-source software. AO provides the specialist engineering and operational capability required to implement it effectively.
Instant and fast-payment systems leave little time for manual intervention before funds move.
Fraud controls are often spread across channels, systems and providers without a complete transaction view.
Rules that are difficult to update become less effective as fraud behaviour changes.
Poorly calibrated rules create unnecessary alerts and increase the workload of fraud and compliance teams.
Alerts without structured case management, evidence and escalation processes are difficult to resolve consistently.
Closed platforms and opaque commercial models can create vendor dependency and restrict control over fraud strategy.
Rules-based monitoring evaluates each transaction using current event data and historical participant behaviour, then combines the results into weighted typologies with configurable thresholds.
Connection Studio and data-enrichment services allow organisations to ingest, map and enrich transaction data from different payment formats and systems.
AI-assisted tooling supports rule recommendations, parameter calibration, threshold optimisation and alert prioritisation.
Case management, investigation workflows and analytics provide the operational capabilities needed after an alert has been raised.
Receive a transaction or payment event through the Transaction Monitoring Service API or an approved integration channel.
Transform the message into the required monitoring structure and enrich it with participant, account or reference data.
Evaluate the transaction against configured rules and behaviour conditions.
Combine multiple rule outcomes into weighted fraud or AML-assisted typologies.
A single rule may not be enough to identify suspicious behaviour. Typologies must be designed, validated and calibrated for the payment environment of each customer.
Apply the configured transaction-level decision.
Available actions depend on the payment architecture and integration scope of the customer.
Create and manage an alert or case where human review is required.
Use operational results, analyst decisions and analytics to improve rules, thresholds and typologies.
The core Tazama engine processes transactions through an event-driven monitoring pipeline. Rule processors evaluate current transaction attributes and historical information. Typology processors combine rule results into weighted risk scores, and the transaction decision layer determines whether the activity should proceed, be blocked or be referred for investigation.
Ingests transaction events into the monitoring platform.
Routes transaction events through the relevant processing pipeline.
Evaluate individual risk conditions using transaction and participant data.
Combines rule results into configurable weighted typologies.
Supports the interpretation and routing of monitoring results.
Aggregates typology outcomes and applies the configured transaction-level decision.
Supports configuration and administration of the monitoring environment.
Provides controlled platform access and service authentication.
The core platform processes transactions, applies fraud logic and produces real-time decisions.
The extension layer supports transaction onboarding, low-code configuration, investigation and operational management.
The analytics layer supports reporting, investigation analysis and data-science use cases.
Connection Studio, Rule Studio, typology scoring, AI assistance, case management and analytics extend the core engine so that monitoring can be onboarded, tuned and operated without rebuilding the platform.
Evaluate real-time payments before or during settlement.
Apply consistent fraud controls across participating institutions and channels.
Monitor account transfers, payment activity and participant behaviour.
Apply fraud controls across wallets, agents, transfers and cash-in or cash-out activity.
Add transaction monitoring without building a proprietary fraud platform from the ground up.
Monitor cross-border and domestic transfer patterns.
Monitor payment flows, merchants, customers and transaction behaviour.
Monitor disbursements, repayments, account behaviour and suspicious transaction patterns.
Apply monitoring controls across API-driven payment journeys.
Support ecosystem-level visibility where the legal and operating mandate permits.
Tazama provides deployment patterns for on-premise environments and major cloud platforms. The final topology depends on transaction volumes, availability requirements, security, sovereignty, integration and operational responsibility. Sandbox stacks published for demonstration and exploration are not production deployment patterns.
Deployment inside customer data centres where sovereignty or latency requires it.
Dedicated tenancy with customer-controlled network and security boundaries.
Deployment onto Microsoft Azure using supported production patterns.
Deployment onto Amazon Web Services using supported production patterns.
Deployment onto Google Cloud using supported production patterns.
Split deployment across on-premise and cloud components where integration demands it.
The customer operates the platform with AO engineering support.
AO deploys and operates the platform under an agreed service model.
Non-production environments for exploration, testing and calibration only.
Payments and financial crime
Real-time fraud monitoring has been difficult to access across many African payment ecosystems because of proprietary licensing models, integration complexity and scarce specialist operating capability.
AO Group and Tazama announced a strategic partnership in 2025 to expand access to real-time fraud monitoring across African payment ecosystems, combining the open-source platform with AO integration, configuration, deployment and support capability.
Typical engagement profiles include national payment switches, central-bank-sponsored payment infrastructure, mobile-money ecosystems, financial-services providers and regional instant-payment programmes.
Customer identities are withheld. Engagements are described at a level agreed for public reference.
AO Transaction Monitoring supports fraud detection, investigation and AML-related monitoring processes. It does not replace legal advice, sanctions-screening platforms, customer due diligence, regulatory reporting obligations or accountable compliance decision-making. Open-source software is not cost-free to implement or operate: customers must budget for infrastructure, integration, configuration, security, testing, support, maintenance and specialist operating capability. Customer fraud, compliance, legal and regulatory decision-making remains with the appropriately authorised customer teams unless a separately defined service is agreed.
A discovery session is a working conversation about scope, constraints and what a credible first release looks like.
