AI-supported integrated finance offers efficiencies to companies

This is where embedded finance, empowered by AI, stops being an add-on and becomes operational infrastructure: less lost payments, less manual activities, less perceived friction.

The whitepaper focuses on another structural inefficiency: data fragmentation. An estimated 80 per cent of the data collected by companies goes unused, signalling how much value remains 'trapped' in silos and legacy systems.

If process-integrated finance brings financial services to the point of contact with the customer, AI tries to make the next leap: transforming transactional metadata and behavioural signals into automated decisions: 'It is no longer about mere technological connectivity, but about intelligent orchestration'.

AI works like an invisible director. Instead of always routing payments in the same way, it evaluates variables that a human cannot handle as quickly: probability of success per channel, country and amount, actual costs, processing latency, impact on the Sca, risk profile. The result is a dynamic routing that selects in seconds the most suitable 'rail' between instant transfer (A2A), card, wallet, applying adaptive alternatives in case of failure.

For an ecommerce or subscription platform, this translates into a very concrete promise: more payments approved on the first attempt and more predictable cash flow.

The second front is the less visible, but more costly: reconciliation. Today, these activities can absorb up to 30-40% of the operational time of companies' finance functions, and advanced embedded finance pushes towards continuous reconciliation, automatically linking payments, commissions, revenue splits and accounting movements, with an estimated reduction in operational costs of between 25% and 35%.

For a non-financial company, it means freeing up resources, reducing manual errors and, above all, restoring control over processes that often depend on too many external actors.

Third chapter: risk and fraud. Anti-fraud models based on static rules can block up to 25% of legitimate transactions, i.e. 'false positives' that become lost conversions. AI-based adaptive systems, combining behavioural data and ecosystem signals, promise to reduce fraud and chargebacks by up to 50 per cent without hardening the user experience.

It is in sectoral use cases that we understand why the advantage shifts to non-financial companies that know how to look 'beyond their sector'. From utilities to insurance to automotive, embedded finance becomes an opportunity if the industrial company knows how to act as a director, without becoming a bank: it can generate recurring revenues and improve the customer experience, while leaving the financial partners in control of the risk.

The background is a growth that Fabrick puts at $100 billion by 2030 for embedded finance in Europe, with Cagr above 20%.

But the thrust is not only market-driven: the whitepaper recalls a European regulatory context that, between Psd3/Psr and Fida (Financial Data Access), tends to favour interoperability and secure data sharing, while the evolution of instant payments and tools such as verification of payee contribute to make the tracks on which to build embedded services more 'standardisable'.

The conclusion is that the centre of gravity is moving: finance remains fundamental, but increasingly it is 'switched on' where it is needed, in times of need, as 'strategic invisibility' that removes operational obstacles and leaves the customer with only the experience