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Case Studies

Real people.
Real results.

Five customer stories: what was on each credit report, what our AI flagged, how the dispute was handled, and the score each customer reported.

Actual clientsReal stories, real results
Reported resultsScores as reported by each customer
Lasting impactBetter credit, better opportunities

Scores are as reported by each customer. Results vary and are not guaranteed.

Fraudulent account Within six weeks

A fraudulent account was identified and removed within six weeks

Sarah Kim had a fraudulent credit-card account appearing on her report. She associated the card with a data breach and needed a way to address the account through the credit-report dispute process.

On the report Fraudulent account Removed

“A fraudulent card from a data breach was gone within six weeks of connecting my report.”

SKSarah KimChicago, IL
Read the case · 4 min
Duplicate loan reporting

A loan balance was reported twice under two servicers

Michael Torres had a starting score of 630 and a reporting problem involving one loan balance.

On the report Duplicate loan reporting Merged

“My loan balance was reported twice under two servicers. Fixing that alone moved my score 60 points.”

MTMichael TorresLos Angeles, CA
Read the case · 4 min
Three report errors Six weeks

Three report errors were found in minutes

Marcus Bennett had already been refused two loans before using AI Credit Care.

On the report Three report errors Removed

“I’d been refused two loans before this. The model found three errors on my report in minutes, drafted the disputes, and my score went up 71 points in six weeks.”

MBMarcus BennettAustin, TX
Read the case · 3 min
Obsolete medical collection One round

An obsolete medical collection was removed in one round

Renee Ortiz had a medical collection appearing on her credit report and a starting score of 588.

On the report Obsolete medical collection Removed

“I didn’t know a medical collection could age off my report. The model flagged it as obsolete and it was removed in one round.”

RORenee OrtizDenver, CO
Read the case · 3 min
Three duplicate accounts

Three duplicate accounts from the same lender were identified

David Chen had a starting score of 601 and three accounts from the same lender that he identified as duplicates.

On the report Three duplicate accounts Disputed

“Three duplicate accounts from the same lender were dragging me down. Once those were disputed, my score jumped fast.”

DCDavid ChenSeattle, WA
Read the case · 3 min

Score changes are as reported by each customer. Your own result may be smaller, or your score may not change at all.

Five Problems, Five Findings

Different reports. Different problems.

Each case started with a specific entry on the customer’s report rather than a generic score problem. Read any case for the full story: the situation, what was found, the process, and what the case shows.

CaseCore problemWhat the AI flaggedLink
Sarah KimChicago, ILA fraudulent card from a data breachFraudulent accountRead the case: Sarah Kim
Michael TorresLos Angeles, CAThe same loan balance appearing twiceDuplicate reportingRead the case: Michael Torres
Marcus BennettAustin, TXTwo loan refusals and unclear report problemsThree report errorsRead the case: Marcus Bennett
Renee OrtizDenver, COAn old medical collectionObsolete collectionRead the case: Renee Ortiz
David ChenSeattle, WASeveral entries from the same lenderThree duplicate accountsRead the case: David Chen
The Common Journey

The same seven steps behind every case.

The customer problems are different, but the workflow follows one pattern. See how AI Credit Care works and how our AI reads your reports for more detail.

  1. 01

    Connect the report

    The customer connects their credit information or provides the report for analysis.

  2. 02

    Analyze the data

    The AI looks at account-level information, not only the headline score: account details, balances, payment history, reporting dates, duplicate entries, possibly obsolete information and differences between bureaus.

  3. 03

    Surface findings

    Entries that may deserve investigation are flagged.

  4. 04

    Explain the finding

    The customer sees what was found and why it matters.

  5. 05

    Draft the dispute

    Once a finding has been reviewed, the platform helps prepare the dispute.

  6. 06

    Customer reviews and approves

    The AI assists with analysis and preparation; it does not replace the customer’s review and approval.

  7. 07

    Submit and track

    After approval, the case moves into submission and response tracking.

These stories use the score figures, customer quotes, locations, issue descriptions, resolutions and timelines supplied for the AI Credit Care case-study material. A reported before-and-after score does not by itself establish that one individual dispute caused the entire change: credit scores can change because of multiple factors and changes in credit-report data. Where a customer described a particular impact, it is presented as a customer-reported experience, not an independently verified measurement. Where an exact timeline was not supplied, no dates are added. Results vary and are not guaranteed.

Real Impact

Better credit.
Brighter opportunities.

Your results depend on your own credit file.

Your Story Could Be Next

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