We define the Legal Analytics field as the integration of a broad array of techniques used to make supportable conclusions from data obtained in research, investigation, and litigation. This includes, in consumer law, an understanding of financial products and how they are used in practice by consumers, familiarity with common usage patterns and typical abusive practices by providers, experience with the systems infrastructure, data structures, and operational technologies used by those companies, and the application of advanced (and not-so-advanced) statistical, AI/ML, and general data analysis methods for deriving insights, trends, and factual conclusions from the data created by both modern and archaic financial systems.

Services

We support all phases of research and litigation on a broad array of technologies and consumer financial products, as consulting experts, and in many scenarios as testifying experts.

Background

We have worked on cases involving the following statutes:

  • UDAAP / UDAP,
  • EFTA (billing errors, overdraft rules, compulsory use),
  • FCRA,
  • FDCPA,
  • TILA,
  • ECOA,
  • Privacy Regulations, and
  • state usury laws

And we have worked extensively with datasets from financial products including:

  • bank accounts,
  • credit cards,
  • mortgage origination and servicing,
  • student loan servicing,
  • credit line furnishing,
  • consumer reporting,
  • remittances,
  • auto lending and servicing,
  • payday and other short term small dollar lending (including auto title), installment loans,
  • prepaid cards,
  • debt collection,
  • debt settlement, debt relief, credit repair,
  • rent-to-own,
  • fintech banking, and
  • peer to peer money transfers.

More importantly, we have worked with very messy (sometimes intentionally) data and systems across these domains and have become very familiar with requesting information in ways that lead to complete productions and analyzable data while overcoming technical and burden limitations in ways that reduce friction with the custodians, whether first or third party.

Examples of what we have done / can do for you:

  • Calculate across a population of consumers, the total amount they paid on payday loans in comparison to what was disclosed in the TILA box

  • Compare the fees that consumers actually paid to the fee structures in their prepaid card disclosures, across multiple states with different fee structures

  • Question company personnel on what factors go into a fraud model that suspends a consumer’s bank account, or a tenant screening model that determines whether a consumer can rent an apartment

  • Systematically generate loan amortization schedules and compare them with the actual consumer payments

  • Project and estimate how much consumers may have been overcharged, etc, for periods of time that we don’t have data for

  • Determine whether consumers actually got the refunds the company stated they were going to provide

  • Identify the prevalence of errors or issues across the company’s entire population of consumers

  • Quantify the amount that consumers paid to debt settlement companies without ever getting a debt settled