What problem does it solve?
A corporate credit memo is a long document built from many sources: audited accounts, management accounts, projections, industry research, bureau data, internal exposure and conduct records, and the bank's own credit policy. Relationship managers sift through annual reports, industry research and internal records to build each memo; DBS says preparing credit memos and related credit activities can take up to 40% of a relationship manager's time.
That is time bankers do not spend with clients. An agent can do the assembly, spreading and first draft, and a shared template with deterministic calculations also keeps memos consistent between authors. But credit is a regulated decision: the value only holds if every number is traceable and people still own the judgement and the approval.
- DBS says preparing credit memos and related credit activities can account for up to 40% of a relationship manager's time.DBS scales agentic AI to transform way of working for corporate bankers (2026)
How does it work?
- Collect the file. The agent gathers financial statements, projections and supporting documents from the client portal, email and document store, and lists what is missing.
- Extract and spread. Document AI extracts line items and maps them into the bank's spreading template, flagging items that need an analyst's judgement.
- Analyse. It calculates ratios, trends and covenant headroom, and pulls bureau data, news, internal exposure and conduct records through approved connectors.
- Check against policy. Retrieval over the credit policy and sector guidelines highlights exceptions and required approvals.
- Draft the memo. It writes each section of the memo in the bank's format, with a link from every figure to its source page and a list of risk flags and open questions.
- Iterate and sign. The relationship manager and credit risk manager challenge the draft, ask the agent for deeper research, edit, and take it through the normal approval.
- Audience
- Employee facing
- Autonomy
- Copilot
- Adoption
- Early adopters
- Channels
- Internal tools
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 |
|---|---|---|---|---|
| Users served | Not pooled | about 1500 | 1 | 1 organization |
Value drivers: Employee productivity, Speed and cycle time, Risk and loss reduction, Compliance quality.
Indicative value
A corporate bank preparing 2,000 credit memos a year for new facilities and annual reviews
USD 540,000 to USD 3.4 million
Banker and analyst time released, valued at loaded cost per year
How this is calculated
Formula: memos * hoursPerMemo * timeSaved * hourlyCost. The low scenario uses every low input, the high scenario every high input.
| Input | Low | High | Basis |
|---|---|---|---|
| Credit memos per year memos, memos per year | 2,000 | 2,000 | The reference bank. |
| Hours of relationship manager and analyst work per memo hoursPerMemo, hours per memo | 20 | 40 | Editorial assumption, replace with your own time study. |
| Share of that time saved timeSaved, fraction of time | 0.15 | 0.3 | Editorial assumption. The upper bound equals the at least 30% goal DBS has set, which is a target and not yet a measured result. Source |
| Loaded cost of a banker or analyst hour hourlyCost, USD per hour | 90 | 140 | Editorial assumption, replace with your own loaded cost. |
What it leaves out: Values released time only. It leaves out the cost of the agent, data licences and model risk validation, and any effect on credit quality, faster time to yes for clients or revenue from the time bankers win back.
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 · 2026
DBS rolled out an agentic AI solution in which specialised agents handle more than 70 tasks to turn raw data (annual reports, industry research, internal records) into a review ready first draft of a credit memo for large and mid sized corporate clients. Relationship managers and credit risk managers iterate with the agents to reach the final memo. After a pilot with 150 users it reached about 1,500 employees globally in August 2026. DBS states a goal of cutting the time spent by at least 30%; that is a target, not a measured result.
- Users served: about 1500, employees globally, August 2026 rollout
"After an initial pilot phase involving 150 participants, the capability has been rolled out to approximately 1,500 employees globally."
Claimed by: organization
Banestes
Brazil · Banking · 2025
Banestes, a Brazilian bank, used Gemini in Google Workspace to accelerate credit analysis by simplifying balance sheet reviews. Banestes CTO Vicente Lopes Duarte said the generative AI tool has made it easier to read balance sheet documents, helping credit analysis teams work faster. No outcome figures for credit analysis are published; the story's only figure, a 70% ticket reduction, is about AppSheet, a separate Workspace tool, not the credit analysis use.
No outcome disclosed.
How do you implement it?
A model agnostic playbook: what to prepare, the order to build in, and what goes wrong.
Data you need
- The bank's spreading template, memo template and credit policy in machine readable form
- Historical memos and spreads to test against
- Access to bureau, rating and news sources licensed for this use
- Internal exposure, limit and conduct data per client group
Systems to integrate
- Loan origination or credit workflow system
- Document management and client portal
- Bureau, rating agency and news data providers
- Core lending and limits systems
- CRM for client context
Complexity: High
Many systems, a regulated decision and model risk validation. Spreading accuracy on messy financial statements, integration with the loan origination system and the bank's approval workflow, and a clear audit trail are the hard parts.
- 1
Start with annual reviews
Annual reviews of existing clients have prior memos and spreads to compare with, which makes accuracy measurable and the change less risky than new to bank credit.
- 2
Decompose the memo into tasks
Break the memo into its tasks (spreading, ratio analysis, industry section, peer comparison, policy exceptions) and automate them one by one; DBS says its agents handle more than 70 tasks.
- 3
Make traceability non negotiable
Link every figure to a source page and every statement to a document or data source, and block the draft from moving on while any figure lacks a source.
- 4
Validate like a model
Run the agent on past files, compare spreads and ratios with the approved versions, and take the results through model risk validation before live use.
- 5
Pilot with a small group, then scale
Pilot with experienced relationship and credit managers, capture their corrections, and widen the rollout only when error rates are stable; DBS went from 150 pilot users to about 1,500.
Guardrails
- The agent drafts; credit decisions and approvals follow the bank's existing authority matrix
- Every figure and statement in the memo links to its source
- Numbers are calculated by deterministic code, not generated by the language model
- Retrieved documents and news are treated as data, never as instructions
- Version history of each draft, with the human edits, is retained with the credit file
KPIs to instrument
- Elapsed time from complete file to memo ready for approval
- Analyst and banker hours per memo, from time studies
- Spreading accuracy against approved spreads on a sample
- Share of memo figures edited by humans, by section
- Credit committee questions or returns per memo, before and after
Human in the loop
Relationship managers and credit risk managers review, challenge and complete every draft, and the approval follows the normal credit authority. Model validation reviews the agent before use and periodically after, and credit risk samples memos to check that the analysis did not become thinner.
Common failure modes
- Fluent but wrong numbers
- A misread statement or unit error flows into ratios and the narrative. Use deterministic calculation, reconciliation checks and source links on every figure.
- Automation bias
- Reviewers accept the draft instead of analysing the credit. Track edit rates and committee challenges, and keep sections that need judgement explicitly blank for the banker.
- Stale or unlicensed data
- News or ratings are outdated or not licensed for AI use. Record the as of date and licence of every external source.
- Scope creep into individual lending
- The same agent is reused for sole traders or personal guarantors, which changes the regulatory tier. Assess each new borrower segment before use.
What are the risks and rules?
EU AI Act
Depends on design
Annex III point 5(b) makes AI used to evaluate the creditworthiness of natural persons high risk. Credit analysis of companies is outside that point, but the tier can change when the same system evaluates the creditworthiness of natural persons, such as sole traders, partners who are personally liable or personal guarantors. Design the scope explicitly and document it.
Guidance
- Annex III, high risk AI systems referred to in Article 6(2) (European Union, Europe). Point 5(b) covers creditworthiness evaluation and credit scoring of natural persons, which sets the boundary for this use case.
- EBA Guidelines on loan origination and monitoring (European Banking Authority, Europe). Expectations on creditworthiness assessment, the information to collect and the use of automated models in credit decisions.
- MAS consultation paper on proposed Guidelines on AI Risk Management (Monetary Authority of Singapore, Asia Pacific). Consultation issued 13 November 2025, not final guidelines. Proposed expectations on AI inventory, risk materiality and human oversight, relevant for agents that support credit decisions.
Controls to put in place
- Model inventory entry and independent validation before live use
- Documented borrower scope, with a check that no natural persons are assessed without the high risk controls
- Source logging and draft version history retained with the credit file
- Periodic back testing of spreads and memo quality
- Clear accountability, with the approver named in the authority matrix owning the decision
Frequently asked questions
- How much faster can AI make a credit memo?
- Public results are still mostly targets. DBS has set a goal of reducing the time spent by at least 30% and rolled its agentic solution out to about 1,500 employees. Measure your own baseline per memo type before claiming a saving.
- Is AI credit memo drafting high risk under the EU AI Act?
- Not for companies. Annex III point 5(b) covers creditworthiness evaluation of natural persons, so corporate credit analysis is outside it, but assessing sole traders or personal guarantors can bring the system into scope.
- Who is accountable for the memo?
- The relationship manager and credit risk manager who complete it, and the approver in the credit authority matrix. The agent prepares a draft; it does not recommend approval on its own.
How to cite this page
Blits.ai AI Use Case Library, "AI agent for corporate credit analysis and credit memo drafting", last verified 27 September 2026, https://www.blits.ai/ai-use-cases/credit-memo-drafting-agent. Licensed under CC BY 4.0. Method: how we verify use cases.
Changelog
- 27 September 2026: First published