Fraudsters move faster than your data

Fraud operates in milliseconds. Detection, however, often operates in minutes - or worse, hours.

This speed mismatch is where fraudsters gain advantage, particularly across instant payments, e-commerce and mobile banking. Most organisations still struggle to ingest and analyse data quickly enough to identify anomalies before money moves.

Fraud teams frequently work with batch-based ingestion processes or legacy systems that were never designed for real-time behavioural analytics. As a result, alerts are generated downstream, after fraud has already taken place.

“Breaches force their way in. Fraud looks authorised. Speed decides whether you spot the difference”.

The industry has normalised this lag, but customers increasingly won’t. Modern cyber-enabled fraud exploits these windows of delay with precision: session takeovers, account hijacks, authorised push-payment scams and synthetic identity fraud all succeed because defenders see the pattern too late.

When we talk about speed, it has two dimensions:

1. Speed of ingestion

If an organisation can’t pull in data from multiple sources — payment flows, device telemetry, behavioural markers, identity checks — quickly, it simply cannot generate a timely fraud signal. Slow ingestion leads to blind spots, which lead directly to financial loss.

2. Speed of analysis

Even with rapid ingestion, data must be made usable. That’s the advantage of Search AI. Elastic’s platform ingests data at scale (streams, logs, transactions, events) and makes them instantly searchable. This allows risk-scoring, anomaly detection and entity correlation to happen at operational speed.

More powerful as one

When ingestion and analysis work together, fraud teams can reduce the time between signal and action dramatically. Analysts can surface patterns such as:

• A device associated with three prior mule accounts
• A behavioural anomaly linked to a session-hijack pattern
• A geographic mismatch between customer history and transaction location
• Clusters of small-value test transactions
• Synthetic identities reusing shared digital fingerprints

These signals often exist, but in siloed systems, they appear too late or not at all.

Beyond a technical problem

The speed gap is not just a technical problem; it’s a business problem. Slow detection increases losses, drives up operational cost, and reduces customer trust. Conversely, institutions that prioritise speed see measurable improvements: fewer escalations, faster investigations, cleaner alert queues and stronger regulatory outcomes.

Real-time ingestion is no longer an aspiration. It’s a prerequisite for staying ahead of an adversary that automates, coordinates and iterates faster than traditional systems can respond. Closing the speed gap is one of the most powerful steps a banking or payments provider can take to reduce fraud losses.