Financial crime management is hitting a breaking point. Analyst teams are buried under escalating alert volumes, much of it noise that still requires manual review. Investigations take too long, case queues continue to grow, and the manual effort required for SAR filings leaves little margin for error with regulators.
At the same time, fraudsters are using AI to increase the speed and scale of attacks. These campaigns adapt faster than legacy tools can respond, multiplying both the number of alerts and the time required to investigate them. Risk teams can’t hire or train fast enough to keep pace, and existing systems were never built for this level of volume or sophistication.
AI is starting to rebalance that equation. By clearing backlogs, automating repetitive investigative steps, and prioritizing the alerts that matter, AI enables teams to handle more cases with fewer resources.
Let’s explore how AI is helping financial institutions scale their defenses and keep financial crime management sustainable.
How AI can help in financial crime management
AI in fraud and AML is not a single technology, but a set of tools that address different layers of the financial crime workflow. Machine learning, large language models (generative AI), and agentic AI each provide distinct capabilities. For chief risk and compliance officers, the challenge is determining where these technologies can be deployed to make the biggest impact on their risk operations.
Machine learning
Machine learning is strongest at real-time detection and pattern recognition at scale. It processes large volumes of transactional and behavioral data to surface risks that static rules miss, and it can identify emerging threats before teams have time to hard code new rules.
- Real-time risk scoring: Models evaluate device, behavioral, and transactional signals instantly, so risky activity can be flagged or blocked before losses occur. This reduces reliance on batch reviews and gives teams the ability to intervene in real time.
- Anomaly detection: Rather than depending only on static rules, ML surfaces deviations in customer behavior, payment flows, or merchant activity that point to emerging fraud or laundering schemes.
- Link analysis: ML connects accounts, devices, and identities to expose mule networks or coordinated attacks that might appear harmless when viewed in isolation.
- Continuous improvement: As confirmed fraud and false positives (feedback) are fed back into the system, models adapt automatically, reducing the need for constant rule tuning by analysts.
Large Language Models (LLMs) and Generative AI
Generative AI refers to systems that can create new content like text or images. Large language models (LLMs) are the subset focused on written language, and they are uniquely effective at turning unstructured, text-heavy data into usable insights. They speed up workflows where analysts would otherwise spend time reading, analyzing data, or writing reports.
- Alert and case summarization: Turn raw logs and dozens of data points into structured summaries that allow analysts to review more cases in less time.
- Investigations: Extract key facts, timelines, and entities from lengthy case files or customer histories, giving analysts a clear view of what matters without wading through raw data.
- Natural language rule generation: Translate policy requirements into executable rules without coding, speeding up deployment of new controls.
- Customer due diligence support: Speed up onboarding reviews by analyzing identity documents, financial statements, websites, and public information to help analysts verify individuals and understand a business’s true nature.
- SAR narrative generation: Draft regulator-ready narratives from structured case data, reducing the time required to file reports and lowering the risk of inconsistencies.
- Dispute evidence preparation: Compile relevant signals (transaction history, device fingerprints, session data, behavioral biometrics) into packages that can be used for chargebacks or escalations.
Agentic AI
Agentic AI is unique in that it can execute tasks within defined workflows, functioning like an additional member of the team. It follows established standard operating procedures to handle routine steps that slow analysts down, while ensuring that judgment calls and escalation remain with human reviewers.
- Automated fraud alerts and triage: Review incoming alerts, dismiss duplicates, and resolve low-risk cases, keeping queues manageable.
- AI-powered onboarding fraud detection: Automate repeatable compliance workflows such as periodic KYC reviews and risk profile refreshes, ensuring customers are re-evaluated on schedule without adding manual workload.
- Fraud ring/mule account detection: Aggregate and connect suspicious accounts, transactions, and devices flagged by models, so analysts don’t have to manually piece together networks.
- False positive resolution: Handle repetitive reviews such as fuzzy name matches in sanctions screening, allowing analysts to focus on the escalations that carry real risk.
- Onboarding orchestration: Coordinate identity verification, fraud screening, and KYC checks across systems to create a streamlined onboarding process without sacrificing compliance.
How financial institutions are using Sardine’s AI platform today
Sardine’s AI platform is trusted by hundreds of banks, fintechs, and payment companies, including some of the largest institutions in financial services. FIS, for example, uses Sardine to power the risk engine in its Money Movement Hub, which supports hundreds of banks processing real-time payments with built-in fraud and compliance controls.
These enterprise tools for managing AI-driven compliance workflows help institutions tackle the jobs that create the most strain on fraud and compliance teams, from clearing onboarding backlogs to resolving sanctions alerts, running open-source checks, and reducing fraud and chargebacks. Here are a few examples of how organizations are using Sardine’s AI to take on that work and the results they are seeing.
Automating onboarding reviews
One card issuer was facing long onboarding delays caused by mismatched data and duplicate records. Backlogs stretched into double-digit hours, and customers often waited weeks for resolution. The issuer deployed Sardine’s AI-powered onboarding fraud detection to resolve the issue, with an agent automatically clearing matches and flagging only edge cases for human review.
- Backlogs dropped from 14 hours to 41 minutes
- Customer wait times fell from 20 days to just 2 minutes
- AI achieved 95% precision on decisions
- 49% faster time-to-revenue
Accelerating alert resolution
A large digital asset exchange was overwhelmed by sanctions and PEP alerts generated during onboarding. Many of the hits were common names like “Mohammed Khan” or “John Smith,” which created false positives that still had to be manually reviewed. Each review took 5-20 minutes, and the backlog quickly outpaced the team’s capacity.
Automated fraud alerts were deployed by Sardine’s AI agents during the exchange to handle the obvious false matches, compiling supporting evidence and escalating only the alerts that required human judgment.
- Cut review times from minutes to seconds
- Resolved 55% of alerts in about 30 seconds
- Doubled review capacity
Reducing card-related fraud
A commercial neobank was facing unsustainable card fraud losses and dispute volumes. Chargebacks were costing nearly $100,000 a month, and analysts were buried under 116,000 disputes. Reviews were mostly reactive, with the team chasing disputes after the fact instead of stopping fraud at the source. With Sardine’s ML models scoring transactions in real time, the bank was able to:
- Lower chargeback losses by 70%
- Achieve 0.0003% chargeback rate
- Reduce disputes by 91%
Use Sardine AI for better financial crime management
Financial crime is growing in scale and sophistication, but AI provides a path forward. From analytical detection to agentic decision-making, AI equips financial institutions with the speed, scale, and adaptability required to outpace criminals.
Sardine is purpose-built for real-time financial crime management, with data science at the core of everything we do. Effective AI for financial crime requires bringing together machine learning, large language models, and agentic AI in a single system that unifies fraud and AML operations. This design gives institutions stronger detection, faster investigations, and lower operating costs, while also improving the customer experience by reducing unnecessary friction.
If you’d like to see how Sardine’s AI for financial crime platform can improve your detection, investigations, and compliance operations, contact us for a demo.
FAQs
What is AI for financial crime?
AI for financial crime is the combined application of machine learning, large language models, and agentic AI to detect and respond to fraud, money laundering, sanctions evasion, and identity-related attacks. Each model class catches a different attack pattern.
How is machine learning different from generative AI for financial crime?
Traditional ML scores known patterns: fraud rules, AML scenarios, KYC risk. Generative AI handles unstructured signals: chat transcripts, voice, video, document analysis, SAR narrative drafting. Both run alongside each other in a modern fraud stack.
What is agentic AI in a financial crime context?
An agentic AI takes multi-step actions inside guardrails: triaging alert queues, drafting SAR narratives, routing manual reviews, escalating high-confidence cases. The human analyst signs off on decisions that touch the customer or trigger a regulatory filing.
How does AI for financial crime align with SR 11-7 and NIST AI RMF?
Every model in the stack ships with the SR 11-7 model risk documentation (development methodology, validation pack, monitoring plan, bias testing) and NIST AI RMF lineage (govern, map, measure, manage). Auditors get a single source for every decision factor.
How does Sardine deploy AI for financial crime without disrupting production?
Shadow mode. Sardine runs the new model alongside the existing rule engine for a configurable window, surfaces the delta in a dashboard, and only switches to production when the fraud lead and the model risk officer sign off.