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How Enter helped Nubank bring operational efficiency to consumer litigation
+6 p.p
2.5x
+6 p.p
2.5x
+6 p.p
Nubank’s legal department reached a 6 percentage-point gain in portfolio performance using Enter’s AI. The result showed up across the three main consumer litigation subjects, in specific regions, measured across lawsuits filed in the same month.
Deployment of Enter’s technology ran 2.5x faster than planned. Over the period analyzed, lawsuits using Enter’s technology moved from 8% to roughly 80%.
Detection of an abusive litigation network: 10 professionals accounting for 38% of cases, using fraudulent documents and identical complaints.including one case that resulted in dismissal and a fine of 9% of the claim value imposed on the plaintiff.
According to Brazil's Central Bank, it is the largest private financial institution by number of customers, reaching approximately 61% of the country's adult population — a position built on consistent growth since 2022, moving up one spot per year since entering the top five.
CHALLENGE
Scale
7 of the 20 largest defendants in the country are financial institutions. The sector's consumer litigation totals more than 4.4 million active cases, with over US$ 15 billion tied up in provisions.
At Nubank, this challenge is amplified by its growth of over 41,000 new customers per day over two consecutive years. That pace of expansion translates into a growing volume of new legal claims — a natural byproduct of operating at this scale. A large share of these cases involve allegations of scams, fraud and unpaid debt, subjects that require detailed case-by-case analysis.
Solution
Nubank hired Enter to support legal customization at scale. The initiative was led by Tina Marcondes, Head of Legal Brazil, and Humberto Chiesi Filho, Legal Director of Dispute Resolution, and structured as a three-step workflow.
For each case, Enter activates over 30 integrations via APIs provided by Nubank to gather all relevant case information at scale. Our AI agents thoroughly review the data to verify the consistency of the claimant's narrative and distill scattered facts into a single, clear, actionable document for the legal team.
Key data collected includes:
- Call recordings and service logs, comparing what the customer claims in the complaint with what they actually reported to Nubank
- Customer communication history, including calls, chat and email
- Customer profile analysis, to verify whether transactions fall within the customer's usage patterns
- Information on the device used in the transaction
- Authentication records (passwords, biometrics, facial recognition)
- Credit and collections data (credit bureaus, debt history, payment records)
In this next step, Enter’s AI agents run more than 30 wrongdoing detection per lawsuit, covering expired documents, tampered proof-of-address records, bar registration status, and other recurring illegal patterns.
Roughly 20% of the lawsuits Nubank receives are tied to abusive litigation patterns. A diagnosis Enter ran uncovered patterns of concentration and coordinated action, shown in the distribution below.
Within this group, just 10 plaintiffs account for more than 38% of the abusive litigation, acting in a coordinated way. The average number of lawsuits per attorney in this group is nearly 50, against 1.5 for a regular plaintiff.
The two worst offenders requested a fee waiver in 100% of their lawsuits and a hearing waiver in more than 97%.
The analysis also uncovered the recurring use of expired documents, shown below, more than 12 years past their expiration date, and proof-of-residence records belonging to third parties, alongside identical initial filings repeated across lawsuits spread over more than 21 states.
“On its own, one lawsuit draws no attention. But when you see the patterns across hundreds of lawsuits, the story changes completely. It would take us months to do what Enter did in days.”
Enter’s AI agent maps these patterns in minutes. Running the same analysis by hand would take Nubank’s internal team months.
With the evidence assembled, Nubank's legal team defines the response criteria for each type of lawsuit. Based on those criteria, Enter's AI agents structure parameterized documents that bring together the most relevant facts and evidence — extending what the team can review and act on at scale.
“With Enter, we map success patterns and assess the relevance of each piece of evidence. The result is an agile defense that corrects flaws in real time and focuses on what truly determines the outcome of the case.”
The approach varies by lawsuit. In scam-related claims, for example, the AI identified:
- The device from which the transactions originated
- The existence of biometric authentication by the account holder
- Customer statements in service interactions - audio or text - that confirm the transactions were approved
- The customer's habitual transaction profile and any alerts Nubank issued during the transaction
- Recovery attempts made by Nubank
For lawsuits tied to abusive litigation patterns, legal inputs were structured around three signals: claim recurrence, standardized filings, and the concentration of amounts over time.
In total, more than 400 AI models, with 97% accuracy, support the generation of drafts and parameterized documents that enable Nubank to handle a share of its monthly lawsuits with heavy use of evidence and far less manual effort.
Impact
The strength of the operation fed straight into the results. Across lawsuits filed in the same month, Nubank’s legal department reached a 6 percentage-point gain in portfolio performance using Enter’s technology. That performance backs Nubank's decision to expand, step by step, the share of lawsuits supported by Enter's technology to roughly 80%.
At the same time, the reliability of the results let Nubank run the deployment 2.5x faster than the original schedule — addressing head-on the challenge of scaling faster.
“The results showed a 6 p.p. gain in performance across lawsuits filed in the same month. That reliability made us run the deployment 2.5x faster.”
Meanwhile, Enter let Nubank scale its customer base without growing the legal team at the same rate, with AI agents producing the legal inputs in minutes that would take months of manual work.
All of this rests on an engineering team drawn from some of the best schools in the world (such as ITA, Harvard, and École Polytechnique), which keeps Nubank’s own engineering focused on what matters most: keeping its customers fanatically in love with its products.
