Thomas is the Founder of Fenaro, a modern fund management platform for litigation finance. He holds a law degree from Durham University and has spent his career designing and delivering large-scale, complex financial services platforms. Prior to founding Coremaker's Fenaro product, he spent much of his career at Accenture, PwC, and other management consulting firms, working with global banks, asset managers, and institutional investors.

Below is our LFJ Conversation with Thomas Bell:

The litigation finance industry has grown to over $16 billion, yet your blog notes that many firms still run on spreadsheets. What specific operational pain points do fund managers face with legacy systems, and how does Fenaro address them? 

Despite the industry’s rapid growth, most litigation funders continue to rely on in-house spreadsheets or fragmented systems that are not designed to handle litigation finance. From our conversations with funders across the market, three operational pain points consistently arise.

The first is calculation risk. Funders regularly tell us about issues arising from manual models and fragile data infrastructure, ranging from investor reports needing to be reissued due to errors (surprisingly common), to material budget discrepancies only being identified late in a case lifecycle (less common, but in some cases career-ending). Purpose-built fund management systems substantially reduce this risk by centralising data and automating complex calculations.

The second is friction with law firms after a deal has closed. One funder summarised their biggest challenge as “getting lawyers to think in IRR terms.” In practice, this reflects the lack of efficient processes for tracking budgets, agreeing drawdowns, reviewing invoices, and sharing case updates. Fenaro addresses this through the borrower portal, which provides law firms with a structured and transparent way to work with funders throughout the life of an investment, resulting in a much more positive relationship.

The third is operational complexity limiting scale. Many funders speak candidly about ambitious growth plans being constrained by manual reporting, bespoke processes, and operational bottlenecks. Without greater standardisation and automation, it is difficult to scale portfolios or support increased institutional participation. Fenaro is built from the ground up to reduce the overhead of operational processes, while giving funders all the key information required to focus on growing and managing the fund.

In mass tort strategies, some funders are managing regular updates across tens or even hundreds of thousands of individual claims and many different law firms. Several have told us that keeping this information accurate and up to date is a data nightmare that quickly becomes unmanageable, and worry they’re spending too much time on administrative data-wrangling efforts, and not enough time on actually mining the content for value.

Fenaro is designed to process high volumes of case data from multiple law firms in a consistent format. Updates can be submitted through borrower portals or uploaded directly, allowing funders to see the status, valuation, and history of every claim at a glance. As part of this process, the platform runs validation checks and identifies potential duplicate claimants, a serious and well-known issue in mass tort funding. The same validated data is then used to produce investor and internal reports without the need for manual reconciliation.

We frequently hear that fund visibility tends to drop sharply after capital is deployed. Monitoring budgets, drawdowns, and expenditure often relies on periodic reporting and manual review.

Fenaro provides funders with continuous visibility at both the portfolio and case level. At the same time, Fenaro gives law firms access to a free borrower portal. Contrary to the perception that firms resist new technology, many lawyers have told us directly that they are willing to adopt tools that reduce administrative burden and improve clarity. The borrower portal allows firms to track funding, compare spend against budgets, submit updates, and request additional capital, reducing friction on both sides and improving the overall relationship.

Funders often tell us that building and maintaining waterfall models in spreadsheets is one of the most time-consuming and error-prone parts of the investment process. Fenaro allows complex waterfalls to be configured in seconds, with multi-step logic based on capital return, interest, IRR, MOIC and other calculation types.

A flow view presents the waterfall logic in plain English, and scenario-modelling functionality allows users to test scenarios and explain outcomes to investment committees and LPs. Once a deal is live, waterfalls update automatically as cashflows occur, removing the need for repeated spreadsheet rework and reducing calculation risk.

Many funders we speak to see the biggest opportunities in greater standardisation, which would help unlock institutional capital and support a more liquid secondary market. There is still significant friction in how funders, law firms, investors, insurers, and brokers interact, and we’re tackling this one step at a time, focusing on the most pressing pain points first.

We are also frequently asked about AI and machine learning. Our view is that near-term value lies in decision support rather than decision replacement—particularly in reducing the time spent evaluating the majority of cases that are ultimately declined, while equipping underwriters with better information to make faster, more confident decisions. It will likely be some time before the technology is sufficient to take underwriting decisions on behalf of the funder, given the complexity and variation of the underlying legal cases, but things are moving quickly in the AI space so we continue to test and review various models as they evolve.

Rory is the CEO of Eperoto, championing the use of decision analysis to improve clarity around litigation and arbitration risk. Originally from New Zealand, he's worked within legal technology for decades, delivering innovative solutions to the top global firms, government, as well as specialist legal boutiques.

Below is our LFJ Conversation with Rory Kingan:

Eperoto’s approach emphasizes using lawyer judgment rather than AI or data-driven models. Why is that distinction important, and how does it build trust among lawyers, funders, and other stakeholders?

At Eperoto, we believe that lawyer judgement is the foundation of credible litigation and arbitration analysis. High-stakes disputes aren’t like consumer tech problems where large-scale historical data exists and small inaccuracies are insignificant. They're unique, context-dependent situations where experience and nuanced legal reasoning are irreplaceable. In most commercial cases, AI simply doesn’t have the training data or contextual nuance to make defensible predictions. Right now it also struggles with the complexity of jurisdictional variation and the role of precedent. No funder or sophisticated client should rely on a generic model to value a multi-million-dollar dispute.

Litigation and arbitration are inherently grey-zones. Outcomes turn not only on points of law, but also on credibility assessments, witness performance, tribunal psychology, and how fact narratives are perceived. These are areas where AI is weak and where judges and experts routinely disagree. Research across behavioural psychology and negotiation theory shows that human reasoning is still essential in these environments. Lawyers will often use an AI tool as a sounding board to explore different ideas and arguments, but ultimately they rely on their own judgement and reasoning to assess how different elements of the case are likely to unfold.

Eperoto is therefore built around a simple principle: Lawyers make the judgement; the platform helps them to structure and quantify it.

This distinction builds trust for three reasons:

1. It reflects how top practitioners already work. Clients retain leading counsel for their experience, intuition, and ability to form a reasoned opinion, not for machine-generated answers.
2. It avoids “false precision.” AI-driven confidence levels often create a misleading impression of certainty. Eperoto keeps the human experts in control.
3. It aligns with stakeholders’ expectations. Funders, insurers, GCs, CFOs and boards want a lawyer’s professional assessment, but expressed in a structured, decision-analytic way. Eperoto strengthens, rather than replaces, that judgment.

The result is a decision-analysis workflow that is transparent, explainable, and fully grounded in legal expertise. Precisely what stakeholders need to trust the numbers behind a funding or settlement decision.

When litigation funders assess potential cases, they often rely on intuition and experience. How does Eperoto help them quantify risk and likely outcomes in a way that strengthens those investment decisions?

Every litigation funder knows that a case is a contingent asset, and valuing that asset depends on understanding the likelihood of outcomes at trial or arbitration. Yet the process used to reach those views is usually unstructured, highly subjective, and difficult to defend when presented to an investment committee or external partner.

Eperoto addresses this by helping lawyers to apply decision-tree analysis. This is a method used for decades in energy, pharma, finance, and indeed litigation. Instead of relying purely on intuition, lawyers:

1. Map the key uncertainties. What issues drive liability? Likely quantum outcomes? How might damages be reduced? Where do procedural or evidentiary risks sit?
2. Assign probabilities grounded in legal judgment. No AI predictions: purely the lawyers’ professional view expressed clearly rather than implied.
3. Estimate costs & cost-shifting, interest, and any enforcement risk.

From this the tool calculates a visual quantitative risk report, showing funders the likely outcomes, expected value, downside scenarios, tail risk, and more.

This sort of analysis:

• makes an investment case more rigorous,
• dramatically improves internal and external defensibility, and
• surfaces insights impossible to see from narrative memos alone.

Funders, insurers, and counsel repeatedly tell us that this level of clarity is transformative. It sharpens decisionmaking, strengthens underwriting discipline, and improves alignment across stakeholders. Over time, a consistent, structured approach creates a more disciplined portfolio and generates a feedback loop that measurably improves investment decisions.

Clearer communication of risk and value benefits all stakeholders. What are the biggest barriers to achieving that clarity in practice?

The biggest barrier is language ambiguity. A typical merits opinion reads something like:

“It's most likely the defendant will be found liable for X, with only an outside chance the court will accept the argument Y. Damages could be as high as Z.”

Terms such as “very likely,” “little chance,” or “low risk” are interpreted wildly differently by different people, even among seasoned professionals. Research consistently shows a huge disparity in how people interpret such terms. For example "unlikely" can be interpreted as meaning anywhere from below 10% to over 40% likely to occur. Your investment decisions shouldn’t be subject to this margin of error just from internal communications.

A second barrier is complexity overload. Lawyers often present lengthy written analyses where different legal issues are explained sequentially:

“X might happen, but if not then Y. In that case Z will determine…”

Decision-makers are left to combine all these uncertainties mentally, plus litigation costs, insurance, interest, enforcement risks, appeal probabilities, and timing assumptions. Even highly experienced professionals can't intuitively do this correctly.

Eperoto solves these issues in three ways:

a) It forces clarity through quantification. “80% likelihood the contract is valid” is unambiguous, whereas “very likely” may be understood as 65% by one person and 95% by another.

b) It combines the factors automatically. No one needs to mentally integrate legal issues, damages pathways, costs, or conditional dependencies.

c) It presents the analysis visually. Charts and diagrams let stakeholders see the shape of the dispute, rather than reading dense text.

Together these remove unnecessary complexity, leaving stakeholders to focus on the true strategic questions rather than being stuck in ambiguous details.

Many lawyers hesitate to provide quantitative estimates because they fear being “wrong.” How do you encourage practitioners to engage with uncertainty in a more structured, transparent way?

This is a common concern, but it fundamentally misunderstands what quantification achieves. Providing estimates numerically doesn't remove uncertainty, it communicates it transparently. The alternative isn't "not being wrong"; it's being vague, which is far worse for the client or investor.

Sophisticated clients, funders, and boards understand that litigation outcomes are uncertain. What they want is clarity, not perfection. Yes, you should still make clear that a percentage estimate is not a promise; it is a transparent reflection of professional judgement, less ambiguous than vague adjectives. But once everyone accepts that, it allows for greater clarity and indeed honesty.

We encourage lawyers to adopt a mindset similar to experts in other industries:

• Quantification is not about being right; it’s about making uncertainty explicit.
• A structured model allows you to compare multiple scenarios, e.g. optimistic vs pessimistic or comparing different counsel’s assessments.
• Visual decision-trees help practitioners and clients see how different issues interact without needing to commit to one “correct” narrative.

Lawyers often find that once they begin using numeric estimates and decision trees, discussions with clients become easier, expectations align more quickly, and advice becomes more defensible. Many even rely on the visual component alone when presenting paths, strategy, and what truly drives the dispute.

How can tools like Eperoto help bridge the gap between legal reasoning and financial analysis, bringing dispute resolution closer to the standards of decision-making seen in other business contexts?

Business-critical decisions in energy, pharmaceuticals, and corporate strategy have used quantitative decision analysis for decades. A pharmaceutical company wouldn't greenlight a $50M clinical trial based on phrases like "good chance of success" or "strong scientific rationale". They'd model probabilities, conditional outcomes, and expected value. Yet litigation decisions involving similar amounts often rely on purely that kind of qualitative language.

The gap isn't from a lack of judgment. It's that legal reasoning and financial decision-making speak different languages. Lawyers think in terms of arguments, precedents, and likelihoods. Funders think in terms of expected values, downside risk, and portfolio returns. Eperoto translates between these worlds.

Here's a concrete example: A law firm presents a case with "strong liability prospects" and "substantial damages potential." The investment committee sees an attractive headline but struggles to assess the risk. Using Eperoto, counsel maps the decision tree and reveals that while liability looks good at 70%, the real value driver is a secondary issue: whether a contractual damages cap applies. If the cap doesn't apply, a 40% likelihood, it would triple the recovery. The investment thesis becomes clear: this isn't a simple 70% bet on liability; it's a case where the upside scenario creates most of the expected value. That fundamentally changes how you price the funding, structure the terms, and think about settlement strategy.

This kind of insight can easily be buried in a narrative memo but obvious when properly structured.

Specifically, Eperoto enables:

1. A common analytical framework - When counsel says "we have a strong case but quantum is uncertain," Eperoto forces that assessment into a structure funders recognize: probability-weighted scenarios with costs, timing, and enforcement risk factored in. This isn't dumbing down legal analysis; it's making it actionable.

2. Proper treatment of uncertainty - In portfolio management, no one expects point estimates: they expect distributions, scenarios, and sensitivity analysis. Eperoto brings that same rigor to litigation assets, showing not just expected value but the shape of the risk distribution. What's the 10th percentile outcome? How sensitive is the return to different assumptions? This is standard practice in all other asset classes.

3. Defensible investment decisions - Just as a PE firm documents the assumptions behind an acquisition, funding decisions should have the same analytical discipline. Eperoto creates an audit trail showing why a deal was approved or a settlement accepted, based on structured analysis rather than gut feel. Critical for investment committee scrutiny and stakeholder confidence.

4. Portfolio-level insights over time - Applying decision analysis consistently across a portfolio creates compounding benefits. Funders develop better calibration of their judgment, identify patterns of cases that outperform or underperform expectations, and build institutional knowledge about what drives value. Over time, this disciplined approach strengthens underwriting quality and improves portfolio returns. Just like how data-driven decision-making in other industries creates feedback loops that enhance performance.

The result is that litigation funding can be managed with the same analytical rigor as any other alternative asset class. Lawyers retain their essential role as expert judgment-makers, but that judgment gets expressed in a framework that investment committees can understand, stress-test, and defend to stakeholders.