From Basic to Advanced: AI’s Transformation of Compliance Processes

At a time when financial crime tactics are evolving sometimes faster than the technologies designed to detect and prevent them, large banks face a growing challenge: how to scale Anti-Money Laundering (AML) and Know Your Customer (KYC) processes to keep pace with the rise in the number of transaction alerts and increasingly complex risk scenarios. 

Non-cash payment volumes were 1.3 trillion transactions in 2023 and expected to grow 15% annually by 2027, according to estimates from Cap Gemini. Traditional rule-based systems, long the cornerstone of compliance at financial institutions, simply weren’t designed to handle the volume of financial transactions that take place today. 

This results in excessive false positives that consume countless analyst hours. But Agentic AI, when deployed properly, is delivering real, measurable impact across the AML and KYC value chain and helping compliance teams do more, with greater precision and less delay while meeting regulatory and audit expectations. 

The Challenge of Volume in AML Operations

Traditional rule-based systems currently flag 85-95% of alerts as false positives, thanks to the billions of transactions across diverse geographies and client profiles that financial institutions process each day. This can create massive backlogs, operational fatigue, increased risk and can cause delays for legitimate customers. 

How AI Responds

AI directly addresses the scale challenge by reducing false positives by as much as 60%. This can be achieved It does so through:

• Entity resolution to disambiguate customers and counterparties

• Dynamic risk modelling that adjusts to evolving typologies

• Real-time triage to prioritise alerts that require investigation

• Automated investigation, recommendations for reporting if needed to reach closure

By intelligently ranking and routing alerts within seconds, AI enables analysts to focus on genuine threats  – streamlining operations and improving both speed and accuracy.

‘When AI works hand in hand with your team  – assisting in AML and KYC processes end-to-end  – you are able to truly scale. This isn’t theoretical. Our AI is already deployed across some of the world’s largest financial institutions, operating in over 150 regulated markets. We’re doing the work today.’

Ben Rayner, Global Head of Sales at Silent Eight. 

The AML & KYC Value Stream: Where AI Adds Value

Agentic AI has the potential to transform the entire AML and KYC lifecycle – enhancing speed, accuracy, and risk coverage across key compliance functions. Here are some of the areas where it is making an impact today:

• Onboarding: AI-powered Optical Character Recognition (OCR) extracts data from scanned documents and Natural Language Processing (NLP) helps computers understand human language. Together, they automate document review, reducing manual effort and onboarding time. 

• Sanctions and PEP screening: Agentic AI platforms, like Silent Eight’s Iris 6, detect sanctions, PEP, and adverse media risks with speed, precision, and minimal manual intervention..

• Real-time triage and alert adjudication: Iris 6 intelligently routes and auto-adjudicates alerts according to human-defined risk policies, with explainable decisions and full auditability built in.

• Continuous monitoring: AI enables perpetual KYC by continuously monitoring customer and counterparty risk-trigger changes in near real time, automating alerts on material events like sanctions hits, ownership changes or adverse media, in full compliance with risk tolerance requirements. 

• Transaction monitoring: Agentic AI can learn evolving financial crime typologies using machine learning and adaptive analytics. 

• Automate investigations: By consuming large quantities of data, leading to richer investigations compared to human capacity, Agentic AI can complete automated investigations.

• Adverse media screening: AI can filter vast volumes of content and surface only relevant signals aligned with defined risk profiles, significantly reducing false positives while preserving detection precision.

Operational Impact & Scalability

Agentic AI has the potential to facilitate impactful scaling of compliance operations. As alert volumes surge due to regulatory expansion, geopolitical events, and customers’ growing appetite for digital transactions, Agentic AI can be deployed to absorb these volume spikes, processing millions of alerts with speed and consistency.

By automating repetitive tasks, like name screening and alert adjudication, AI reduces manual workload while improving response times and decision accuracy. Risk-aligned automation ensures alerts are triaged and resolved based on predefined thresholds, reducing variation across analyst teams and enabling more consistent outcomes.

Furthermore, fit-for-purpose Agentic AI like Silent Eight’s Iris 6 can work through more complex investigations, continuously learning from prior decisions to enhance both detection precision and adjudication quality over time.

This level of operational resilience is critical. During regulatory reviews or crisis events, AI ensures continuity, even when teams are distributed or overwhelmed. It enables faster escalation of high-risk cases and streamlines responses to law enforcement or internal audit requests.

Importantly, Silent Eight’s explainable AI supports full traceability, allowing firms to scale without compromising transparency or governance. This means compliance teams can scale, responding to regulatory expectations with agility while managing costs.

The Broader Industry Shift: Adoption Trends

The global financial sector is undergoing a fundamental transformation in how it approaches AML and KYC compliance. Traditional rule-based systems are being supplemented by data-driven models that enable more adaptive, risk-aligned decisioning.

According to BCG, 50% of large banks now use scorecard-based models that assign dynamic risk scores to customers and transactions. These models provide a feedback loop between detection and case resolution, allowing banks to continuously recalibrate risk thresholds and improve alert relevance.

Meanwhile, 44% of banks have adopted machine learning–based detection models, leveraging both supervised and unsupervised learning to identify complex financial crime patterns that static rules often miss. These models adapt over time, learning from historical alerts, new typologies, and emerging risk behaviours.

AI is no longer confined to refining detection thresholds. It is now being deployed to reshape end-to-end compliance workflows – enhancing customer risk ratings, improving alert triage, and reducing misclassification of high-risk customers.

Efficiency Gains & Strategic Value

AI delivers measurable efficiency improvements and strategic advantages across AML and KYC operations. A 2025 Nasdaq report estimates $25–$50 billion in annual savings for banks’ risk and compliance functions, while maintaining equal or improved effectiveness.

Adoption of AI also accelerates customer onboarding significantly. Juniper Research forecasts a 30% reduction in digital onboarding from over 11 to under 8 minutes per identity check by 2028, thanks to AI-driven document verification and workflow automation.

Beyond speed, AI enhances operational capacity. By automating low-risk, high-volume tasks (e.g. identity extraction, alert triage), compliance teams can focus on reviews that require their expertise  – resulting in fewer escalations, reduced rework, and more consistent outcomes.

Faster decision cycles also reduce client drop-off and lower regulatory risk exposure. These capabilities, paired with explainable AI governance, offer banks not just cost-cutting but competitive edge and scalability in compliance.

A Strategic Edge in Managing Compliance Processes More Efficiently

From reducing false positives to enabling perpetual KYC and scalable investigations, AI is helping banks modernise AML and KYC operations with greater speed, precision, and resilience. As adoption deepens, forward-looking institutions will gain not just efficiency, but a strategic edge in managing financial crime risk.