Connect every transaction type. Uncover hidden risk.

A single, flexible data model for all your transaction monitoring needs

Our composable data model connects activity across all your products – from cards to crypto – revealing suspicious patterns that separate systems can’t.

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Why legacy models are creating gaps in your risk view

Modern financial services businesses are rarely single-product. Customers move money across cards, bank transfers, crypto wallets, and more, and this product mix will keep evolving.

But legacy systems weren’t built for this reality.
They force a compromise that leads to critical gaps in your risk view:

The lowest common denominator gap

This occurs when a single, generic data model is used for all transactions, stripping away the critical, unique signals for each transaction type.

The silo gap

This is created by using separate monitoring systems for each transaction type, making it impossible to detect patterns that cross between them and preventing the architecture from scaling with your business.

Connecting the dots with a composable schema

To close these gaps, we rejected the legacy approach entirely. We built our composable schema: a flexible, layered data model designed to provide a single, unified view of risk across every transaction type.

How it works: From a common core to custom fields

01.

The shared common layer

Core details common to all transactions – like amounts, account balances, and channel – are defined once in a universal layer inherited by every transaction type.

02.

Transaction-specific fields

On top of the core, each transaction type adds only the fields and context unique to it, such as crypto wallet addresses or card-specific data.

03.

Client-specific custom fields

Finally, you can extend the model with your own custom fields to meet unique business requirements, without impacting the core architecture.

Why this matters for your business

Clarity: See everything in one place

Our shared common layer unifies risk data across all transactions. This makes cross-transaction-type detection the default, not the exception, giving your team a complete and coherent view of customer activity.

Power: Detect more sophisticated risk

Go beyond traditional AML by analyzing non-monetary transaction data, like login anomalies and device changes. This allows you to uncover complex, hybrid threats that were previously invisible.

Agility: Scale at the speed of business

Because the architecture is designed to grow with you, adding a new transaction type is a simple configuration, not a complex development project. This means your compliance program can adapt as fast as your business does.

Ready to see the full picture?

Don’t let data silos dictate your risk view. Start monitoring how your customers actually behave across all your products, all in one place.

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Transaction Monitoring data model FAQ

A data model in transaction monitoring defines how the system ingests, structures, and interprets information for different transaction types. The comprehensiveness of the data model influences your ability to detect risk.

A composable data model is a flexible, layered data model that can be easily extended. Instead of a rigid, one-size-fits-all structure, it starts with a shared foundation and allows new transaction types or custom data points to be added as modular layers. This makes the architecture highly scalable and future-proof. For our customers, this means that adding a new transaction type is simpler and quicker.

Our platform is designed for immediate use, meaning that if the required fields for a transaction type are already provided in the shared common layer, they are ready to use. Because our data model is designed to grow with your business, we can rapidly configure new fields specific to any transaction type if needed. We have extended the shared common layer with fields specific to 10 transaction types ready to use today, and we are continuously adding more. Our current transaction types include: