A fintech executive watches their latest customer acquisition campaign deliver thousands of signups, only for 70% to drop out before completing onboarding. A corporate client ready to move millions waits six weeks while documents shuffle between operations, compliance teams across three jurisdictions.
This happens daily across UK fintech. Client Lifecycle Management (CLM), encompassing onboarding, KYC, AML, and ongoing monitoring, has become a critical chokepoint in the revenue engine.
Industry data reveals the scale:
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- UK banks spend £2,613 per corporate KYC review, up 19% year-on-year
- Up to 63% of customers abandon digital onboarding if it takes too long
- Corporate onboarding averages 45–90 days from application to activation
Yet reductions of up to 70% are achievable by rethinking the architecture from the ground up.
Why current systems create bottlenecks
Most banks rely on established CLM vendors like Pega, Fenergo, KYC360, and Encompass. These platforms were designed to centralise documentation and enforce process consistency. They digitised manual workflows effectively when compliance was about record-keeping.
Today’s challenge is different. Fintechs compete on activation speed, not documentation quality. They need systems that make intelligent risk decisions in real-time, not platforms that route documents through approval queues.
Current architectures create four structural bottlenecks:
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By GlobalData- Sequential processing across borders, even when regulatory requirements are independent
- Manual validation of machine-readable data, despite the IDP being capable of instant extraction
- Compliance policies trapped in Excel files and institutional memory
- Risk discovered after sales teams have set client expectations
Legacy platforms were built as workflow engines: route this document, collect these fields, enforce this sequence. What fintechs need are decision engines: evaluate this risk, dynamically determine requirements, activate when rules permit, and escalate only genuine ambiguity. The required transformation demands rethinking CLM from the ground up.
Five architectural shifts that enable intelligence-led CLM
Shift one: Design onboarding as revenue infrastructure
Most CLM platforms measure success by audit readiness. This treats onboarding as a risk management function that happens to enable revenue, rather than a revenue function that manages risk.
One global bank inverted this relationship by asking: “What’s the minimum information required to activate each product safely?” The result was layered activation:
- Core accounts opened immediately upon baseline identity and sanctions checks
- Trade finance services are activated once commercial documentation is cleared
- Multi-jurisdiction structures are processed in parallel, with each region’s approval enabling incremental service access
Clients who previously waited months could begin transacting within days. Revenue capture occurred in the first week rather than the first quarter. Regulatory risk didn’t increase; it was managed more precisely.
Technical implementation requires:
- Progressive disclosure: Show clients only the information needed at each threshold
- Contextual guidance: Explain why data is required
- Smart defaults: Pre-populate from authoritative sources
When combined with deterministic policy execution, compliance becomes nearly invisible to users.
Shift two: Encode compliance policies as executable logic
A major bank managed compliance across 50+ markets using spreadsheets. Updates are required every month. Regional teams interpreted rules differently. Audit findings multiplied.
The problem was that policies existed only as documentation, not as code. When rules live in PDFs, every implementation is an interpretation.
Policy-as-code makes compliance deterministic. Instead of “High-risk industries require enhanced due diligence,” the system encodes specific logic that auto-executes. This enabled a three-tier architecture:
- 80% global core rules are deployed enterprise-wide
- 15% regional configuration for jurisdiction-specific variations
- 5% client-specific overrides with explicit approvals
All rules are exposed as APIs, enabling pre-onboarding tools to auto-generate jurisdiction-specific requirements. When new AML directives were issued, the bank deployed updated rules globally within days rather than months, ensuring identical interpretation across every jurisdiction.
Shift three: Automate document intelligence, reserve humans for judgment
Traditional CLM treats document review as a human task supported by technology. Intelligence-led CLM inverts this: technology performs extraction, validation, and risk assessment; humans intervene only when genuine judgment is required.
One institution delivered 70% reductions in manual work through:
- IDP and data aggregators auto-populate 80% of routine information from authoritative sources
- LLMs validate extracted data against compliance requirements and auto-clear low-risk applications
- Predictive routing assigns complex cases to the right specialist immediately
- Real-time dashboards give leadership visibility into risk velocity
The key principle: machines handle pattern recognition and rule application; humans focus on ambiguous risk assessment and relationship context. The integration with Shift Two is critical, as executable policies define the rules against which AI validates documentation.
Shift four: Architect a unified intelligence layer over distributed data
One bank discovered that three internal systems reported three different risk scores for the same client. In intelligence-led CLM, this is architecturally impossible.
The solution is an API-based data fabric treating all information as streams feeding a single analytical layer. Client data lives on a centralised platform; CLM consumes it via APIs, ensuring consistency.
This enables:
- Real-time data enrichment from internal and external sources
- Composite risk scoring that’s continuously calculated rather than periodically updated
- Parallel processing across jurisdictions
One implementation reduced corporate onboarding time by 70% while achieving zero false positives and complete audit traceability. Sequential processing adds no regulatory value when requirements are independent. Digital-native CLM should operate like modern software: distributed, parallel, coordinated through data.
Shift five: Measure business outcomes, not activity completion
Traditional metrics count activity: reviews completed, documents collected, and cases pending. Intelligence-led platforms measure outcomes:
- Time to activation: How quickly clients can transact
- Audit performance: Zero high-severity findings prove speed doesn’t compromise quality
- User adoption: 90% adoption in week one signals intuitive workflows
- Customer satisfaction: 96% satisfaction shows a better client experience
Outcome-based measurement creates accountability for the entire system architecture. If activation is slow, the problem might be manual review, fragmented data, or inflexible policies. Workflow metrics hide these root causes. Outcome metrics expose them.
The strategic imperative
Two forces make intelligence-led CLM essential:
Open banking adoption: UK adoption is projected to reach 60.5% by 2026, with payment volumes growing 69% year-on-year. Consented data access enables instant risk evaluation, but only if the CLM architecture can exploit it. Policy-as-code encodes logic for evaluating transaction data. Automated intelligence processes patterns at scale. Unified architecture combines open banking feeds with traditional verification.
Collapsing customer tolerance: When 63% abandon onboarding due to excessive effort, acquisition economics break down. The gap between consumer expectations set by Amazon, Netflix, and fintech and the reality of fintech is unsustainable.
The window for differentiation
Fintech competition is increasingly about infrastructure, not brand. Revolut’s European growth stems from faster account activation across multiple jurisdictions than traditional banks. Stripe’s dominance comes from API reliability, not cheaper fees.
CLM is becoming the same category of infrastructure advantage. The components exist: AI for document understanding, API-based data fabrics, policy-as-code platforms. The real question is which institutions will act first.
Day-zero activation should be the standard, not the aspiration. The transformation requires rethinking CLM from first principles: not as a workflow that routes documents, but as a decision system that evaluates risk. Not as a compliance gateway clients must pass through, but as a revenue infrastructure that enables business growth.
The institutions that make this shift will set the pace for the next decade. The question for everyone else is whether they’ll follow quickly enough to survive.
Anandhan Kannan is Lead Engineering Analyst and Regional Tech Lead (UK & Europe) at Standard Chartered Bank
