Financial institutions function in a world that is increasingly interconnected, with transactions, account activity, client activity, market activity, ubiquity of liquidity, and activity within their operations 24×7. Each transaction is a signal for an action to make the right financial decision (updating balances, responding to market trends in prices, reacting to account changes, increasing credit limits, upgrading the payment system). But the significance of those signals lies in how rapidly institutions can integrate those signals into sensemaking and act on them. Organizations require an intelligence architecture that guaranties continuous awareness of their operations as financial ecosystems become increasingly intricate.

Conventional financial systems generally track information on departmental workflows, databases, and isolated platforms. Customer data is collected in customer relationship systems, risk is calculated in risk engines, liquidity is stored in treasury systems, and payments are listed in payment systems. These systems are important, but they can be siloed, leading to gaps in decision making and delays in reporting.

For instance, a suspicious transaction might appear harmless by itself, but when combined with account behavior, device information, and historical transfers, it might indicate a pattern of fraud. Similarly, a delayed settlement can become a liquidity problem when viewed in the light of future payment obligations and changing funding conditions. And without interlinked visibility, these institutions would not know what is coming until the issue spreads.

A solution to this challenge is the Financial Nervous System. It is an architecture of networked intelligence which continuously senses, interprets, acts on, and learns from activity in financial institutions.

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