A federal judge just ordered a bankruptcy firm to give clients their money back. Nobody filed a motion. She found it on her own.

A published opinion out of Chicago this week (In re Bell, N.D. Ill. Bankruptcy, July 1).

The firm ran a $0-down Chapter 7 model. You pay nothing before filing. Then within 10 days you sign a second agreement for around $2,500, and the paperwork says they can drop you if you refuse. The judge noticed an odd fee disclosure on one docket, started pulling the firm's other cases, and found the same structure over and over. Same recycled agreement, same signature image pasted across different clients' filings. She held a hearing, took briefing from the US Trustee, and ruled the arrangement violated the parts of the Bankruptcy Code that require fee agreements to be clear and honest. The firm has to disclose everything it collected after filing and return it in the three cases before her.

Two things stuck with me from the opinion. Judges can review attorney fees without anyone asking them to. And when that happens, the attorney has to justify the fee. The client doesn't have to prove anything.

A lot of people assume that once a lawyer has your money, it's gone no matter what happened. This opinion says otherwise. It's on the court's website and it's a readable 15 pages.

Threads about a lawyer going quiet, or someone wanting out, come up here pretty often. Nobody ever seems to know if you can actually get any of it back. This is the clearest yes-side answer I've seen published. Has anyone here ever had a judge or trustee actually question what your attorney charged?

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u/ilikemath9999 — 12 hours ago

Chart: https://imgur.com/a/Y3Ivi8j

After 15 years averaging ~13 § 523(a)(8) student-loan-discharge adversary proceedings filed per fiscal year, the November 17, 2022 DOJ/ED Brunner-relaxation guidance triggered an immediate surge in filings. DOJ has publicly disclosed two cumulative counts:

- 632 § 523(a)(8) APs filed in the first 11 months after the guidance (Nov 2022 to Sep 2023), per DOJ press release Aug 2023.

- 1,220 cumulative APs through March 2024, per DOJ press release Apr 2024.

The chart shows fiscal-year shape from an OBP RECAP-derived dataset, which is a floor estimate (~9-13% of DOJ totals) but provides year-over-year and district-level granularity that DOJ does not publish.

Per-fiscal-year counts (OBP RECAP-derived, floor estimate):

FY 2008: 12

FY 2009: 6

FY 2010: 6

FY 2011: 5

FY 2012: 13

FY 2013: 10

FY 2014: 3

FY 2015: 22

FY 2016: 10

FY 2017: 9

FY 2018: 11

FY 2019: 25

FY 2020: 28

FY 2021: 16

FY 2022: 19 (last full FY before DOJ guidance)

FY 2023: 57 (Nov 2022 guidance hits ~6 weeks in)

FY 2024: 179

FY 2025: 424

FY 2026: 857 (partial through April 27, 2026, ~7 of 12 months)

Pre-DOJ baseline FY 2008 to FY 2022: n=195 over 15 years, ~13 per year average.

Sources:

- Absolute counts (632 / 1,220): U.S. Department of Justice press releases, Aug 2023 and Apr 2024.

- Fiscal-year shape data: Open Bankruptcy Project (OBP) § 523(a)(8) RECAP-derived dataset, n=1,770 dated APs from CourtListener / RECAP archive coverage of U.S. bankruptcy adversary proceedings, FY 2008 to April 2026.

OBP matcher disclosure: the OBP scrape identifies APs via case-name pattern matching (defendant servicer + "v." + bankruptcy court code). Known systematic gaps:

- APs styled "Debtor v. United States" rather than against a specific servicer are excluded.

- APs against private servicers not in our servicer corpus (AES, Discover, KHEAA, others) are excluded.

- The CourtListener nature-of-suit field is empty for nearly all bankruptcy adversary proceedings, so a NOS-based filter is not feasible against this dataset.

These gaps are why OBP counts run roughly an order of magnitude lower than DOJ's. The directional shape of the time series is robust and corroborated by DOJ's own published numbers.

Caveats:

- RECAP archive coverage of older bankruptcy adversary proceedings is incomplete and uneven across districts and years. Pre-2018 absolute counts are floor estimates.

- The chart counts APs filed, not APs that result in actual discharge of student-loan debt. Roughly half of filed APs settle or are dismissed before judgment.

- The DOJ guidance applies to federal student-loan APs handled by U.S. Trustees and the Department of Education. Private-servicer APs follow different procedural paths.

- DOJ does not publish § 523(a)(8) AP filing counts for the pre-2022 era. The "~13 per year" baseline is OBP's RECAP-derived floor estimate.

Tool: hand-built SVG (no charting library). Bars per fiscal year FY 2008 to FY 2026, with reference lines at BAPCPA 2005, Nov 2022 DOJ guidance, June 2023 SAVE plan launch.

Disclosure: Open Bankruptcy Project is a 501(c)(3) public charity (IRS Letter 947 issued 4/6/2026, EIN 41-5159631, public-charity status under 170(b)(1)(A)(vi)). No paywall, no ads, no donations solicited in this post. We do not accept attorney advertising or referral fees. Aggregator script and raw per-AP CSV available on request.

u/ilikemath9999 — 2 months ago

Follow-up to my state-level chart from last month:(https://www.reddit.com/r/dataisbeautiful/comments/1rur949/).

For v1 I aggregated district-level data up to states, which masked huge variance inside multi-district states. This maps all 91 federal bankruptcy districts directly.. same FY2023 BAPCPA Table 6 source.

Caveat: 26 of the 50 states are single-district, so v1 and v2 show identical rates for those. The variance reveal is in the 24 multi-district states. New York averages 64% statewide but ranges from NY-E (Long Island/Brooklyn) at 91% - the highest in the country - to NY-N (upstate) at 34%. Texas (TX-N 64% / TX-S 55%), California (CA-C 60% / CA-N 37%), and Georgia (GA-N 64% / GA-M 45%) show similar in-state splits.

The deeper pattern: multi-district states are population centers... urban venues with enough caseload to need multiple courthouses. Those are also where high-volume consumer bankruptcy practices operate at scale, which correlates with elevated dismissal rates. The single-district rural states (ND 21%, VT 21%, MT 21%) sit low partly because that operating model doesn't scale at low caseload.

Tools: Python (matplotlib, geopandas). Geometry: HIFLD US District Court Jurisdictions. Source: [BAPCPA Table 6, FY2023, uscourts.gov](https://www.uscourts.gov/statistics-reports/bapcpa-report-2023)

u/ilikemath9999 — 2 months ago