TL;DR: Choosing a fraud detection system is a decision about decisions: how fast a verdict arrives, how well you can explain it, and how much control your team keeps. This guide gives fintech teams in the UK, Singapore and Germany a plain scorecard for comparing vendors, tied to the rules each market is putting on payment fraud. A free PDF version of the scorecard is available below.
Why vendor selection is harder than it looks
Most fraud vendor demos look the same. A dashboard shows a risk score, a map lights up, and someone says "machine learning". The differences that matter only show up later: when a legitimate customer is blocked on payday, when a regulator asks why a transfer was released, or when your fraud team needs a new rule live by Friday and the vendor says it is a quarter-two roadmap item.
Fraud also sits at the intersection of three pressures. Regulators are moving liability onto payment firms. Real-time payment rails leave seconds, not hours, to decide. And privacy and automated-decision rules limit what you can do with customer data. A scorecard that ignores any one of these will mislead you.
This article walks through the market context first, then the eight areas we suggest scoring every vendor on. If you only want the tool, jump to the download at the end.
What is changing in the UK, Singapore and Germany
You do not need to be a lawyer to see the direction of travel. In all three markets, the cost of fraud is being pushed toward the institutions that can detect it. Always confirm the current position with your compliance team or counsel, because these rules keep evolving.
United Kingdom
Since 7 October 2024, the Payment Systems Regulator's mandatory reimbursement requirement has applied to authorised push payment (APP) fraud over Faster Payments and CHAPS. Reimbursement is capped at £85,000 per claim, and the cost is split between the sending and receiving firm (see Lewis Silkin on the PSR's confirmed maximum and Bird & Bird's overview). The practical effect: the receiving side now has a financial reason to spot mule accounts, not just the sending side. A vendor that only scores outbound payments covers half the problem.
Singapore
The Monetary Authority of Singapore's Shared Responsibility Framework took effect on 16 December 2024. It sets duties for financial institutions and telecommunications operators around phishing scams, alongside revised E-Payments User Protection Guidelines (see the Allen & Gledhill summary and the MAS annual report section on countering online scams). For vendor selection, that means evidence matters: you will want logs showing which controls fired, when, and what was done.
Germany and the euro area
The EU Instant Payments Regulation requires euro-area payment service providers to offer a Verification of Payee check (name-to-IBAN matching) for euro credit transfers, with the obligation applying from October 2025 (see the ECB's page on the regulation and Bird & Bird's guide). Wider EU payment-fraud rules under the PSD3 and Payment Services Regulation package have been under negotiation, so check their current status before you commit to a multi-year contract. GDPR also applies to profiling and automated decisions, which affects how a fraud model may be used and explained.
The eight-area vendor scorecard
For each area below, score the vendor from 0 (not offered) to 4 (proven in your environment). Weight the areas by what matters to your business, then compare totals only after the weights are agreed. Agreeing the weights first prevents the loudest demo from winning.
1. Decision speed and architecture
Ask where the decision is made, and how long it takes under load. For real-time rails, a decision path that depends on a batch job is not a real-time control. Ask for latency measured in your region, with your data volume, rather than a brochure number. Check what happens when the scoring service is slow or unavailable: does the payment fail open, fail closed, or follow a rule you choose?
2. Detection approach and tuning control
Vendors mix rules, supervised models, anomaly detection and network or graph analysis. None of these is automatically better. What you need to know is who can change them. Can your fraud analysts write and deploy a rule without a ticket to the vendor? Can they test it against historical data before it goes live? Can you see why a model flagged a transaction?
3. Fraud types covered
List the fraud types you actually face and ask for each one explicitly: card-not-present, account takeover, APP scams, mule accounts, synthetic identity, first-party fraud, and application fraud at onboarding. Many tools are strong on one or two. In the UK, make sure inbound mule detection is covered, given the shared-liability structure described above.
4. Data inputs and integration
Fraud models are only as good as their inputs. Device signals, behavioural biometrics, payment history, KYC data and consortium data can all help. Ask what the vendor needs from you, how it is sent (API, event stream, batch), and how long integration usually takes with your core banking or ledger. Integration effort is often the hidden cost, so treat it as a scoring area, not an afterthought. Our API development and integration work often starts exactly here.
5. Explainability and audit evidence
Every block, hold and release should leave a record your compliance team can read: inputs, rules or model factors, the decision, who reviewed it. Ask to see a real case file. If the vendor cannot show why a decision was made, you will struggle with customer complaints and regulator queries in all three markets.
6. Data protection, residency and AI governance
Ask where data is stored and processed, which sub-processors are involved, and how long data is retained. For Germany and the wider EU, ask how the vendor supports GDPR obligations on automated decision-making. Ask for current security attestations and verify them directly rather than relying on a logo on a slide. Our compliance and risk consulting practice can help you frame these questions.
7. Case management and analyst workflow
Detection only creates alerts. People close them. Review the analyst experience: queue prioritisation, linked-account views, notes, escalation, feedback into the model, and reporting. A tool that produces many false positives with a poor queue will burn out your team regardless of model quality.
8. Commercial model and exit
Understand what drives cost without relying on any price list: per transaction, per decision, per user, per module. Ask what happens at volume spikes. Then ask the exit questions that vendors rarely volunteer: can you export your rules, labels and case history, and what is the notice and data deletion process? Pricing varies by scope, so put each vendor's quote on the same assumptions before comparing.
How to run the evaluation
A short, disciplined process beats a long one.
- Write your fraud profile. List the fraud types, rails and volumes that matter, and the outcomes you care about (loss, false positives, time to decide).
- Agree weights across the eight areas before any demo.
- Shortlist three to five vendors, and send the same written questionnaire to each.
- Run a proof of value on your own data. Replay historical transactions, including confirmed fraud and known good traffic, and compare detection and false-positive behaviour on the same sample for every vendor.
- Interview references in your market and rail type.
- Score, then stress test the winner: what would make you leave in two years?
Our technology vendor evaluation service follows this pattern, and the same approach applies whether you buy, build, or combine both.
Buy, build or hybrid?
Buying gives speed and shared intelligence. Building gives control and fit. Many fintechs end up with a hybrid: a vendor for baseline scoring and consortium signals, plus in-house rules and orchestration for what is unique to their product. If you lean that way, look at the shape of your own data first. Our fintech work on fraud and risk analytics and reconciliation engines shows how these pieces connect to the rest of a payments stack.
Common mistakes
- Judging on demo scenarios instead of your own historical data.
- Scoring detection but ignoring analyst workflow and case management.
- Assuming one region's compliance posture covers the others.
- Forgetting inbound payments and mule detection.
- Signing without an export and exit clause.
- Treating a vendor's accuracy claim as a fact without seeing how it was measured.
Download the scorecard
The accompanying PDF turns the eight areas into a printable weighted scorecard, a vendor questionnaire and a proof-of-value checklist. Pricing and scope for any Syslabs engagement are set on a consultation.
If you would like help shortlisting vendors, designing a proof of value, or integrating a fraud engine with your payment stack, book a meeting with Syslabs.
