Two AI applications solving commercial onboarding today
Commercial client onboarding costs banks millions every year. Industry research puts the annual cost of KYC review, which includes KYB checks for business clients, at $175 million for many commercial banks, according to Fenergo. For the broader architecture argument on why commercial onboarding stays slow and how agentic AI changes it, see our guide to agentic onboarding in commercial banking. This piece covers two specific applications already delivering results: intelligent document ingestion and AI-augmented entitlements setup.
What manual KYB and document processing cost commercial banks
That $175 million figure is driven by review volume: Fenergo estimates commercial banks perform KYC reviews for roughly 83,800 medium-risk clients a year at an average cost of $2,089 per case. But direct costs are only part of the story.
Revenue delay. Every week a commercial client spends in onboarding limbo is a week they're not transacting or deepening their relationship with your bank.
Client experience problem. CFOs and treasurers compare your onboarding experience to the consumer apps they use daily. When your process requires them to submit the same information three times, they notice.
Compliance risk. Manual data entry creates errors, and errors in KYB documentation create regulatory exposure. Deloitte's 2024 Banking & Capital Markets Data and Analytics Market Survey found more than 90% of data users in banks report that the data they need is often unavailable or takes too long to retrieve, a gap that compounds directly into the onboarding delays commercial clients experience.
Use case 1: automating commercial document ingestion and KYB
Commercial documentation, including articles of incorporation, beneficial ownership charts, and financial statements, comes in too many formats for rigid templates to handle.
Generative AI changes the equation. Current models scan, categorize, and extract data from unstructured documents without needing a rigid template; the model understands the intent of a document and identifies relevant fields even from formats it hasn't seen before.
Instead of an analyst spending 45 minutes manually reviewing and entering data, the AI handles extraction in minutes. The analyst's role shifts to reviewing and validating output, a fraction of the original time. The compliance workflow accelerates too: automated extraction means fewer gaps, fewer requests back to the client, and fewer re-keying errors during audit.
Use case 2: AI-augmented entitlements in commercial account configuration
Document processing gets the most attention, but entitlements configuration is an equally expensive bottleneck. Once a client clears KYB, operations teams build out user access, approval matrices, payment limits, and role hierarchies, often for 15-20 individual users across multiple accounts.
This work is manual and high-stakes. Grant the wrong approval authority and you've created a security exposure. AI augmentation addresses this through agent enablement: an AI assistant pulls live client data, recommends entitlement structures based on the client's organizational profile, and flags errors before they're committed. A manufacturing company with 12 subsidiaries gets a different permission structure than a professional services firm with three partners, and the AI surfaces the right template while flagging decisions that need human judgment.
The architectural requirement: why unified data is the real fix
Both applications share a common dependency: clean, connected data. In most commercial banks, onboarding information lives in the KYB system, account data in the core banking platform, and entitlements in a separate back-office tool, none sharing context automatically.
This fragmentation is what keeps AI stuck in pilot. Document ingestion AI performs better with connected client context; entitlements AI performs better with real-time data on account structures. Without Nexus, the Semantic Layer providing a shared operational model underneath, both underperform regardless of how sophisticated the models are.
As Jouk Pleiter, CEO & Founder at Backbase, puts it: "Every bank can say they're 'AI-first,' but most are bolting models onto mainframe-era silos. Transformation isn't about algorithms, it is about an architecture that operationalizes insights at scale. Without that, AI stays in pilot."
The fix doesn't require rip and replace. Grand Central, the Connectivity Layer, sits above existing systems so that when a client submits information once during onboarding, it propagates automatically to KYB, core banking, digital channels, and the entitlements engine, without anyone re-keying it.
Onboarding is one of four use cases covered in our full report. For a complete framework spanning relationship manager productivity, fraud prevention, and payments automation, download Pragmatic AI strategies for commercial bank growth in 2026.
Frequently asked questions
How much can AI reduce commercial onboarding time and cost?
Industry estimates put AI-driven efficiency gains in commercial onboarding at up to 60%. KYC and KYB reviews alone cost large institutions roughly $175 million annually, primarily because the process remains manual and paper-heavy.
What does AI-powered commercial onboarding look like in practice?
Instead of an operations analyst manually re-keying data from emails and PDFs, AI agents parse incorporation documents and ownership charts, guide clients through entitlements based on their specific business profile, and propagate data across systems automatically.
Onboarding is one of four use cases covered in our full report. For a complete framework spanning relationship manager productivity, fraud prevention, and payments automation, download Pragmatic AI strategies for commercial bank growth in 2026.
