What problem does it solve?
When a borrower gets into difficulty the right intervention early is cheaper for everyone than enforcement later. But finding it is slow. A hardship or workout specialist has to pull together balances, arrears history, income and expense evidence, collateral and the borrower's own explanation, then work through policy to see which options are allowed and affordable. Queues grow exactly when times are hard, decisions vary between specialists, and the reasons are not always written down.
Regulators expect lenders to treat borrowers in financial difficulty fairly, to choose sustainable solutions over short term fixes that fail, and to document why. Inconsistent or undocumented concessions are a conduct risk and a credit risk at the same time.
How does it work?
- Assemble the position. The assistant gathers balances, arrears, payment history, other exposures, collateral and any income or hardship evidence the customer has provided.
- Read the evidence. Document AI extracts figures from payslips, bank statements and letters, and flags gaps.
- Test the options. For each option the policy allows (due date change, payment holiday, term extension, temporary rate relief, capitalisation), it calculates the new payment, the effect on arrears and whether it fits the stated budget.
- Recommend with reasons. It ranks the options, cites the policy clause behind each, and writes a short rationale and the risks.
- Decide and record. A specialist accepts, changes or rejects the recommendation; the decision, the reasons and the evidence are stored with the case.
- Follow up. Review dates are scheduled and the arrangement is monitored for early signs that it is not working.
- Audience
- Employee facing
- Autonomy
- Copilot
- Adoption
- Emerging
- Channels
- Agent desktop, Internal tools, Mobile app
What is it worth?
Benchmarks are computed from the public deployments below: one data point per organization per KPI, with who made each claim.
No public deployment has disclosed a measurable outcome yet.
Value drivers: Risk and loss reduction, Customer experience, Employee productivity, Compliance quality.
Indicative value
A retail bank handling 10,000 hardship and restructuring requests a year
USD 225,000 to USD 1.1 million
Specialist time released per year
How this is calculated
Formula: requests * hoursSaved * hourlyCost. The low scenario uses every low input, the high scenario every high input.
| Input | Low | High | Basis |
|---|---|---|---|
| Hardship and restructuring requests per year requests, requests per year | 10,000 | 10,000 | The reference bank. |
| Specialist hours saved per request on assembling the case and testing options hoursSaved, hours per request | 0.5 | 1.5 | Editorial assumption. Replace with your own time study. |
| Fully loaded cost of a hardship specialist hour hourlyCost, USD per hour | 45 | 70 | Editorial assumption. |
What it leaves out: Counts specialist time only. It leaves out the credit effect of earlier and more sustainable arrangements, fewer broken plans and lower complaint volumes, which can be larger but need a controlled measurement.
Who already uses it?
1 public deployment, strongest evidence first. Grades: A regulator or audit, B the organization itself, C vendor case study, D anonymous or estimate.
Commonwealth Bank of Australia
Australia · Banking · 2022
Commonwealth Bank's Customer Engagement Engine (CEE), built on Pega Customer Decision Hub, suggests in real time the next best conversation to have with each customer, whether in the branch, on the phone, online or on a mobile device. Beyond suggesting conversations, the bank uses it to match customers to government benefits and rebates they may be missing (Benefits finder) and to reach customers hit by natural disasters with same day support such as a loan deferral. The same decisions feed digital channels and prompts for branch and contact centre staff.
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
- Restructuring and hardship policy with eligibility rules per product
- Account, arrears and payment history per borrower
- Income and expense evidence, or a structured budget from the customer
- Outcomes of past arrangements to learn which options last
Systems to integrate
- Loan servicing and collections systems
- Document intake for hardship evidence
- Case management for hardship and workout teams
- Customer channels for evidence requests and outcome letters
Complexity: Medium
The option calculations are deterministic once policy is written down. The effort is in encoding policy, reading hardship evidence reliably and fitting the assistant into the specialist's case workflow.
- 1
Encode the policy
Turn the restructuring policy into explicit rules per product: which options, for how long, with which limits and approvals. The assistant can only recommend what the rules allow.
- 2
Automate the case pack
Start by assembling the borrower's position and evidence automatically. Specialists gain time even before any recommendation is shown.
- 3
Add option testing and ranking
Calculate each allowed option's payment and effect, rank them on affordability and sustainability, and show the calculation behind every number.
- 4
Measure agreement and outcomes
Track how often specialists accept the recommendation and how arrangements perform after six and twelve months, by option and segment.
- 5
Extend to proactive outreach
Once recommendations are trusted, combine them with early warning signals to offer support before customers fall behind.
Guardrails
- Recommendations only; every restructure is approved by a person with authority
- Options limited to what policy allows, with the policy clause cited
- A written rationale stored with every decision
- Consistency checks that flag similar cases receiving different outcomes
- Vulnerability flags shown prominently and never used to reduce support
KPIs to instrument
- Time from hardship request to decision
- Share of recommendations accepted without change
- Arrangements still performing after six and twelve months, by option
- Complaints about hardship decisions
- Outcome differences between comparable customers
Human in the loop
Hardship and workout specialists decide every case and can override any recommendation, with a reason. Credit risk approves the rules and reviews arrangement performance; conduct risk reviews consistency and outcomes for vulnerable customers.
Common failure modes
- Short term fixes that fail
- The assistant optimises for the lowest payment now and the arrangement breaks later. Rank on sustainability and track long term outcomes.
- Rubber stamping
- Specialists accept recommendations without reading them. Show the reasoning, sample decisions for review and measure override quality.
- Evidence misread
- Wrong income or expense figures lead to an unaffordable plan. Show the source document next to each extracted figure.
What are the risks and rules?
EU AI Act
Depends on design
Recommending restructuring terms for individuals involves assessing their ability to pay, which can amount to evaluating the creditworthiness of natural persons under Annex III point 5(b). Human approval alone does not remove that: the Article 6(3) exception covers only systems that do not materially influence the decision, such as a narrow procedural or preparatory task, and never applies when the system profiles natural persons. A tool that only assembles the case file can fall under the exception; restructuring for companies is outside point 5(b).
Rules that apply
Guidance
- Guidance to banks on non-performing loans (European Central Bank, Europe). Sets expectations for viable forbearance solutions, borrower affordability assessments and clearly defined, consistent decision making procedures.
- FG21/1: guidance for firms on the fair treatment of vulnerable customers (Financial Conduct Authority, Europe). Fair treatment of customers in vulnerable circumstances, relevant to every hardship decision.
- REP 782 Hardship, hard to get help: Findings and actions to support customers in financial hardship (Australian Securities and Investments Commission, Asia Pacific). Review of how 10 large lenders handled home loan customers in financial hardship (May 2024), with good and poor practices; ASIC says the insights are relevant to hardship involving all types of credit.
Controls to put in place
- Policy rules under change control, approved by credit risk
- Decision log with recommendation, final decision, override reason and evidence
- Periodic consistency review across comparable cases
- Outcome monitoring for vulnerable customers
Frequently asked questions
- Can AI decide a loan restructure?
- It should recommend, not decide. A person with authority approves every restructure, with the assistant's calculation and rationale in front of them, because hardship cases need judgment and fair treatment.
- Is anyone using AI to offer hardship support proactively?
- In 2022 Commonwealth Bank said it used its Customer Engagement Engine and a weather data model to reach customers hit by natural disasters with same day support, such as deferring a loan or an emergency overdraft. That is proactive hardship outreach, not a restructuring recommender. A US auto lender's collections agent, as described by its vendor, spots borrowers' pay patterns on the call and triggers a change of due date in the lender's system, and routes hardship cases to its dealerships.
- What makes a good restructuring recommendation?
- One that the borrower can sustain, that policy allows, and whose reasoning is written down. Track arrangements for six to twelve months to learn which options actually last.
How to cite this page
Blits.ai AI Use Case Library, "AI recommendations for loan restructuring and hardship arrangements", last verified 27 September 2026, https://www.blits.ai/ai-use-cases/loan-restructuring-recommendations. Licensed under CC BY 4.0. Method: how we verify use cases.
Changelog
- 27 September 2026: First published