VoxquartzSystems
Free consultation Talk to us

AI for Fintech

In financial services AI works in four places: credit scoring on alternative data, real-time fraud detection, compliance, and personalising the offer. Fintechs and banks running machine-learning models report credit losses down 40% and applications processed 60% faster. Under PSD2 and AML rules there is a second side to it: a model that influences a credit decision has to be explainable to a regulator and to the customer. Choosing the model is a legal decision as much as a technical one.

Three uses of AI in Fintech

01

Credit scoring on alternative data

An ML model assesses creditworthiness from credit history, transaction data under PSD2, spending patterns, income stability and behavioural signals. It also serves thin-file applicants with no bureau record. Default prediction is 25% more accurate than conventional scoring.

Credit losses down 30%, decision time from days to seconds
02

Real-time fraud detection

An anomaly-detection system built on ensemble models scores every transaction in under 100 ms, weighing location, spending patterns, device and hundreds of other variables. It learns new fraud patterns as they appear, so nobody has to rewrite rules when the attack changes shape.

95% of fraud caught at a false-positive rate under 0.5%, €115,000+ a year saved
03

AML and KYC automation

The pipeline takes on identity verification (document OCR and face matching), sanctions and PEP screening, and transaction monitoring for suspicious patterns. It files suspicious-activity reports and escalates the cases a compliance officer must see - the compliance team's workload drops by 60%.

35 hours a week saved on compliance, onboarding time down 80%

Recommended stack

Python XGBoost Apache Spark Kafka Kubernetes PostgreSQL

Return on investment

35 h

Hours saved weekly

€35

Hourly rate

€58,500

Annual saving

The maths: 35 h/week × €35/h × 48 weeks = €58,500 a year

Figures are quoted in euro, converted from Polish złoty at a fixed rate of 4.30 PLN to 1 EUR and rounded. Contracts are settled in either currency.

What makes it hard

Supervisory expectations - scoring models must be explainable, and automated decisions fall under GDPR article 22

Bias in credit models - fairness analysis and the removal of indirect discrimination have to be routine

Financial data security - PCI DSS compliance, encryption and penetration testing

Fraud methods evolve fast, so models need continuous retraining and a team watching for drift

Frequently asked questions

Will an AI scoring model satisfy our supervisor?

Yes, when it is built for it. We use explainable AI (SHAP, LIME) to produce the reasoning behind each credit decision. Every automated decision carries an audit trail and a route to human review, as GDPR article 22 requires.

How fast does it catch fraud?

The system scores a transaction in under 100 ms - faster than payment authorisation takes. It catches 95% of fraud at a false-alarm rate of 0.3-0.5%, so legitimate transactions are not blocked. The model picks up new patterns on an hourly cycle.

What does an anti-fraud system cost?

For a mid-sized fintech doing 100,000+ transactions a month, €18,500-46,500 to build and €2,300-5,800 a month to run. The return is immediate - a single wave of fraud avoided often covers the year.

AI in Fintech

Tell us what gets done by hand at your company, and how often. Within 24 hours you get back where to start and how long it takes.

Free consultation

Related

AI integrations