Real-time fraud detection software for banks, fintechs and VASPs — Scenario Manager + Link Miner graph analytics + Machine Learning to cut false positives.
Speed is critical in combating fraud and financial crime. KROTON Fraud Miner operates in real time, monitoring transactions and activities continuously so that suspicious behavior is flagged immediately. Unlike legacy systems that might generate alerts after hours or days, KROTON’s streaming analytics engine evaluates each event on the fly. This real-time capability means you can stop fraudulent transactions mid-stream or investigate potential money laundering patterns as they unfold, rather than after the damage is done. The platform is also highly scalable – it’s currently capable of processing on the order of tens of millions of transactions per day without missing a beat – ensuring that even high-volume institutions get instant fraud alerts with minimal latency.
Every organization faces unique fraud and compliance risks. KROTON Fraud Miner’s built-in Scenario Manager empowers your compliance team to define, customize, and manage detection rules to fit your specific needs. Through an intuitive interface, you can set up tailor-made AML scenarios and fraud rules – for example, flag transactions over certain thresholds, unusual customer behavior patterns, or high-risk geographies – and adjust these parameters at any time. This flexibility means your fraud detection strategy is not one-size-fits-all; instead, it evolves with changing regulations, new typologies, and your business model. By allowing quick scenario updates without any coding, the Scenario Manager ensures you can respond immediately to emerging threats or regulatory guidance. And while rules alone provide a first layer of defense, KROTON’s intelligent design augments them with advanced analytics (machine learning and link analysis) to catch what pure rule-based systems might miss, closing the gaps in traditional monitoring approaches.
Customize your fraud scenarios with full flexibility
Traditional transaction monitoring often looks at events in isolation, but sophisticated criminals exploit networks of accounts and connections to launder money or commit fraud. KROTON’s Link Miner module uses network analysis to detect these intricate webs of illicit activity. It can rapidly sift through vast data sets – analyzing billions of relationship data points in under an hour – to reveal hidden links between accounts, individuals, and transactions .
By connecting the dots across customers and across time, Link Miner uncovers patterns and associations that scenario-based rules alone would not detect. For instance, a series of smaller transactions below reporting thresholds might individually appear benign, but Link Miner can identify when those transactions are part of a coordinated “smurfing” network, highlighting the entire suspicious sub-network within minutes . Investigators and analysts are provided with interactive visual network graphs and alerts that clearly pinpoint key entities (accounts, companies, beneficiaries) at the center of fraudulent rings or money laundering schemes.
This broadened perspective helps your team see the bigger picture behind isolated alerts, making it far easier to identify collusion, mule account networks, and complex laundering tactics that would otherwise go unnoticed.
KROTON Fraud Miner utilizes advanced machine learning models to dramatically improve detection accuracy and reduce the noise from false alarms. By analyzing historical data and patterns of legitimate versus illicit behavior, the AI learns to distinguish normal customer activity from truly suspicious activity with far greater precision than manual rules. In practice, this means the system can eliminate a large portion of false positives – in fact, tests have shown up to a two-thirds reduction in false alerts thanks to the AI’s alert suppression capabilities .
At the same time, these models are adept at catching subtle anomalies and complex schemes that rules might miss, leading to an exceptionally high true positive rate (one benchmark showed over 98% of flagged transactions were confirmed as actual issues) . By augmenting your fraud detection with machine learning, KROTON ensures you aren’t blindsided by patterns that are invisible to rule-based systems alone , and your team isn’t overwhelmed by trivial or irrelevant alerts. The result is a smarter alert pipeline: fewer false positives, more actionable cases, and a more efficient use of your investigators’ time.
Financial criminals are constantly innovating new methods, so a static detection system can quickly become outdated. KROTON Fraud Miner addresses this challenge through active learning, an AI technique that continuously improves the system’s models using new data and expert feedback. The platform doesn’t just learn offline in a lab – it actively incorporates feedback from your analysts and the outcomes of investigations to refine its understanding of what fraudulent behavior looks like. This means if a new fraud pattern or money laundering scheme emerges, KROTON can adapt to detect it even before you explicitly write a new rule for it .
Active learning allows the system to routinely discover novel suspicious activities on its own by leveraging both AI and human expertise . For example, when your team flags a certain unusual transaction pattern as fraudulent, the system takes that input to better recognize similar patterns in the future. Over time, the models become increasingly accurate and adept at catching emerging threats with minimal human retraining. This continuous improvement loop ensures your fraud prevention stays one step ahead of criminals. In short, KROTON Fraud Miner’s active learning capability means it’s not only fighting the fraud of today, but also proactively learning to stop the fraud of tomorrow – all while reducing the need for constant manual tuning of rules.
Real-time fraud detection scores payments and account activity as events happen—card, ACH, wires, faster payments, P2P, and digital channels. KROTON supports streaming and API decisioning with latencies designed for production traffic, plus scheduled batches for back-book reviews. You apply one policy across channels, so high-risk events are blocked or challenged immediately while lower-risk items queue for investigation without delaying genuine customers.
Rules flag single accounts; networks expose the orchestrators. Link Miner maps relations across devices, phones, emails, merchants, beneficiaries, and shared counterparties, then highlights “strong connections” and unusual fund flows. Investigators see clusters and hubs that indicate mule networks, first-party fraud, or collusive merchants. This graph context complements scenarios and dramatically improves detection of coordinated fraud that evades standalone rules.
KROTON combines machine-learning scoring with targeted scenarios to re-rank or suppress low-value alerts. Thresholds are tuned via champion–challenger tests on historical data, and reason codes keep decisions explainable. The result: fewer interruptions for good customers, higher precision for analysts, and stable recall protected by guardrails and monitoring—so you cut noise without widening the fraud gap.
Yes. Scenario Manager lets you build flexible rules with filters (channel, merchant, device, geo), temporal windows, velocity checks, ratios, and cohort-specific thresholds. Run what-if simulations and A/B (shadow) tests against guardrail KPIs—precision/recall, approval impact, backlog—and promote only winners with full versioning and rollback. This shortens the iterate-and-learn cycle from months to days
Core transaction fields, customer/account attributes, device and session metadata, merchant/acquirer IDs, geolocation, and any behavioral signals you already collect. KROTON integrates via REST APIs or secure batch, runs alongside your current systems (no rip-and-replace), and pushes outcomes—scores, flags, explanations—into case management and downstream workflows for consistent actions across channels.
Fraud shifts constantly. Active Learning surfaces uncertain/novel cases for analyst labels, then updates models incrementally. Drift tests track feature and outcome changes by segment; when limits are breached, thresholds are re-allocated under governance. This human-in-the-loop loop helps you discover new typologies early and deploy tuned policies safely, with approvals, audit trails, and fast rollback if needed.
Copyright © 2025 H3M Analytics Inc.
ISO/IEC 27001:2022 & ISO/IEC 22301:2019 certified — Certificates CFE/25/55892 and CFE/25/41059; valid 12 Aug 2025–11 Aug 2028
We use cookies to ensure that we give you the best experience on our website to personalise content and adverts and to analyse our traffic using Google Analytics.