Financial crime risk is becoming harder to detect than ever before. Fraud and AML typologies are growing more complex, AI is lowering the barrier to entry for illicit activity, and many firms are still trying to bridge legacy systems, siloed data and rising regulatory expectations. The answer? Combining bureau & fraud data with transaction forensics to combat emerging risk.
How can Resistant AI and Experian help combat emerging risk?
By layering Experian’s bureau and fraud data over Resistant AI’s behavioural signals, firms can understand not just who is involved in a payment, but whether the activity itself looks legitimate, anomalous or linked to wider financial crime risk: improving decisioning accuracy without adding unnecessary noise. In an initial joint pilot with a UK-based PSP, true detection rates increased by 200%, false positives reduced by 80%, and overall alerts reduced by 50%.
If you want to understand why financial crime is becoming harder to detect (and more dangerous), the first place to look is authorized push payment (APP) fraud.
In Experian’s Fraud and Financial Crime Report, 47% of UK businesses reported being impacted by APP fraud, making it one of the most pressing fraud challenges facing the industry today.
What is APP fraud?
A scam where criminals manipulate victims into willingly transferring money to an account controlled by the fraudster, typically through social engineering and impersonation. By the time the fraud is visible, the money may already have moved, the customer may already have suffered harm, and the firm may already be facing difficult questions around reimbursement, liability, investigation and customer trust.
It is a highly visible example of a much broader financial crime problem: fast-moving money, incomplete counterparty visibility, short intervention windows, and high-stakes decisions that sit across fraud, AML, compliance, customer experience and cost.
Stronger controls can protect customers, but they can also create payment delays, false declines and repetitive checks. More rules can increase coverage, but they can also generate alert noise and operational overhead. More automation can accelerate decisioning, but without transparency and human oversight it can create new governance risks. That is where transaction forensics, counterparty intelligence and richer fraud data can change the operating model.
How transaction forensics and comprehensive data combine to create a better defense
First, there’s the problem of data. A sending institution may understand its own customer, but have limited visibility of the payee. A receiving institution may see incoming funds, but lack the full context of the payer. At Experian, bureau and fraud data can help fight APP fraud (and wider issues), but gaps remain.
Despite industry progress, we know that banks often know little about the payee – just a name, sort code, and account number, making accurate interdiction difficult, with a high proportion of referrals (believed to be potentially up to 95%) deemed false positives. These gaps are even wider without visibility into behavioral signals. The typologies are rarely neat. A single pattern of activity may involve multiple risk signals: mule behaviour, investment scams, romance scams, unusual inbound flows, suspicious outbound movement or other anomalies in payment behaviour.
That’s where the combined approach steps in, to fill that data and behavioural visibility gap. By combining:
- Bureau data: Not just who your customers are, but who the payees and sending parties are, even when they’re not your customers.
- Fraud data: Historical risk indicators and consortium links.
- Transaction forensics: Real-time behavioural insights.
Resistant AI has developed 80+ AI models and detectors that analyse entity and transaction patterns. When combined with banks’ internal data (such as product type, inbound and outbound flow, device location, IP address and account age) these models can support interdiction in real-time. Outputs are categorised, contextualised and prioritised, helping automate alert triage, accelerate investigatory work and resolve incidents faster with explainability.
Experian’s data then takes this capability further by adding broader counterparty context. This combination is the foundation of a wider fraud and AML proposition. It gives firms a smarter view of who they are dealing with, what behaviour they are seeing and whether that behaviour fits the broader risk picture:
- Map a party’s footprint across the broader financial services sector whether they are a customer or counterparty.
- Assess credit behaviours and aggregate transaction data.
- Detect links to fraud consortia.
- Identify financial hardship indicators.
Traditional rules-based monitoring can help, but it often struggles to keep pace with this complexity, creating too much noise and missing financial crime that does not fit a known pattern. And even when a suspicious transaction is flagged, investigators still need enough context to decide whether the activity is truly risky.
Leading by example: A UK-based PSP elevates fraud detection
There’s no better example than the first Experian x Resistant AI joint-pilot. The initial focus was APP fraud, but once the immediate case was addressed, the question became broader: how could the firm horizon scan for laundering risk, delayed reporting and unreported fraud?
This is where the combined approach created measurable impact.
The customer already had controls in place and was investing in technology and compliance. But by applying strong modelling and richer curated data, the partnership was able to expose risks that existing approaches were not catching. With just a handful of transactions flagged per day, the first joint pilot exceeded expectations:
200%
Increase in true detection rates
80%
Reduction in false positives
50%
Reduction in overall alerts
Firms need controls that can adapt as criminal behaviour changes. By combining Experian’s bureau and fraud intelligence with Resistant AI’s transaction forensics, financial institutions can enrich existing monitoring environments with a smarter augmentation layer.
We’re here to help you detect and prevent fraud with confidence.
Transaction Forensics brings together our data assets with Resistant AI’s advanced behavioural modelling, enhancing detection with unprecedented speed and accuracy.