Imagine receiving a spreadsheet with hundreds of cases from a borrowing law firm — your first instinct might be, “Great! Strong cashflows!” But here’s the hard truth: sheer case volume means nothing without verified quality.
Data hygiene is one of the most overlooked — yet critical — factors in effective collateral monitoring for litigation finance portfolios. While it may seem simple on the surface, the work is tedious and time-consuming, making it a discipline that only a few firms execute well and consistently.
In this post, we’ll debunk a common myth in litigation funding: more data means more value. We’ll explore how poor case data hygiene inflates risk through concealed red flags, and why the real competitive edge lies in clean, structured, and consistently validated data.
Data Volume Illusion: Not all that glitters pays out
When law firms hand over massive case dumps, it might seem like a treasure trove. But beneath the surface, not all cases will generate value – and some can quietly erode your returns and credibility.
Here’s what funders need to watch out for:
Non-Compensable Claims
A portion of submitted cases might fall into non-compensable categories: inactive statuses, weak merits, incomplete information, or outright invalid data. In some instances, spreadsheets are cluttered with test entries, placeholder values, or superficial data points, indicating a lack of diligence on the part of the borrower.
Common data issues we saw:
- Stage descriptions buried in attorney notes: Critical case milestones are hidden in free-text comments instead of standardized fields — making automation and comparison impossible.
- Non-significant injuries: Claimed damages don’t meet legal or medical compensability thresholds, weakening the basis for recovery.
- Statute of Limitations (SOL) risks: Cases include looming or already-lapsed deadlines that significantly reduce viability — often without disclosure.
- Vague or missing status markers: Key indicators, such as filing, discovery, or settlement stages, are absent or unclear, obscuring the case’s actual status.
- Outlier case durations: A block of cases may have timelines that extend beyond the expected life of the loan, disrupting modeled recovery cycles and cash flow expectations.
Dormant Dockets
Beyond non-compensable claims, another silent threat lurks in most litigation finance investments: dormant dockets. These are cases that appear active on paper but have seen no meaningful movement in months – or even quarters. Often buried in the messy data, these cases inflate the perceived value of the portfolio.
Common signs of dormancy include:
- Static attorney notes with recycled or vague language.
- No movements in injury or usage confirmation for quarters
- Last client contact exceeding months.
- Missing expected procedural events, like discovery deadlines or mediation dates
Ask yourself: When was the last timestamped activity across 20% of your borrowers’ case lists? Are you accounting for stagnant dockets in your projected fallout?
When dormant dockets aren’t flagged early, they’re often priced in as performing assets, becoming a capital trap that degrades returns.
Reality Check: A spreadsheet with messy data is a liability, not an asset.
In litigation finance, messy data doesn’t just slow you down — it actively undermines investment performance and introduces compounding risk.
- Overinflated Portfolio Values: Duplicate or invalid entries may be counted as active assets, thereby inflating NAV and distorting underwriting models from the outset.
- Unreliable Cash Flow Forecasts: Without structured data on litigation stages and resolution paths, capital planning and pricing decisions rely on guesswork.
- Hidden Operational Risk at Law Firms: Disorganized data often reflects broader dysfunction in borrower operations — from intake workflows to case management.
- Eroding LP Confidence: Limited partners expect rigor, not spreadsheets filled with inconsistencies. Poor reporting damages credibility and slows fundraising.
On the other hand, clean data is what separates consistent, predictable returns from compounding, unforeseen risks. With high-quality data in place, funders can develop targeted signals and metrics to guide decision-making. In upcoming posts, we’ll explore the key indicators that drive smarter investment management, more effective borrower diligence, and more accurate portfolio pricing.
What Funders Typically Do — and Why It’s Not Working
1. The Blackbox Approach
“Let’s trust the numbers and average things out.”
This method assumes data quality without verification. At best, this leads to blind allocation; at worst, it leaves funders exposed to silent risk hidden within inflated portfolios. Without deeper analysis, non-performing cases go unnoticed until capital is already at risk.
2. The Manual Slog
“We’ll clean the data ourselves.”
Relying on analysts to manually normalize case data is time-consuming and difficult to scale. It introduces inconsistency, delays capital deployment, and diverts talent away from higher-value work. At the end of the day, you’re still combing through disconnected spreadsheets — trying to reconcile formats, decode vague notes, and estimate case quality — instead of making confident, data-driven decisions.
The Real Solution: Smart Data Infrastructure
To move beyond reactive cleanup and manual fixes, funders should consider building an operational structure that supports data precision, continuous oversight, and scalable decision-making. While every organization’s tools may differ, specific foundational capabilities matter.
1. Intelligent Data Normalization
Design processes that convert messy, unstructured case inputs into standardized formats — enabling reliable comparison across firms, jurisdictions, and claim types.
2. Borrower Data Quality Benchmarking
Track the consistency and completeness of submissions as a proxy for borrower reliability. Clean, well-structured data often reflects stronger case discipline and lower long-term risk.
3. Performance Gap Detection
Once data is normalized, funders can identify variations across borrower cohorts — enabling smarter pricing, capital allocation, and strategic prioritization.
4. Ongoing Data Diligence
Build workflows to proactively flag inconsistencies, missing milestones, or signs of dormancy. Even lightweight monitoring improves responsiveness and reduces exposure.
5. AI-Powered Intelligence
The rapid evolution of large language models (LLMs) and other AI models is reshaping how funders interact with case data. From parsing unstructured case data to surfacing anomalies, enriching due diligence, and assisting with underwriting, AI serves as a powerful intelligence layer on top of the data. It doesn’t replace human judgment — it augments it, enabling faster, more consistent, and more scalable decisions across the litigation finance workflow.
And, all these aren’t just software features — they’re structural considerations for any funder aiming to scale litigation finance operations with confidence.
So, want to chat about data hygiene?
If these data hygiene challenges sound familiar, we’d love to learn about your current approach. Schedule a conversation with the Bridgify team.