Catch arrears early, before they escalate

Your AI employee detects arrears patterns before they become crises. It sends early, sensitive outreach, offers flexible payment plans, connects tenants to your money advice team, and keeps detailed case notes — so nothing falls through the cracks.

The challenge

Why this workflow is broken today

01

Late intervention makes recovery harder

By the time most housing associations contact a tenant about arrears, the debt has grown to a level that feels insurmountable. Early intervention — within the first week — dramatically improves recovery rates, but teams don't have capacity to chase every missed payment instantly.

02

Sensitive conversations need the right tone

Arrears are often a symptom of deeper problems — job loss, illness, relationship breakdown. Generic demand letters alienate tenants who might engage with a more supportive approach, pushing them further away from resolution.

03

Case tracking is inconsistent

When income officers manage 800+ accounts each, notes don't always get logged, follow-ups slip, and the full picture of a tenant's situation is scattered across systems. This means the next person who contacts the tenant starts from scratch.

How your AI employee handles this

From request to resolution

1

Detect patterns early

The AI employee monitors rent accounts daily, identifying missed payments, partial payments, and changing payment patterns. It doesn't just react to arrears — it predicts them, flagging accounts where behaviour has changed (e.g., a tenant who always paid on the 1st now paying late two months in a row).

2

Make sensitive, immediate contact

Within 24 hours of a missed payment, the AI employee reaches out via the tenant's preferred channel with a supportive, non-threatening message. It acknowledges the missed payment, offers practical next steps (payment plan, benefits check, money advice referral), and asks if there's anything the housing association can help with.

3

Create and manage payment plans

If the tenant engages, the AI employee can offer payment plan options based on your policies and the tenant's circumstances. It sets up the plan, sends reminders before each payment is due, and adjusts if the tenant's situation changes — all logged in your HMS with a complete audit trail.

4

Escalate with full context

If the tenant doesn't respond after your defined number of contact attempts, the AI employee escalates to a human income officer — but with a complete case file: every contact attempt, every response, the tenant's history, any vulnerability flags, and a recommended next action. The officer picks up a warm case, not a cold one.

Scenario

How Ashford Community Homes would use this

Organisation

Ashford Community Homes

A 9,500-home housing association in the East Midlands with a current tenant arrears rate of 5.8% — above their 4% target — and a team of 6 income officers each managing approximately 700 accounts in arrears.

Challenge

Ashford's income team could only prioritise accounts above £500 in arrears, meaning tenants with smaller balances weren't contacted until the debt grew significantly. Average first contact was 18 days after the first missed payment. Their collection rate had dropped to 98.2%, with bad debt write-offs increasing year on year.

Approach

The AI employee was configured to contact every account within 24 hours of a missed or partial payment, using Ashford's tone-of-voice guidelines and escalation policy. Income officers retained all complex cases and those involving court proceedings.

Projected results

18d → 1dAverage time to first contact after missed payment
5.8% → 3.6%Current tenant arrears rate after 9 months
42%Of arrears cases resolved without human involvement
98.2% → 99.1%Rent collection rate improvement

The AI catches things we simply couldn't get to. A tenant who misses one payment and gets a supportive message the next day is much more likely to engage than one who gets a formal letter three weeks later.

Head of Income, Ashford Community Homes (representative scenario)

This is a representative scenario based on typical client profiles. Specific results vary by organisation.

Frequently asked questions

Common questions about this workflow

How does it handle Universal Credit tenants specifically?

The AI employee understands the common UC payment patterns (monthly in arrears, 5-week wait, managed payments). It adjusts arrears thresholds and contact timing for UC tenants, and can help tenants request managed payments or alternative payment arrangements where appropriate.

What about tenants with known vulnerabilities?

The AI employee checks vulnerability flags in your HMS before making any contact. For tenants with known vulnerabilities, it adjusts its approach — using simpler language, offering more support options, and escalating to a human advisor sooner. It never threatens legal action against a tenant flagged as vulnerable.

Can it handle court and legal processes?

No — and intentionally. Court proceedings, eviction processes and legal correspondence are always handled by human officers. The AI employee's role is to resolve arrears before they reach that stage, and to provide complete case documentation if legal action does become necessary.

How does it work with the DWP and benefits system?

The AI employee can identify tenants who may be entitled to additional benefits or support, and signpost them to your money advice team or external services. It tracks Housing Benefit and UC payment dates to distinguish between genuine arrears and payment timing issues.

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