What problem does it solve?
Corporate clients do not call about one card. Their finance and treasury teams ask where a payment is, why a file was rejected, what the cut off time is for a currency, how to add a user or reset a token, and what a fee on the analysis statement means. Many of these questions arrive at the same moments (month end, payroll, a failed payment run), and every hour of delay can hold up a supplier payment or a payroll.
Service teams for transaction banking are small and specialised, and much of their time goes to questions whose answer already exists in a product guide or a status screen. Consumer style chatbots do not help much here: they lack the entitlement model of a corporate portal, cannot see payment status, and invent fees or cut off times when their content is thin.
How does it work?
- Recognise the user and entitlements. The assistant runs inside the authenticated portal and only sees the accounts and functions the user is entitled to.
- Answer from approved content. Product guides, cut off tables, fee schedules and how to articles come from the bank's own knowledge base, retrieved with citations.
- Look things up. Through read only APIs it checks payment status, balances, file processing results and user administration status.
- Complete simple requests. Within an allow list (for example a token reset request or a statement copy) it performs the action or opens a service request with the details filled in.
- Hand over with context. Anything outside the allow list, or where the client asks for a person, goes to a service specialist with a summary, and the specialist's copilot drafts the reply from the same knowledge base.
- Audience
- Customer facing
- Autonomy
- Supervised agent
- Adoption
- Early adopters
- Channels
- Web chat, Mobile app, Agent desktop, Email, Phone and voice
What is it worth?
Benchmarks are computed from the public deployments below: one data point per organization per KPI, with who made each claim.
| KPI | Median | Reported range | Data points | Claimed by |
|---|---|---|---|---|
| Contact deflection | Too few to pool | 16% | 1 | 1 organization |
| Interactions handled | Not pooled | at least 120,000 | 1 | 1 organization |
| Satisfaction uplift | Too few to pool | at least 23% | 1 | 1 organization |
| Users served | Not pooled | about 4000 | 1 | 1 organization |
Value drivers: Lower cost to serve, Customer experience, Speed and cycle time, Employee productivity.
Indicative value
A transaction bank serving 5,000 corporate and SME clients through its online platform
USD 150,000 to USD 1.2 million
Specialist service cost avoided per year
How this is calculated
Formula: clients * requestsPerClient * containment * costPerRequest. The low scenario uses every low input, the high scenario every high input.
| Input | Low | High | Basis |
|---|---|---|---|
| Active corporate and SME clients clients, clients | 5,000 | 5,000 | The reference bank. |
| Servicing requests per client per year requestsPerClient, requests per client per year | 10 | 20 | Editorial assumption, replace with your own service desk volumes. |
| Share of requests the assistant resolves without a specialist containment, fraction of requests | 0.2 | 0.4 | Editorial assumption. No bank on this page publishes a resolution rate. The high value is capped at the more than 40% of CashPro Chat client interactions Bank of America says Erica handles, which is a handled share used here as an upper bound, not a resolution rate. |
| Cost of a specialist handled request costPerRequest, USD per request | 15 | 30 | Editorial assumption for a specialised transaction banking service desk. Replace with your own loaded cost. |
What it leaves out: Gross avoided service cost only. It leaves out the cost of the assistant and integrations, the time saved by the specialist copilot on the requests that are handed over, and the value to the client of faster answers at month end.
Who already uses it?
2 public deployments, strongest evidence first. Grades: A regulator or audit, B the organization itself, C vendor case study, D anonymous or estimate.
DBS Bank
Singapore · Banking · 2025
DBS rolled out a generative AI version of DBS Joy, its virtual assistant for corporate clients, inside the IDEAL digital banking platform. It answers servicing questions around the clock from the bank's own knowledge base, passes complex requests to a service specialist who has a generative AI copilot, and its answers are reviewed afterwards by experienced customer service agents working as DBS Joy evaluators. DBS reports customer satisfaction scores improved by over 23% over the trial period. A later update made DBS Joy agentic (see the separate DBS Joy and digibot record).
- Interactions handled: at least 120,000, unique chats since the start of trials
"Since early trials of the new features started in February, DBS Joy has managed over 120,000 unique chats and counting."
Claimed by: organization - Users served: about 4000, corporate clients per month
"About 4,000 corporate clients, the vast majority of which are small and medium enterprises, now use the service every month."
Claimed by: organization - Satisfaction uplift: at least 23%, users of the virtual agent, from the start of trials in February to November 2025
"In addition to quicker responses and shorter wait times, users of the virtual agent were also more satisfied with their experience, with customer satisfaction scores improving by over 23% in the same period."
Claimed by: organization
Bank of America
United States · Banking · 2023
Bank of America brought the AI behind its Erica assistant into CashPro Chat, the service assistant inside the CashPro platform that corporate and commercial clients use for payments, deposits, loans and trade. It finds transactions and account information, guides users through the platform and routes complex requests to specialised service teams. After the Erica integration, chat volume rose 41% on the 2023 weekly average while chats with a live agent fell 16%. In August 2025 the bank said 65% of CashPro clients use CashPro Chat and that Erica handles more than 40% of client interactions in it.
- Contact deflection: 16%, chats with a live agent since the Erica integration, while chat volume rose 41% compared to the 2023 weekly average
"Since launch, chat volume increased by 41% compared to the 2023 weekly average. Meanwhile, chats with a live agent decreased by 16%."
Claimed by: organization
How do you implement it?
A model agnostic playbook: what to prepare, the order to build in, and what goes wrong.
Data you need
- Current product guides, cut off tables and fee schedules with named owners
- Service request categories with volumes from the service desk
- Payment, file and user administration status reachable through APIs
Systems to integrate
- Corporate banking portal and app (authentication and entitlements)
- Payment hub and file processing status
- Service desk or CRM case management for handover
- Specialist desktop for the copilot
Complexity: Medium
Answering from product content is straightforward. The effort is in the portal's entitlement model, read access to payment and file status, and a clean handover into the service desk tool.
- 1
Rank requests by volume and risk
Take a quarter of service desk tickets, group them into intents, and start with the high volume informational ones (payment status, cut off times, how to) before any action.
- 2
Build on the portal's entitlements
Let the assistant call only the APIs the logged in user could use in the portal, and test that a user from one entity never sees another entity's data.
- 3
Own the content
Give every guide and fee table an owner and a review date, and make the assistant refuse when retrieval finds nothing rather than guess a fee or cut off time.
- 4
Equip the specialists
Put a copilot on the specialist desktop that drafts replies from the same content and shows the assistant's conversation summary, so handovers are fast.
- 5
Review conversations, not only dashboards
Have experienced service staff review a sample of conversations every week and feed corrections back into the content. DBS uses experienced customer service agents as DBS Joy evaluators, who assess the quality of responses after the chat and suggest improvements.
Guardrails
- Access limited to the user's portal entitlements, enforced on every API call
- Answers on fees, cut off times and terms only from approved content, with citations
- Read only by default; any action goes through an allow list with its own authentication level
- A visible way to reach a human at any point
- Full transcripts retained for disputes and complaints
KPIs to instrument
- Containment per intent, counting a repeat request within seven days as not contained
- Time to first answer and time to resolution against the specialist channel
- Client satisfaction on assistant conversations and on handed over cases
- Share of answers with a citation, and answers corrected by evaluators
- Monthly active client users of the assistant
Human in the loop
Service specialists handle every request outside the allow list, every complaint and any case where the client asks for a person. Experienced staff review a sample of assistant conversations each week and approve content changes before they go live.
Common failure modes
- Invented fees or cut off times
- The assistant answers from general knowledge when content is missing. Force refusal when retrieval is empty and test with questions outside the content.
- Entitlement leaks
- A user sees another entity's payments through the assistant. Enforce entitlements in the API layer, not in the prompt, and include cross entity tests.
- Handover that loses context
- The specialist starts again and the client repeats everything. Pass the summary, the identified entity and the steps already tried.
- Month end overload
- Volume spikes expose slow integrations and time outs. Load test at month end volumes before launch.
What are the risks and rules?
EU AI Act
Limited risk (transparency)
A chatbot that interacts with people at client companies must disclose that it is AI (Article 50). It does not evaluate creditworthiness or decide on access to an essential service (Annex III point 5), so it is not high risk.
Rules that apply
Guidance
- Article 50, transparency obligations for providers and deployers of certain AI systems (European Union, Europe). Users must be informed that they are interacting with an AI system unless this is obvious from the context.
- MAS Guidelines for Artificial Intelligence (AI) Risk Management (Monetary Authority of Singapore, Asia Pacific). Consultation paper issued on 13 November 2025 proposing supervisory expectations for all financial institutions on AI oversight, AI inventories and life cycle controls, covering generative AI and AI agents.
Controls to put in place
- AI disclosure in the assistant and a documented route to a human
- Inventory entry with an owner, the content sources and the action allow list
- Entitlement tests and regression tests on every release
- Transcript retention in line with the bank's record keeping rules
- Weekly quality review by experienced service staff
Frequently asked questions
- How is a corporate servicing assistant different from a retail chatbot?
- It works inside the corporate portal's entitlement model, answers treasury and payments questions rather than card questions, and hands over to specialised service teams. DBS runs DBS Joy inside its IDEAL platform, and said in November 2025 that about 4,000 corporate clients used it every month.
- How much of the chat volume can it take on?
- None of the banks cited on this page publishes a resolution rate for its corporate assistant. Bank of America says Erica handles more than 40% of client interactions in CashPro Chat, and that chats with a live agent fell 16% after the Erica integration while chat volume rose 41%. Results depend on the intent mix and on whether the assistant can see payment and file status.
- How do banks keep the answers accurate?
- By grounding answers in the bank's own knowledge base, filtering responses through rule based checks, and having experienced service staff assess responses and suggest improvements, as DBS describes for DBS Joy.
How to cite this page
Blits.ai AI Use Case Library, "AI assistant for corporate and commercial client servicing", last verified 27 September 2026, https://www.blits.ai/ai-use-cases/corporate-client-servicing-assistant. Licensed under CC BY 4.0. Method: how we verify use cases.
Changelog
- 27 September 2026: First published