Best Fraud Detection Software for Banks (2026): Top 9 Compared

FICO Falcon Fraud Manager is a transaction fraud system sold to banks and card issuers. The product page describes end-to-end monitoring of credit, debit, prepaid and commercial cards, digital payments including real-time and peer-to-peer services such as Zelle and FedNow, account-to-account transfers and wire transfers. FICO states that its models are designed for specific portfolios and regions and are built from data supplied by more than 10,000 financial institutions.

The consortium is the centre of the product. FICO says tagged transaction data from its global consortium drives the development of its machine learning models, and that banks can deploy those models immediately rather than training their own from scratch. The scoring engine can also run FICO custom models or a bank's own models, which matters for institutions with internal model risk teams.

Around the models sit a rules engine, case management and customer communication. Business and fraud users can define, test and deploy rules. Analysts review alerts at customer, account and product level in a configurable web interface. An automated two-way customer communication service contacts customers through their preferred channel when a transaction needs confirmation.

No price is published. Falcon suits a bank or issuer with a large card book and a fraud team able to tune rules on top of consortium models. Community institutions may prefer platforms on this page that are sized for smaller teams.

SAS Fraud Decisioning is a cloud-native fraud detection and prevention product that runs on SAS Viya. SAS states that it is designed for banks, credit unions, payment providers and other financial institutions that need to detect, prevent and investigate fraud across transactions, customer onboarding, authentication and digital commerce.

The fraud types named on the product page are broad. They include payment fraud, payment scams, account takeover, application fraud, synthetic identity fraud, check fraud, ecommerce fraud and other digital fraud. SAS adds that the product can help identify money mule activity, funnel accounts and bust-out fraud, which are common concerns for retail banks dealing with authorised push payment scams and first-party credit losses.

The platform combines real-time detection, analytics, machine learning, data enrichment, orchestration and decisioning with model management and governance, alert triage, case management, dashboards and prebuilt fraud models. For a bank, the governance element matters because fraud models at regulated institutions are often subject to model risk review, and keeping model management on the same platform as detection simplifies that documentation.

SAS does not publish prices. The product suits a bank with data science capacity that wants to build and govern its own fraud models alongside prebuilt ones. Institutions without analytics staff may find managed scoring services or consortium-led products on this page simpler to operate.

Nasdaq Verafin sells fraud detection and management to banks and credit unions from a shared cloud. Its central argument is that payment fraud, especially authorised push payment scams, is now best detected by profiling the person receiving the money rather than only the bank's own customer. The vendor states that its consortium includes about 2,700 financial institutions and has profiled more than 800 million counterparties without sharing personally identifiable information.

The product pages list separate detection for wires, ACH, checks, real-time payments, cards, online banking activity and loans. Check fraud detection combines image, behavioural and counterparty analysis. Payments fraud detection adds a suspected money mule database updated from consortium feedback, a payee name mismatch check when the receiving account sits inside the network, and real-time interdiction that lets an investigator release or reject a payment from an alert or case.

Packaging is flexible. Nasdaq Verafin states that banks can adopt wire, ACH, check and faster payments modules individually, as part of its wider financial crime management platform, or as real-time risk scores delivered by API into another monitoring system. The same cloud includes case management and automation for currency transaction reports and suspicious activity reports.

No pricing page is published. Verafin suits a bank or credit union that lacks its own data science team and wants network intelligence on counterparties out of the box.

BioCatch sells behavioural intelligence for fraud and financial crime, and its homepage is written for banks. The vendor states that more than 350 retail banks use its products and reports, for 2025, $17.2 trillion in transactions assessed and 18 billion user sessions analysed a month. These are vendor-reported figures.

The product line maps closely to bank fraud problems. Account Opening Protection analyses applicant behaviour to separate genuine applicants from stolen or synthetic identities and mule activity. Account Takeover Protection monitors sessions after login to identify remote access tools, malware and impersonation, even when credentials and devices look legitimate. Mule Account Detection looks for mule behaviour across the account lifecycle. Scams360 targets cognitive signs of a scam in progress, such as hesitation, confusion and external pressure, when a genuine customer is being manipulated into a payment. BioCatch Trust assesses the receiving account before a payment completes, using a collaborative network that does not share personal data.

BioCatch states that its Connect platform collects more than 3,000 behavioural and device signals through one SDK and assesses risk in under 500 milliseconds. It positions these signals as working alongside a bank's existing controls, so the product is usually an addition to a transaction monitoring system rather than a replacement for one.

No pricing page is published. BioCatch suits a retail bank whose losses come mainly from scams and account takeover in digital banking.

Alloy sells an identity and fraud prevention platform to banks, credit unions and fintechs. Its fraud page states that the company supports organisations from global banks to local credit unions, and that 25% of the top 50 U.S. banks and 7 of the top 10 credit unions use it for fraud prevention. These are vendor-reported figures.

The product starts from identity. Alloy argues that fraud always begins with a real, stolen or fabricated identity, and its decisioning engine connects more than 270 third-party data solutions so a bank can evaluate applications, accounts and transactions against one profile. Fraud Signal is a machine learning model trained across a network of more than 900 financial institutions and fintechs, aimed at slow-burn and post-onboarding fraud.

Controls are configurable. Banks can hold or reject payments, freeze accounts and block logins, and step-up verification adapts to risk using third-party providers for multi-factor authentication and document checks through one SDK. Integration is through a RESTful API to account opening, loan origination and CRM systems. The Alloy Testing Suite lets teams validate policies before production, and versioned policies support gradual rollout.

No pricing page is published. Alloy suits a bank whose fraud losses concentrate around new accounts and digital onboarding, and that wants onboarding data to inform later fraud decisions.

DataVisor sells a fraud platform and a combined fraud and AML, or FRAML, platform to banks, credit unions, payment providers and digital businesses. Its site has separate industry pages for banks and credit unions. The platform page frames the bank offer around compliance mandates at scale and the credit union offer around member experience and protection against account takeover, payment fraud and identity theft. NASA Federal Credit Union is described as a customer that rebuilt onboarding, detection and investigation on the platform.

Detection combines several methods. DataVisor lists supervised machine learning, patented unsupervised machine learning, rules, a knowledge graph and real-time transaction monitoring. The unsupervised element is aimed at coordinated fraud that has no prior labels, such as new fraud rings. The vendor states that it scores transactions in under 100 milliseconds at more than 15,000 queries a second, and covers ACH, wire, card, loan, check and crypto transactions.

The FRAML platform adds AML transaction monitoring, case management with multi-hop link analysis, AI agents that triage and route alerts, and generation of SAR and CTR narratives with governance controls. DataVisor also describes a consortium of anonymised device, behaviour and entity signals shared across banks, fintechs and marketplaces, and pre-built connectors to KYC, KYB and sanctions screening providers.

No pricing page is published. DataVisor suits a bank or credit union that wants fraud and AML alerts in one workspace and is concerned about organised fraud that rules alone miss.

Unit21 sells a fraud and AML platform with a dedicated financial institutions page for banks, credit unions and sponsor banks. It describes consolidating fraud detection, AML monitoring, case management and automated filings in one configurable system with audit transparency. Patriot Bank, Third Coast Bank and Service Credit Union are named as customers in Unit21's financial institutions guide, with outcome figures reported by the vendor.

The real-time payment fraud product is the core of the fraud offer. Unit21 states that each transaction is evaluated against rules in under 150 milliseconds, fast enough for FedNow, RTP, Zelle and other instant payments, using behavioural signals, device intelligence, velocity patterns, consortium data and sanctions risk. ACH, wires, RTP, cards, P2P, account-to-account payments and checks run through one decision engine.

Control sits with analysts. A no-code rule engine lets fraud and compliance staff deploy and adjust thresholds without engineering tickets, and explainable machine learning shows why alerts fire. NACHA and X9 file ingestion brings ACH and check data into detection. Banks can connect by API or flat file ingestion. On the compliance side, the platform automates CTRs, SARs, STRs, 314(a) requests and watchlist screening, with pre-populated forms generated from case data.

No pricing page is published. Unit21 suits a bank or credit union that wants a modern, configurable fraud and AML system without a large implementation team.

Fiserv sells Fraud Risk Manager as an end-to-end fraud prevention environment for financial institutions. The product page states that it monitors customer accounts for deposit and on-us fraud, ATM and debit card fraud, kite fraud and payments fraud across channels and product lines. Detection uses profiling, predictive analytics, peer group analysis and rules.

Two features set it apart for deposit-focused banks. Kite fraud, in which a customer exploits float between accounts at different institutions, is named as a target, which matters for banks with check-heavy business customers. The product also monitors nonfinancial events, such as address changes, new debit card and PIN requests and changes in ownership, which often precede account takeover and first-party fraud.

Fraud Risk Manager is sold standalone or integrated with Fiserv AML Manager. In the integrated form, the two share alert handling, workflow, investigation, case management and reporting, which Fiserv says reduces total cost of ownership. For instant payments, Fiserv documents a separate product, FraudNet, an automated transaction monitoring engine that freezes processing of suspected fraudulent payments until a specialist reviews them, using shared rule sets and negative files from participating institutions.

Fiserv does not publish prices. The product suits a bank already using Fiserv systems, or one that wants fraud and AML from the same provider.

ACI Worldwide, a long-standing payments software provider, sells Fraud Management for Banking as an AI-powered financial crime management product. The product page addresses fraud, scams and anti-money laundering together, and frames the rise of real-time payments as the reason banks need faster detection.

Deployment flexibility is a stated feature. ACI says the product reduces fraud and management costs on-premises, in the cloud or in a hybrid environment, which suits banks with data residency rules or existing data centre commitments. Anomaly detection uses what ACI calls patented self-learning algorithms. On the compliance side, the vendor describes automated model refreshes, transaction monitoring and timely suspicious activity reporting for AML and know-your-customer obligations.

Two related offers are listed on the page. A fully managed fraud scoring service runs in the Microsoft Azure public cloud and uses ACI's patented incremental learning, which suits a bank that wants model outputs without running models itself. A strong customer authentication capability is described as delivering full SCA compliance with support for an exemptions strategy.

The page states headline figures, including detection rates above 85% and a 3:1 false positive rate, without a method. Buyers should treat them as vendor-reported. No price is published. ACI suits a bank that values deployment choice and a managed scoring option.