Why customers go unreachable in collections (even when you already buy contact data)

You buy bureau data, you may buy a second contact file alongside it, the team runs the standard refresh cadence, and a stubborn portion of the portfolio still cannot be reached. For debt purchasers, DCAs, and in-house collections teams, this is one of the most expensive problems in operations. 

Diallers churn through dead numbers, letters go to addresses that no longer exist, and vulnerability indicators are missed because the customer never picked up.

The problem is, the data has aged.

Bureau records are built from confirmed events that lenders have already shared, ingested and matched. By the time you query a file, you’re looking at a customer’s past, and not their present. Numbers, addresses, employers and email accounts move on. The file does not move at the speed your operation requires. This is a structural feature of how credit information works, and it has a measurable cost.

Bureau data limitations: Why credit files limit collections contactability

It might sound obvious, but bureau files are aggregated from data that lenders, utilities and other regulated firms share with the three large CRA’s. The information is reported, ingested, matched to existing records and made available for queries. This process takes time by design.

The system is built to confirm what has happened. This could be a default registered, a new account opened, or an address recorded against a credit application. These are reliable for credit risk decisioning on customers whose circumstances have settled.

For finding a customer who has just moved house, switched mobile provider or changed jobs, the same system is working against you. The event needs to happen, get reported, get ingested and get matched before it appears on the file you’re querying.

The FCA’s Credit Information Market Study put numbers on this. After examining the records held by the three large CRAs, the regulator found that the three large CRAs only held consistent information on the number of defaults for around 30% of matched individuals. On one of the most heavily reported variables in the entire system, the three biggest bureaus agreed on roughly three in ten cases.

If consistency is patchy on default records, it’s worth being honest about what that means for a phone number, an email or a current employer.

↪ Related reading: Is it the end of the credit bureau monopoly? 

How fast does Collections contact data expire?

Industry benchmarks put annual contact data decay at around 22%. Phone-specific decay sits closer to 18% per year. Apply that to a portfolio of 500,000 accounts, and roughly 110,000 contact records become unreliable inside 12 months. None of them flags themselves. They simply stop working.

Here are some of the main reasons:

#1. Mobile churn is high

UK mobile switching rates ran at around 16% in 2021, according to Ofcom’s Switching Tracker, which is a sector-wide figure that has stayed broadly stable since. Vodafone alone reported a UK mobile churn rate of nearly 30% in its most recent published European data, the highest across its European footprint. SIMs are swapped, networks are switched, and new numbers rarely find their way back to the lender’s file. 

#2. Address change is constant

Around 428,285 households in the UK wanted to move to another home as of July 2024, alongside hundreds of thousands more already in the process. The first half of 2024 alone saw home moves rise by around 10% on the previous year, according to Lloyds Bank’s Home Mover Review. An address captured on a credit application 18 months ago is likely out of date today.

#3. Employment churn

UK unemployment was 4.9% in early 2026, with around 1.78 million people unemployed and 711,000 vacancies open, according to ONS Labour Force Survey data. Several million people move in and out of work each year. Job changes, contract endings, hours reductions and moves in and out of self-employment all happen without any feed into a credit file 

#4. Relationship changes (separation, divorce, bereavement)

In 2023, there were 102,678 divorces and 1,138 civil partnership dissolutions in England and Wales, on top of cohabitation breakdowns that don’t appear in court figures. Each separation typically triggers an address change, often a phone change, and sometimes a name change. Bereavement adds further volume that may not reach the lender’s record for months. 

#5. Email account abandonment

The average UK adult holds two to three email addresses. Older accounts get abandoned when people change ISPs, leave employers, or simply stop checking. The address that was active when the account was opened may no longer be monitored, and bounce-back rates won’t always tell you which is which. 

Layer these together across a 12-month window, and the file ages whether or not anyone touches it. The customer record was accurate when you bought it. The world moved.

The collections match rate ceiling: Why standard data providers share the same pool

Most operations teams will recognise the curve. The first data refresh delivers a decent uplift. The second helps less. The third barely moves the needle. Match rates flatten, and the team concludes they’ve hit a ceiling.

The reason is the structure of the data ecosystem itself.

Every standard provider (bureau, supplementary contact file, secondary tracing service) is drawing from a similar pool of information. Lenders share data with the three large CRAs. CRAs share onward with downstream providers. Tracing tools layer in public records that update on their own slow cadence. The pool is large, but it has the same fundamental composition wherever you draw from it.

Refreshing inside that pool gets you the same answer faster. It doesn’t get you a different answer.

The customers who remain unreachable tend to share a profile:

• They’ve moved recently. 
• They’ve changed mobile providers. 
• They’re in flexible work, gig contracts or self-employment. 
• They have thinner credit files because their lending activity doesn’t update as frequently.
• Younger demographics and more geographically mobile customers appear here too.

 

These are also the customers most likely to need engagement and most likely to fall under the FCA’s vulnerability characteristics. They’re the ones a Consumer Duty review will ask the hardest questions about.

The standard ecosystem wasn’t built to capture them at the speed at which their lives change. It was built to confirm credit events for stable, well-recorded individuals. That’s a different problem.

Closing the residual takes data captured outside that ecosystem, sourced closer to the event, refreshed on its own cadence, and not constrained by the same lender-shared model that produced the file you started with.

↪ Related reading: How to use 35% better match rates to cut cost-to-collect

Alternative data sources: Find unreachable customers outside the credit bureau ecosystem

The bureau model works by aggregating data that lenders, utilities and other regulated firms choose to share. [It’s a closed loop by design.] The information that travels through it is rich in confirmed credit events and lighter on everything else.

Another category of data exists outside that loop. It’s captured directly from consumer-facing sources rather than reported through lender contributions. It’s refreshed on its own cadence, often daily or in real time. And it covers ground the lender-shared model was never built to cover.

For collections operations, four types of output do most of the work:

1. Verified contact information. Phone numbers and email addresses are validated against current activity, not last reported usage. The validation point is the difference. A number that someone is actively using today is worth more than one that worked when the account was opened.
2. Current employment data. Where someone is working now, captured through sources outside the credit-reporting framework. This is where collections strategy meets affordability decisioning, particularly for customers in flexible work whose income volatility never appears on a credit file.
3. Income and expenditure indicators. Real-world spending and earning behaviour that shows financial change before arrears confirms it. For Consumer Duty evidencing, this is the data that lets a firm say it identified a customer’s circumstances proportionately and in time.
4. Vulnerability indicators. Behavioural data aligned to the FCA’s four drivers of vulnerability (health, life events, resilience and capability), refreshed at a frequency that supports proactive identification rather than retrospective disclosure.

 

The structural advantage is the same across all four. The data isn’t waiting for a lender to report it.

↪ Related reading: What your current collects data isn’t telling you about your customers

How alternative data drives collections performance and contact uplift

Where alternative data is layered onto records that traditional sources have already returned as no-trace, match rates lift by around 35% on validated phone numbers and email addresses. The records that the standard ecosystem could not place are the ones that move first.

One of the UK’s largest debt purchasers ran this directly. 

They handed over a sample of records that their existing providers had been unable to match. The data went through Data On Demand’s tracing process. 

• Connect rates on the resulting contacts came in at typical activity levels. 
• Right party contacts rose above the standard benchmark. 
• Revenue per right party contact exceeded the usual average. 
• Revenue per dial held steady, indicating the lift was coming from data quality rather than channel intensity.

 

Essentially, records the team had already written off as unreachable produced collections performance in line with, or better than, the standard book. The cost of contact dropped because the dialler was working against numbers that were live.

For operations leaders, the operational change is what gets quantified in a Proof of Value. A sample of unmatched records goes through the same workflow the team uses today. The output shows what proportion is recoverable, what the contact uplift is on the team’s own portfolio, and what the commercial impact looks like at scale.

Most teams find that the answer materially changes how they think about their unmatched residual.

A quick diagnostic for senior leaders Three questions tend to reveal more about the unmatched residual than another data refresh would. What’s the median age of the contact data we’re calling against? Most teams cannot answer this directly. The question itself is diagnostic. What’s our match rate ceiling across the last three providers we’ve tested? If the curve has flattened, the next provider in the same data pool will not change it. Of our unreachable customers, what proportion shows signs of recent change, such as a recent move, a job change, or a new account opened elsewhere? This is the cohort for which an alternative data source is built to find.   Most teams do not know the answers until they look. A Proof of Value run on a sample of unmatched records is the fastest way to get them, on your own portfolio, against the metrics your team already reports against. Run a Proof of Value Project

Key takeaway

The bureau model works as intended for confirmed credit events on settled customers. But for finding a customer who has just moved, switched mobile, or changed jobs, the same model works against the operation.

The residual that current providers cannot place is not random. It carries a profile. Recently moved customers, gig and PAYG workers, thinner-file households, and customers showing vulnerability characteristics under Consumer Duty. The cohort the standard ecosystem misses is also the cohort regulators expect firms to find soonest.

Closing the residual takes data captured outside the lender-shared loop, refreshed on its own cadence. That data exists. It just isn’t in the file you started with.

You can find out more about FinTrace here or talk to us. We’d genuinely love to help. 

Or if you still have unanswered questions, check out our most frequently asked questions below.

Frequently asked questions

Why do collections teams have low right party contact rates even when they buy bureau data?

Bureau data records confirmed credit events that lenders have already shared with the credit reference agencies. Reporting and matching takes time. By the time a record is queried, the customer’s address, phone or employer may have changed. Match rates plateau because every standard provider draws from the same lender-shared pool.

How often does customer contact data go out of date?

Industry benchmarks put annual contact data decay at around 22%. Phone-specific decay sits closer to 18% per year. On a portfolio of 500,000 accounts, that means roughly 110,000 contact records become unreliable inside 12 months, driven by mobile switching, address change, employment moves and relationship change.

What does alternative consumer data mean in collections?

Alternative consumer data is captured directly from consumer-facing sources rather than reported through lender contributions to credit reference agencies. It covers verified contact information, current employment, income and expenditure, and vulnerability indicators. The data is refreshed on its own cadence, often daily, and is not constrained by the lender-shared model.

What are the regulatory consequences of out-of-date contact data under Consumer Duty?

Consumer Duty requires firms to evidence proportionate, timely engagement with customers, particularly those showing characteristics of vulnerability. Acting on contact data that is months out of date undermines that evidence. Firms can demonstrate the right process internally and still face questions if the underlying data did not reflect the customer’s current circumstances.