Investment banks find themselves at a decisive turning point in 2026. The World Corporate and Investment Banking Report 2026 from Capgemini reveals that the industry’s growth curve is losing momentum, with compound annual growth falling from 6.5 percent to 5.4 percent over the next 5 years.1 Competitive pressure is intensifying as non-banking financial institutions scale at speed, and legacy issues hold some incumbent players back. Meanwhile, rising expectations are becoming harder to meet, with fewer than one in four corporate and financial clients believing their bank is delivering the real-time, personalized experiences they require.
The Customer Gap
Fewer than 1 in 4 corporate clients feel their bank effectively meets their need for real-time, personalized experiences.
These pressures are not driven solely by strategy or investment constraints, but by limitations within traditional operating models that cannot keep pace with the speed, complexity and regulatory intensity of today’s markets.
Nowhere is the gap more visible than in Know Your Customer (KYC) and due diligence. It represents the front door of every client relationship and touches every revenue-generating activity. Yet interviews conducted by Capgemini reveal that 75 percent of banking executives say KYC and due diligence processes remain highly manual and fragmented, making them the single most inefficient workflow across the investment banking value chain. Despite substantial investment, most banks still run KYC on fragmented systems, siloed teams and reactive workflows. The result is a continual gap between investment and outcomes.
Capgemini’s research also points to a way forward: Intelligent, Agentic AI-led operating models that combine automation, orchestration and human decision-making to navigate these structural constraints. Encouragingly, leading organizations already harnessing these capabilities within KYC are demonstrating what this shift looks like in practice.
In this article, we explore how organizations can re-imagine KYC and due diligence and, in doing so, turn one of banking’s most persistent operational challenges into a source of competitive advantage.
The Process Bottleneck
75% of banking executives admit KYC and due diligence processes remain highly manual and fragmented, creating the industry's most inefficient workflow.
Why KYC and Due Diligence Need Transformation
As Capgemini’s research shows, in 2026, there is a fundamental mismatch between traditional KYC operating models and today’s risk, regulatory and client expectations. The traditional KYC model was built around fixed checkpoints: Collect documents at onboarding, conduct periodic reviews on a set cycle and react to adverse findings when they surface.
For decades, this approach was considered sufficient, but the landscape has fundamentally shifted, with three converging forces re-shaping what KYC and due diligence require in practice.
The Shift from Periodic to Perpetual KYC
The result is a re-definition of what KYC and due diligence mean in practice. It signifies a shift from periodic reviews to event-driven, risk-triggered reassessment, from static document collection to dynamic, continuously refreshed data. From point-in-time onboarding checks to lifecycle risk management and from siloed jurisdictional processes to a unified, enterprise-wide client risk profile.
Leading organizations are already realizing the benefits of this evolution, with financial firms using perpetual KYC or pKYC frameworks cutting 70 to 90 percent of their periodic review workloads.3
So how exactly can enterprises build the kind of operational models that bring this future to life?
4 Pillars of Intelligent KYC and Due Diligence Operations
Recognizing that KYC and due diligence must change is one thing, but re-wiring the operations that deliver them is another. Most banks still run KYC, AML monitoring, fraud detection and sanctions screening on separate datasets, with each function maintaining its own view of the client. The result is duplicated effort, inconsistent risk assessments and an inability to connect signals across domains.
Promisingly, the technologies, tools and expertise required to do so are converging, enabling this fundamental shift in how operations are designed, connected and managed to be enacted. However, achieving this potential requires more than deploying solutions individually. It calls for an integrated approach built on four key pillars:
1. A Unified Client Data Foundation
of executives cite disjointed data across business lines as the primary barrier to creating value.
Intelligent KYC starts with a single, continuously enriched client risk profile that serves all compliance functions across the lifecycle, from initial onboarding through ongoing monitoring to trigger-event reviews. When external data flows into this profile continuously rather than being checked at scheduled intervals, the shift from static compliance to dynamic risk sensing begins, forming the foundation for scalable customer due diligence solutions.
Capgemini’s World Corporate and Investment Banking Report 2026 proves what an absence of such initiatives means, noting that without unified data foundations, pilots remain isolated and enterprise impact stays limited. The research finds that 71 percent of executives say disjointed data across business lines is the primary constraint to value creation.
2. AI and ML Embedded Across the KYC Lifecycle
Reduction in onboarding timelines
More cases processed monthly
AI ACROSS THE KYC LIFECYCLE
AI and ML are already proving their value in KYC operations specifically. The Capgemini report establishes that banks are automating KYC through AI-powered document processing, parallel task execution and reusable digital identity frameworks. These capabilities are already delivering tangible impact, compressing onboarding timelines by up to 40 percent, reducing repetitive data requests across jurisdictions and enabling firms to process up to three times as many cases each month. Multi-agent AI systems are orchestrating end-to-end KYC, integrating data collection, verification and exception management. These represent some of the most impactful AI use cases in KYC today.
Beyond processing efficiency, ML is changing how risk is assessed. Rather than depending solely on static risk categories to determine which clients require enhanced due diligence, ML models can now identify patterns in transactional behavior, ownership changes and external signals that indicate emerging risk, flagging clients for targeted review before the next scheduled cycle.
Early adopters of perpetual KYC are reporting false positives reduced by 20 to 40 percent, onboarding turnaround time reduced by 40 to 60 percent and case backlogs reduced by 50 to 70 percent.4
3. Scalable, Cloud-native Infrastructure
The shift from periodic to perpetual KYC also has considerable infrastructure implications. An event-driven model generates far more reviews than a calendar-driven one. However, many of those reviews, when the trigger turns out to be immaterial, should be resolvable with minimal or no human involvement. Cloud-native KYC platforms replace the manual handoffs that slow current operations. Integration with external data providers, including corporate registries, screening utilities and regulatory reporting systems, eliminates the friction that exists at every step of the existing process.
4. Re-designed Processes
The most critical aspect of this new approach is also the most frequently underestimated. Process transformation in KYC is not only about automating existing processes, but also re-designing them so that automation, human decision-making and data work together seamlessly rather than in sequence, supporting a more risk-based KYC approach across the lifecycle.
This entails moving from functional silos, where KYC, AML and fraud teams operate independently with their own systems, data and case queues, toward integrated case management where a single client view supports all compliance activities. It also entails replacing the periodic review cycle with event-driven, risk-triggered workflows.
Capgemini’s perpetual KYC research showcases this orchestration in action, finding that intelligent analytics, including Generative AI, allows analysts to spend 80 percent of their time interpreting risk rather than hunting for information. Analysts can focus their expertise where it matters most, supported by systems that handle the routine and surface the material.
Why Transformation Efforts Often Prove Inadequate
Going forward, the re-imagining of KYC and due diligence will see the convergence of historically separate disciplines, including onboarding, periodic review, enhanced due diligence, screening and monitoring, into a unified, intelligence-led capability. Each connection between previously siloed processes reduces duplication, improves detection quality and frees analyst capacity for higher-value work.
Building and sustaining intelligent KYC operations internally at the pace the market now demands is no easy feat. The combination of deep domain expertise, technology capability, operational breadth and continuous innovation required exceeds what internal teams can typically assemble, particularly when banks also need to maintain business-as-usual compliance throughout the transformation.
Why Transformation Fails
82% of banking executives report zero revenue gains from new products, while 51% see no cost reductions.
Capgemini’s research on innovation failure reinforces this point. 82 percent of banking executives report no revenue gains from new products, while 51 percent have seen no expected cost reductions. Transformation is not failing because banks lack ambition, but because layering new technology onto unreformed processes and disjointed data does not produce intelligent operations, just expensive ones.
The right partnerships can change the equation, however. They provide instant access to operational scale, specialist talent, proven accelerators and continuous improvement without requiring banks to build and maintain everything internally. With the right partner by their side, firms can build intelligent, Agentic AI led operating models that optimally combine automation, orchestration and human discernment required to succeed in this landscape.
The Way Ahead: From Compliance Burden to Competitive Capability
The banks set to lead in the next phase of KYC and due diligence will be those that embrace it as an operational capability to build, embedded in the operating core rather than bolted on.
This means connecting what has historically been disconnected: Data, disciplines, technology and talent. This harmonized approach will usher in a groundbreaking era of intelligent KYC and due diligence operations.
KYC is only the starting point. The same transformation logic now extends across client onboarding, transaction and trade reconciliation and other key areas that will define the next phase of intelligent banking operations, enabling broader AML compliance transformation and more efficient onboarding automation across banking functions.
Explore how intelligent, AI-led approaches are re-shaping financial crime compliance and KYC operations at scale.
About the Author
Garry Harrison
Senior Vice President,
Banking and Financial Services
Garry is a business leader and board advisor at WNS, with 25+ years of experience across technology, AI, FinTech and financial services. He advises organizations and investors on AI-led transformation, financial crime innovation, commercial strategy and business growth.
FAQs
1. Why should investment banks prioritize KYC and due diligence transformation now?
Rising compliance costs, fragmented workflows, evolving AML regulations, and increasing client expectations are exposing the limitations of traditional KYC operating models. Modernizing KYC and due diligence enables investment banks to improve onboarding speed, strengthen risk visibility, and build more scalable intelligent operations.
2. What are the benefits perpetual KYC (pKYC) and how does it differ from traditional KYC?
Perpetual KYC (pKYC) replaces periodic reviews with continuous, event-driven monitoring that dynamically updates customer risk profiles in real-time. Unlike traditional KYC models built around fixed review cycles, pKYC enables more proactive, risk-based customer due diligence and faster response to emerging risks.
3. How can AI and machine learning improve KYC efficiency?
AI and machine learning improve KYC efficiency by automating document processing, orchestrating onboarding workflows, identifying anomalies, enhancing risk scoring, and accelerating case resolution. These capabilities reduce manual effort, improve compliance responsiveness, and enable analysts to focus on higher-value risk interpretation.
4. What business outcomes can banks expect from intelligent KYC transformation?
Leading institutions modernizing KYC operations are already reporting:
- 40–60% faster onboarding
- 20–40% reduction in false positives
- 50–70% reduction in case backlogs
- Improved compliance accuracy and audit readiness
Intelligent KYC transformation also helps reduce duplicated effort, strengthen lifecycle risk visibility, and improve operational scalability.
5. How does KYC transformation support revenue growth in investment banking?
Streamlined onboarding and intelligent due diligence operations reduce client friction, accelerate account activation, and improve responsiveness across complex client relationships. This enables investment banks to shorten onboarding timelines, improve client experience, and support faster revenue realization.
6. How can banks reduce compliance costs through intelligent KYC operations?
Banks can reduce compliance costs by integrating unified client data foundations, AI-led workflow orchestration, and event-driven risk monitoring into KYC operations. Intelligent operating models reduce repetitive manual reviews, improve automation rates, and increase analyst productivity across compliance functions.
7. What are the most impactful AI use cases in KYC and AML operations?
Common AI use cases in KYC and AML include document verification, sanctions screening, onboarding automation, anomaly detection, risk scoring, adverse media monitoring, and continuous lifecycle monitoring. Multi-agent AI systems are increasingly orchestrating end-to-end KYC workflows across onboarding, verification and exception management.
References
-
World Corporate and Investment Banking Report | Capgemini
-
Anti-Money Laundering and Countering the Financing of Terrorism Overview | European Commission
-
Reimagining KYC: From Legacy Friction to the pKYC Triad | Capgemini
-
Reimagining KYC: From Legacy Models to Perpetual KYC | Capgemini