Fraud detection software we provide

The alert your analyst can actually explain in one sentence

Fraud losses in the US rose by roughly a quarter last year, and most of that growth outpaces static rule sets long before anyone notices. We build fraud detection software with explainable risk scoring, using AI to catch fraud earlier while cutting the noise that buries real threats.

Fraud Detection Software for Banking_2880x1760

Our approach to fraud detection software development

Fraud detection sits at the intersection of customer experience and financial crime risk, so we treat detection accuracy and false-positive rates as equally important architecture decisions. Fraud losses in the US rose by roughly a quarter last year alone, and UK institutions recorded over three million cases in a single year, which is exactly why static rule sets can’t keep up on their own. Every system we build logs every score, decision, and override in a complete audit trail from day one.

Before writing code, we map your transaction flows, existing rules engines, and case management processes, so data quality issues and regulatory reporting requirements surface at the start, not mid-project.

Who We Serve

Community banks and credit unions

You need real fraud protection without the enterprise price tag or headcount a Tier 1 bank has. We build focused detection sized to your transaction volume and team, without the cost of consortium-scale infrastructure you won’t fully use.

Fraud and risk operations leaders at regional and national banks

You’re accountable for the false-positive rate your analysts drown in as much as the fraud that slips through. We build explainable scoring that cuts noise without cutting detection.

Compliance and financial crime officers

You need every decision defensible to an examiner, not just accurate. We build a complete audit trail into every score, alert, and override from day one.

Challenges we address

Fraud doesn’t happen at one point in the customer relationship; it happens at three distinct surfaces, each with its own detection problem. Most institutions overinvest in one and leave the others exposed.

Transaction-time fraud that outpaces static rules

Card, wire, ACH, and real-time payment fraud evolves faster than static rule sets can keep up with, while those same rigid rules flag too many legitimate transactions and bury analysts in noise. We build adaptive machine learning scoring that retrains on emerging patterns and cuts false positives without missing new fraud typologies.

Onboarding and identity fraud that slips past KYC

Synthetic identities and account takeover attempts are built specifically to pass a standard identity check, which means fraud that starts at onboarding often isn’t caught until much later, if at all. We build identity graph analysis and behavioral signals that catch a fabricated identity before an account is ever opened.

Application-time document fraud that transaction monitoring never sees

A doctored pay stub or falsified document can pass a clean identity check and only become visible as a loss much later, after funds have already moved. We build document fraud detection that catches this before an application is approved, not after the damage is done.

Regulatory and reporting pressure

Suspicious Activity Reports, AML obligations, and audit requirements demand a complete, defensible trail of every decision. We build reporting and case management in from the start.

How we build a system your analysts trust

STEP 1

We map your fraud surfaces and current detection gaps

Before any code gets written, we look at where fraud actually gets through today- transaction-time, onboarding, or application-time- and where your current tools leave the biggest gap.

STEP 2

We design scoring your analysts can act on, not just trust

We build explainable models that show the reasoning behind every flag, improving investigation speed alongside detection accuracy.

STEP 3

We build, validate, and keep you informed

Our engineers build the solution in tracked, reviewed cycles. At every stage, we validate detection accuracy and false-positive rates against real historical fraud data. You get regular progress updates, so nothing surfaces as a surprise.

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Real-time transaction monitoring

Scoring of card, wire, ACH, and real-time payments as they happen, with risk decisions returned in milliseconds.

Machine learning risk scoring

Adaptive models that learn from transaction history and evolving fraud typologies, rather than static rule thresholds alone.

Behavioral biometrics and device intelligence

Analysis of typing patterns, navigation behavior, and device fingerprinting to flag account takeover and bot activity.

Case management and investigation tools

Alert triage, investigator workflows, and a full audit trail for every decision and override.

AML and watchlist screening

Sanctions, PEP, and adverse media screening integrated into the same transaction flow.

Consortium and network intelligence

Shared fraud signals across institutions to catch mule accounts and known bad actors faster. The value compounds with scale: a pattern invisible in your own transaction history alone often becomes obvious the moment it's checked against a shared network.

Regulatory reporting

Automated preparation of Suspicious Activity Reports (SARs) and other filings required by regulators.

Reports and dashboards

Real-time fraud loss, false-positive rate, and analyst productivity metrics exportable to your BI tools.

Explainable risk decisions

A risk score without a reason attached slows down investigators and erodes trust in the system. We build models that surface the specific factors behind every flag, so an analyst can act on a decision in seconds instead of re-investigating it from scratch.

Identity and document fraud detection

Identity graph analysis and document verification that catch synthetic identities and falsified applications before an account opens or a loan funds, not after money has already moved.

Important Integrations for the fraud detection software

Case management, CRM, and support tools

Route flagged cases into the tools your analysts and support agents already use, with full context attached, so investigations move faster and customers get clear answers sooner.

Data warehouses and event streaming platforms

Integrate with data lakes, warehouses, and streaming platforms such as Kafka to feed historical data into model training and keep real-time pipelines running at production volume.

AML, sanctions, and watchlist screening

Connect to sanctions lists, PEP databases, and AML monitoring tools so fraud and compliance teams work from one view of customer risk, not two disconnected systems.

Payment gateways and core banking systems

Stream authorization requests, transfers, and card transactions directly into the fraud engine. The system returns decisions to approve, hold, or decline before the transaction completes, not hours later in a batch report.

KYC and identity verification services

Link document checks, biometric verification, and identity data providers to catch synthetic identities and account takeovers at onboarding and at every high-risk step after it.

Device fingerprinting and behavioral analytics

Add device, IP, geolocation, and session behavior signals to each risk score. This helps separate a legitimate customer on a new phone from a fraudster using stolen credentials.

SIEM and security monitoring

Share fraud signals with your security stack to spot coordinated attacks, bot activity, and credential stuffing that cross the line between fraud and cybersecurity.

External risk and consortium data

Enrich decisions with credit bureau data, email and phone reputation services, and shared fraud intelligence networks for signals your internal data cannot provide on its own.

Is custom fraud detection software the best option for your needs?

Answer a few simple questions to find out whether you should choose a custom solution or a pre-built fraud detection platform.

1 / 10

Do you process a high volume of transactions across multiple channels (cards, wire, ACH, real-time payments)?

Is your current system generating a false-positive rate that’s overwhelming your fraud analysts?

Does your team still spend significant time on manual case review or rule tuning?

Do you need the system to integrate with a specific core banking platform, card processor, or payment rail?

Have you experienced new fraud typologies (e.g., authorized push payment scams, synthetic identities) that your current rules don’t catch?

Do you operate under regulatory requirements such as AML, BSA, or SAR filing obligations?

Do you need AI-based capabilities such as behavioral biometrics, network analysis, or adaptive risk scoring?

Have you already tried an off-the-shelf fraud platform that didn’t cover your workflows?

Do you plan to keep scaling transaction volumes over the next 2–3 years?

Lower false-positive rates

Machine learning-based scoring adapts to real behavior and cuts noise. Institutions that move from static rules to model-assisted scoring commonly report alert volume reductions in the 30 to 50% range within the first six months, freeing analysts to focus on cases that actually matter.

Faster investigation times

Explainable scoring means an analyst can act on a flag in seconds instead of reconstructing the reasoning behind it manually.

Earlier fraud detection across all three surfaces

Coverage at transaction-time, onboarding, and application-time catches fraud that a single-surface tool would miss entirely.

Reduced regulatory risk

A complete, defensible audit trail reduces the cost and risk of a finding during a regulatory examination.

Stronger network-level detection

Consortium intelligence catches a pattern invisible in your own data alone, often before it becomes a repeated loss.

Lower false-positive rates

Map your fraud surfaces and design the solution

We analyze where fraud actually gets through today across transaction, onboarding, and application-time surfaces, then define the feature set, scoring model, and technology stack tailored to your transaction volume and regulatory environment.
Build and validate against real fraud data

Our engineers build the solution in short, reviewed cycles. We verify detection accuracy and false-positive rates against your real historical fraud and transaction data, not synthetic test cases.
Integrate your core systems

We connect the system to your core banking, card processing, payment rails, and identity verification systems, testing each integration against live data before go-live.
Launch, train your team, and stay available

We train fraud and compliance staff on the new workflows before rollout. After launch, we offer ongoing technical support and implement enhancements as fraud typologies and regulatory requirements evolve.

Costs of custom fraud detection software development

Based on Svitla’s experience, average costs for building custom fraud detection software range from $50,000 to $400,000, depending on solution complexity, the number of integrations, and the AI functionality involved.

Want to understand the cost of your fraud detection solution?

Calculate the cost

Check Our Client Success Cases

A closer look at real projects where explainable scoring, identity fraud detection, or regulatory reporting were part of the challenge, not just the pitch.

InvoiceASAP

Project Type Mobile Development

Industry Financial Services

Discover how we enhanced InvoiceASAP, a mobile invoicing network, by developing new features, increasing app accuracy, and improving reporting and visualization. With a dedicated development and QA team, we expanded capabilities, improved stability, and enhanced user experience.

Collaborative Banking Fintech Solution

Project Type Cloud Solutions, DevOps, Managed Operations Support, Mobile Development, Quality Assurance

Industry Financial Services

Discover how we significantly advanced a collaborative banking fintech solution, fostering integration between fintech and banks. Enhancements included improved web and mobile app performance, UX/UI redesign, and utilizing a microservices architecture and cloud services for scalability and optimal platform operation.

Payment Services

Project Type Quality Assurance

Industry Financial Services

Uncover the ways we streamlined the testing process for a global tech leader's payment terminal software, introducing an automated framework and BDD-style tests that accelerated testing, enhanced quality, and reduced costs, enabling more efficient testing under various conditions.

Our awards and partnerships

Fraud detection software consulting

Fraud and AML process needs analysis.

Audit of the existing fraud detection workflows and tools (if any).

Recommendations on optimal features, architecture, and tech stack.

A plan of integrations with your core banking, card, and payment systems.

Implementation cost and time estimates, expected ROI calculation.

Fraud detection software engineering

Fraud detection solution conceptualization and architecture design.

Custom development of the fraud detection system.

Integration with the necessary financial systems.

Quality assurance and model validation.

Continuous support, model retraining, and evolution (if required).

FAQ

How is explainable scoring different from just getting a risk number?

A risk number tells an analyst something is wrong; an explanation tells them what and why, which is the difference between a five-minute investigation and a thirty-minute one. We build models that surface the specific factors, not just the score.

Can the system catch fraud at onboarding, not just after an account is active?

Yes, if we build in identity graph analysis and document verification as part of the solution, so a synthetic identity or falsified application gets flagged before an account opens, not months later.

How do you avoid flooding analysts with false positives while still catching real fraud?

We build adaptive scoring that learns from real behavior and outcomes over time, and institutions moving from static rules commonly see alert volume drop 30 to 50% without missing the fraud that actually matters.

Do you build in the audit trail regulators actually expect, or is that a separate reporting layer?

It’s built into the architecture from day one, every score, decision, and override logged and traceable, so a regulatory exam is a quick lookup rather than a reconstruction project.

When does custom fraud detection software make more sense than an off-the-shelf platform?

Off-the-shelf platforms work well when your transaction volume and fraud patterns are fairly standard. Custom development pays off when your specific fraud surfaces, false-positive tolerance, or integration needs don’t fit a packaged platform’s assumptions.

Do you provide ongoing support after launch?

Yes, and fraud typologies shift constantly, so we offer post-launch support that covers monitoring, model retraining, and enhancement as new fraud patterns emerge.

Ready to stop fraud before it costs you?

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Fraud detection software we provide

The alert your analyst can actually explain in one sentence

Fraud losses in the US rose by roughly a quarter last year, and most of that growth outpaces static rule sets long before anyone notices. We build fraud detection software with explainable risk scoring, using AI to catch fraud earlier while cutting the noise that buries real threats.

Our approach to fraud detection software development

Who We Serve

Community banks and credit unions

Fraud and risk operations leaders at regional and national banks

Compliance and financial crime officers

Challenges we address

Transaction-time fraud that outpaces static rules

Onboarding and identity fraud that slips past KYC

Application-time document fraud that transaction monitoring never sees

Regulatory and reporting pressure

How we build a system your analysts trust

We map your fraud surfaces and current detection gaps

We design scoring your analysts can act on, not just trust

We build, validate, and keep you informed

Real-time transaction monitoring

Machine learning risk scoring

Behavioral biometrics and device intelligence

Case management and investigation tools

AML and watchlist screening

Consortium and network intelligence

Regulatory reporting

Reports and dashboards

Explainable risk decisions

Identity and document fraud detection

Important Integrations for the fraud detection software

Case management, CRM, and support tools

Data warehouses and event streaming platforms

AML, sanctions, and watchlist screening

Payment gateways and core banking systems

KYC and identity verification services

Device fingerprinting and behavioral analytics

SIEM and security monitoring

External risk and consortium data

Is custom fraud detection software the best option for your needs?

Almost done

Thank you!

Lower false-positive rates

Faster investigation times

Earlier fraud detection across all three surfaces

Reduced regulatory risk

Stronger network-level detection

Lower false-positive rates

Map your fraud surfaces and design the solution

Build and validate against real fraud data

Integrate your core systems

Launch, train your team, and stay available

Ready to stop fraud before it costs you?

Costs of custom fraud detection software development

Estimate the cost of your fraud detection solution

What’s the goal of your fraud detection solution?

Where are you now?

How many transactions do you process per month?

Which systems should the solution integrate with?

Which AI capabilities do you need?

Which compliance requirements apply to you?

What’s your expected timeline?

Do you have a budget range defined?

Almost done

Thank you!

Check Our Client Success Cases

InvoiceASAP

Collaborative Banking Fintech Solution

Payment Services

Our awards and partnerships

Fraud detection software consulting

Fraud detection software engineering

FAQ

How is explainable scoring different from just getting a risk number?

Can the system catch fraud at onboarding, not just after an account is active?

How do you avoid flooding analysts with false positives while still catching real fraud?

Do you build in the audit trail regulators actually expect, or is that a separate reporting layer?

When does custom fraud detection software make more sense than an off-the-shelf platform?

Do you provide ongoing support after launch?

Ready to stop fraud before it costs you?