By Steven Barge-Siever, Esq. CEO | Upward Risk Management May 23, 2025 The Legal Line Between Hype and Fraud Startups are told to “sell the vision.” But when that vision crosses into fabrication, it’s not just risky - it’s criminal. This week, the SEC charged Jeremy Jordan-Jones , CEO of Amalgam Capital Ventures , with securities fraud after allegedly soliciting a $500,000 investment  based on false statements in a pitch deck and diligence materials. The DOJ filed criminal charges the same day. The allegations included: A product that didn’t exist Revenue partnerships that weren’t real A bank account in the red Over $100,000 of investor funds  used for personal expenses These weren’t exaggerations. They were material misstatements - false claims likely to influence an investor’s decision. And that’s exactly what regulators care about. The Legal Standard: Material Misstatements vs. Optimism Under U.S. securities law, a material misstatement  is any false or misleading statement of fact that a reasonable investor would consider important when deciding whether to invest. In this case, those statements were made in a pitch deck and due diligence packet - the same types of materials routinely shared in early-stage fundraising. The SEC’s complaint alleges that: Amalgam’s blockchain platform did not exist The partnerships and revenue projections were fictitious The company’s financial condition was misstated Investor funds were misused almost immediately None of this occurred in public filings. These were private fundraising materials - meaning the case sends a clear signal: the SEC is scrutinizing investor communications at every stage of the startup lifecycle . Why This Matters for Startup D&O Insurance Many startup founders assume their D&O (Directors & Officers) policy will provide protection in the event of a lawsuit or investigation. But when litigation arises from investor communications, the scope of coverage becomes highly specific - and often limited. Key issues: Fraud exclusions : Most policies do not cover claims involving intentional misrepresentation, and final judgments of fraud can trigger clawback of defense costs. Misuse of funds : Conduct exclusions may apply when personal use of investor capital is alleged. Capital raise exposure : Not all startup D&O policies are structured to respond to claims tied to private placements or investor pitch materials. For venture-backed companies, startup D&O coverage must be built around real risks , including the potential for investor claims, regulatory investigations, and internal disputes. Without proper structuring, startup D&O policies may fail to respond in the exact scenarios founders assume they’re covered for - especially when the claim begins with what was said in a pitch. What Startups and Investors Should Take From This Case This wasn’t a high-profile IPO or a billion-dollar collapse. It was a private, early-stage investment - one of thousands that happen each year. But the legal framework is the same. Startups should: Treat investor-facing materials as legal documents, not just marketing content Ensure factual claims about products, finances, or partnerships are accurate and current Review D&O policies with counsel or a specialist to ensure they cover this category of exposure VCs and board members should: Understand how D&O coverage applies when misstatements come to light Encourage risk audits during or after capital raises Avoid assuming coverage exists simply because a policy is in place Our Perspective At Upward Risk Management, we specialize in aligning startup D&O insurance with real legal and regulatory risk - not theoretical checkboxes. We’ve seen how investor claims, enforcement actions, and internal disputes unfold in practice, and we design D&O and E&O coverage accordingly. Many founders assume their D&O policy will protect them in the event of litigation or regulatory scrutiny. But standard policies - especially startup D&O insurance issued at the early stage - often contain exclusions or structural limitations that leave founders and boards exposed. This case is a clear reminder: The materials you send to investors can, and will, be used in litigation. Coverage needs to be built with that reality in mind. 📩 If your D&O policy hasn’t been reviewed since your last round, we recommend doing so now. Contact us to ensure your insurance is designed to respond when investor communications are challenged. #StartupRisk #SEC #DandO #FounderLiability #MaterialMisstatement #InvestorLitigation #VentureCapital #RegulatoryRisk #InsuranceCoverage

And What to Do About Them Before the Claim Hits When GPs finally read their GPL policy, it’s often in the middle of a crisis - an SEC subpoena, a letter from an LP, or a board seat that just turned adversarial. The problem? GPL policies are designed to reduce insurance carrier exposure to the highest risk claims. This leaves them riddled with exclusions that only become obvious once it’s too late. In this guide, we break down the 10 most common GPL exclusions  we see in claim disputes, underwriting meetings, and postmortems - and how to fix them. Want a deeper dive into what GPL insurance covers? 1. LP Lawsuits Blocked by Insured vs. Insured Language In GPL The risk:  Some policies define limited partners (LPs) as insureds, which can invalidate coverage for LP-initiated claims - even when those claims allege fiduciary breach or fund mismanagement. What to do:  Carve back claims brought “in the capacity of an investor or limited partner.” This ensures LP disputes don’t get bounced on a technicality. 2. Regulatory Inquiries That Aren’t ‘Formal’ Enough The risk:  Many policies won’t respond to an SEC or DOJ inquiry unless it’s labeled a “formal investigation.” That means early subpoenas, requests for testimony, or even staff letters may not trigger coverage. What to do:  Ensure your policy includes pre-claim inquiries, subpoenas, and informal investigations  in the definition of “Claim” - and confirm Side A and C  respond without ambiguity. 3. Professional Services Exclusion That Voids E&O Coverage The risk:  Vague exclusions for “advisory” or “professional services” can be interpreted to exclude private investment activities - the core function of a GP. What to do:  Negotiate E&O language that affirmatively covers fund-level services , including oversight, capital deployment, and portfolio management. 4. Outside Capacity Gaps for Board or Dual-Hat Roles The risk:  If a manager is serving on a portfolio company board, or wearing dual hats (GP + advisor), coverage may be denied for acting “outside insured capacity.” What to do:  Define “insured capacity” to include all relevant roles, and confirm that ODL coverage  responds properly - especially when indemnification is unavailable. 5. Contractual Liability Exclusions That Bar Side Letter Claims The risk:  Many LP disputes stem from side letters, co-invest agreements, or allocation promises- all contractual in nature. Some policies exclude these claims entirely. What to do:  Carve back “liability that would have existed in the absence of a contract”  to keep side letter and LPA-based claims covered. 6. Prior Knowledge and Prior Acts Clauses That Are Too Broad The risk:  If any insured “knew or should have known” about a potential issue - no matter how informal or internal- coverage can be voided. What to do:  Narrow this language by clarifying what constitutes knowledge, requiring actual awareness , and tying exclusions to a specific prior acts date . 7. Bankruptcy Exclusions That Undermine ODL The risk:  Portfolio company insolvencies often lead to board-level litigation. Some GPL policies exclude claims brought in bankruptcy, rendering ODL  worthless when it’s needed most. What to do:  Ensure ODL coverage includes claims brought by creditors or trustees , and removes blanket bankruptcy exclusions tied to portco financial status. 8. Cyber and Privacy Exclusions That Eliminate Real Risk The risk:  If LP data is breached, or phishing leads to a wire fraud or disclosure issue, some policies exclude it outright under “cyber risk” language. What to do:  Carve back for network security, privacy liability, and data breaches  tied to fund operations - or purchase coordinated Cyber coverage with aligned definitions. 9. Shared Limits That Quietly Erode Fund Protection The risk:  GPL coverage is sometimes stacked within a shared tower alongside D&O or other lines. If a portco burns through the limits, there’s nothing left for the fund. What to do:   Separate GPL and D&O towers - or clearly disclose when limits are shared, and track burn rates during active litigation. 10. EPL Sublimits That Disappear Fast in Management Company Claims The risk:  Wrongful termination, harassment, and retaliation claims at the management company  often trigger the EPL portion of GPL. But it’s frequently limited to $250k or $500k. What to do:  Negotiate standalone EPL coverage or require a minimum $1M shared sublimit  with a clear defense allocation strategy. 📩 Want to Know If Your GPL Policy Has These Gaps? We audit GPL policies line-by-line, then show you exactly where your risk lies. Request a Fund-Level Policy Audit → Upward Risk Management Upward Risk Management was founded by a former claims attorney to bring sharper, litigation-aware insurance strategy to private equity and venture capital firms. We specialize in structuring fund-level coverage that holds up when it’s tested - whether by LPs, regulators, or collapsing portfolio companies. With deep expertise in policy language, exclusion traps, and negotiation strategy, we don’t just place coverage - we protect decision-makers. If you want a second opinion on your GPL program, we’ll audit it line by line. Learn more about GPL insurance and how it protects your fund

What AI Washing Means for Litigation, Risk, Insurance. And How Underwriters Are Getting Smarter By Steven Barge-Siever, Esq. Upward Risk Management LLC Almost a year ago, I spoke with an underwriter who had adopted an early-stage AI tool designed to streamline data collection for risk evaluation. It sounded promising - until they discovered the “automation” was actually being performed by offshore contractors, manually scraping and inputting the data. Fast forward nine months, I had a conversation with a broker using a platform marketed as an AI solution for policy placements and coverage reviews. But the turnaround time was slow. Suspiciously slow. The kind of delay you’d expect if a person - not a model - was doing the heavy lifting. The broker concluded the tech was likely powered by human labor disguised behind an AI-branded front end and that they were probably just looking for data. These aren’t isolated anecdotes - they’re early warning signs of a broader trend: AI washing , and its legal sibling, AI litigation . What Is AI Washing? AI washing refers to the exaggeration or misrepresentation of a company’s use of artificial intelligence  in products, services, or operations. It’s often used to attract capital, customers, or media coverage, and it's becoming a serious legal, regulatory, and insurance risk. Where greenwashing distorts environmental credentials, AI washing distorts technological credibility. And when that distortion shows up in investor decks, press releases, or SEC filings, it opens the door to AI litigation , securities fraud , and regulatory enforcement . Puffery or Material Misrepresentation? Startups live in a world where puffery is a survival strategy. Pitch decks are built on optimism. Founders are taught to “sell the vision,” sometimes well before the product is fully built. In places like San Francisco, statements like “We’re building world-class AI” or “This is the smartest platform on the market” aren’t just tolerated - they’re required. Investors reward bold claims over modest realism. But that kind of puffery doesn’t fully translate to New York, London, Delaware or Washington D.C. The startup playbook doesn’t hold up in a courtroom. In legal terms, puffery  refers to vague, promotional statements that no reasonable investor would rely on as fact . Courts generally excuse language like “cutting-edge,” “best-in-class,” or “industry-defining” as non-actionable sales talk. But AI misrepresentation  is different. When a company claims “Our platform is AI-powered” - and it turns out to be driven by human labor, simple scripts, or unmodified third-party tools - that’s not puffery. That’s a material misstatement , and in legal contexts - especially SEC filings or investor pitches - it becomes actionable fraud . This is where real exposure begins: Securities litigation Regulatory enforcement Insurance coverage denial It’s one thing to impress a seed-stage investor in Palo Alto. It’s another to explain the same claim to a regulator or judge in Delaware. For brokers, underwriters, and VC-backed founders, this distinction isn’t academic - it’s survival. Puffery may be harmless in the pitch. AI washing, by contrast, is prosecutable. Real AI Litigation: Enforcement and Lawsuits Are Already Here We’re no longer in hypothetical territory - AI litigation  is already triggering lawsuits, enforcement actions, and securities fraud charges: Presto Automation : Target of the SEC’s first formal AI washing enforcement action. The company allegedly misrepresented the AI capabilities of its voice product, which in reality relied heavily on off-the-shelf third-party tools. Nate (Albert Saniger) : Marketed as an AI-powered app that automated online purchases. In reality, transactions were executed manually by overseas workers. The founder was charged with securities fraud for misleading investors. Innodata, AppLovin, and Skyworks : Each faced securities litigation from investors alleging that the companies overstated their use and integration of AI, distorting market value and misleading shareholders. These cases are leading indicators of growing regulatory attention and plaintiff activity in the AI sector . Is There Insurance for AI Misrepresentation? It Depends. What Might Be Covered Securities class actions  alleging AI misrepresentation may trigger Directors & Officers (D&O) insurance  for legal defense and settlements, provided there's no final adjudication of fraud. Defense costs during SEC investigations  may be covered under sublimits, depending on policy wording. Where Coverage Breaks Down Fraud exclusions : If a court or regulator finds intentional deception, coverage can be voided. Prior knowledge : If executives knew the AI claims were false when applying for coverage, the insurer may walk away. Regulatory fines and penalties : Often sublimited or excluded completely For venture-backed startups, accurate AI disclosures aren’t just a legal risk - they’re a risk transfer issue . Missteps can void the very insurance meant to protect the leadership team. AI Underwriting Is Evolving, Fast Underwriters have already been misled by “AI tools” that were little more than front ends to human work. That experience is now shaping how insurers evaluate technology companies. How Can Underwriters Spot AI Washing AI Risk Flags: Signals of Misrepresentation in Startup Disclosures Red Flag What It Likely Indicates Vague references to “AI capabilities” with no architectural detail Absence of true ML infrastructure; likely reliance on deterministic logic or human input masked as automation Claims of “proprietary AI” with no IP filings, technical whitepapers, or codebase access White-labeled or third-party models rebranded as in-house innovation; elevated risk of IP misrepresentation Operational delays inconsistent with claimed automation Underlying processes are manual or pseudo-automated; potential breach of SLA or deceptive product claims Overreliance on buzzwords like “LLM,” “neural net,” or “deep learning” without system-level specificity Superficial understanding of ML; marketing-driven narratives unsupported by technical substance or deployment rigor Smarter Underwriting Questions for AI Risk Evaluation What foundational model or architecture underpins your system (e.g., LLaMA, GPT-4, Mistral)? (Follow-up: Is it open-source, licensed, or internally developed?) Who performed the model training or fine-tuning, and on what data? (Look for in-house vs. outsourced, use of proprietary vs. scraped vs. synthetic datasets, and data governance controls.) Which tasks are fully autonomous, and which involve human intervention or post-processing? (You’re looking for clarity on decision boundaries, confidence thresholds, and fallback protocols.) Do you manage inference on your own infrastructure, or are you dependent on third-party APIs like OpenAI, Anthropic, or Cohere? (This affects latency, security posture, and contractual control over your core functionality.) How do you monitor for model drift, hallucinations, or system degradation over time (Absence of a model monitoring strategy is a serious technical and operational liability.) If the answers are evasive, vague, or overly polished - price the risk accordingly. Model Architecture Red Flags (What Founders Say vs. What Underwriters Hear) As underwriters probe more deeply into AI operations, these three claims are increasingly scrutinized: “We built our own LLM.” Probably not. Or at least without multi-million-dollar compute access and deep ML talent. Often a red flag for exaggeration unless accompanied by technical proof (training logs, architecture specs, compute documentation). “We fine-tuned an open-source model.” Feasible (and something I've been building), but triggers questions about model choice (e.g., Mistral, LLaMA, DeepSeek), training data provenance, data handling practices, and post-deployment safeguards (e.g., hallucination filters, PII obfuscation). “We use GPT/Claude via API.” Perfectly valid - but only if disclosed. Risk escalates when companies rebrand hosted inference as “proprietary AI,” omit dependency disclosures, or lack security protocols around sensitive data transmission. Where the First AI Lawsuits Will Hit Startups Litigation won’t wait for IPO. Enterprise buyers  may sue over breach of contract if the AI solution is really human-powered. Investors  may sue under Rule 10b-5 when they realize valuations were built on inflated claims. Regulators  (SEC, FTC) may investigate companies for deceptive trade practices, especially those with consumer exposure. These claims are already surfacing at Series A and B - not just in the public markets. Evidence of AI misrepresentation at early stages: In 2024, the SEC charged the founder of For example: Joonko, a Series B AI hiring startup, with fraud for exaggerating its AI capabilities and customer base. The company had raised $38M before collapsing. The Acceleration of AI Litigation: What’s Next AI litigation is poised to accelerate - driven by overstated capabilities , opaque disclosures , and a flood of startups racing to capitalize on hype . Regulators are watching. Plaintiff firms are circling. And underwriters are starting to adapt. At URM, we expect this pressure to grow - not just for public companies, but for any startup leaning into AI as a core feature. That includes: SaaS tools claiming automated decision-making Fintechs promoting AI-driven underwriting or lending HR tech and recruiting platforms using AI for hiring or compensation decisions Cyber companies offering AI threat detection If your company is making AI claims, you need to know what’s covered - and what won’t be. Conclusion: Insurance Protects the Truth, Not the Hype AI is revolutionizing risk - and AI litigation is redefining it . Founders must understand that the language used to raise capital is now subject to scrutiny from regulators, insurers, and courts.Brokers must prepare clients for the legal and coverage fallout of AI misrepresentation.Underwriters must dig deeper than pitch decks - and adjust pricing, limits, and exclusions accordingly. Because in this next wave, insurance doesn’t protect the promise of innovation. It protects the consequences of exaggeration. About Upward Risk Management (URM) Upward Risk Management is a specialist insurance advisory firm built for the modern risk economy. We advise venture-backed companies, PE and VC funds, brokers, and underwriters on risk strategy, insurance structuring and securing coverage in high-complexity areas - especially where AI, automation, and regulatory exposure converge. Whether reviewing Tech E&O, D&O, or bespoke policy language, URM delivers clarity in chaos. We translate legal risk into insurance strategy - and expose where traditional coverage falls short in the face of emerging technology. We don’t insure buzzwords. We insure what's real. Learn more at www.upwardriskmanagement.com

By Steven Barge-Siever, Esq. Example: How Plaintiff Attorneys Build an AI Litigation Case Seasoned plaintiff attorneys lay out how litigation will likely take shape as AI enters high-stakes decisions, and why early-stage companies should take notice before plaintiffs do. Example AI Litigation vs. Classic Plaintiff Malpractice I’ve done this before - not with AI, but with buildings, surgeries, and drug labels. I’ve sued developers who blamed subcontractors. I’ve cross-examined doctors who swore the outcome wasn’t their fault. Every industry assumes its complexity will protect it. But in a courtroom, complexity doesn’t protect you - it isolates you. When juries don’t understand how a system works, they look for someone to hold accountable. And the more opaque the system, the more they want a human face to blame. Add to that a headline number - like a $100 million valuation - and they don’t think “runway” or “Series B.” They think you’re a cash cow. To most jurors, $100 million might as well be infinite. It shifts the emotional posture of the case. You’re no longer a scrappy startup. You’re a company that can afford to do better. So when I look at a Series A or B tech company using AI  to make credit decisions, offer dynamic pricing, or flag user risk, I see a familiar pattern emerging. If litigation takes off in this space - and I believe it will - here’s what I expect it to look like. Step 1: AI Litigation Starts with the Human Story Plaintiff attorneys typically begin with a narrative that feels intuitively unfair - because that’s what resonates with juries. Maybe a borrower is denied a loan. Same income, same region, but a different outcome than someone else. Different demographic profile. Or maybe a user is flagged as suspicious - they clicked through too quickly, or their IP address changed. They don’t know why they were blocked. They just know they were. Attorneys can make a fairly confident assumption that the company hasn’t fully documented or audited how that decision was made - and that gives them space to frame the harm not as bad luck, but as a systemic failure buried in automation . Attorneys looking at these cases today will often point to Mobley v. Workday  as a sign of where courts are heading. In that case, the court didn’t just allow the lawsuit to proceed against the employer  who used the AI-powered hiring tool - it also allowed claims to move forward against Workday itself , the AI vendor . “You built the system. You enabled the outcome. You share responsibility.” That’s the shift. Courts are beginning to recognize AI tools as real decision-makers - and they’re willing to treat both the deployers and the creators as accountable. If they’re litigating against an AI fintech, they’ll cite Mobley  to show this reasoning is already gaining traction. Step 2: Follow the Promises Next, plaintiffs will examine the company’s public-facing materials - the website, onboarding language, and investor decks. If they find statements like: “Bias-free lending”“Democratizing financial access” “Objective, automated decisions” Those statements become commitments. And any divergence from them becomes a liability. That gap between what’s promised and what’s delivered supports claims like: Misrepresentation Unfair or deceptive business practices Negligent oversight or design The case doesn’t require technical failure. Just inconsistency that creates the appearance of harm. Step 3: Push for Discovery To move forward, plaintiff attorneys file claims that are likely to survive the initial stages - just enough to unlock discovery. That’s where they start building leverage. They look for: Slack messages about flagged users or product changes A/B test results showing unequal outcomes Lack of model audits or explainability standards Vendor relationships that shift liability but not accountability Fast-moving startups are rarely buttoned up at this stage. And that’s not unethical - it’s just operational reality. But in litigation, missing documentation often reads as missing diligence . Step 4: Break the Firewall Most companies will try to position themselves as neutral facilitators: “The AI made the decision.” “That was a third-party tool.” “We just show the result.” But attorneys have seen these firewalls before in healthcare, construction, and product liability. Courts don't often buy it. The typical argument could be: The company profited from the outcome It had the power to test or question the tool It positioned the system as trustworthy or unbiased That’s often enough for a theory of shared liability , or negligent oversight  - especially in the eyes of a jury. Step 5: Pressure the Policy Limits This is where things turn strategic. If the company has $1M in D&O , that’s not protection - that’s a legal budget. It’s often just enough to retain outside counsel, get through early motions, and respond to discovery. And it’s usually less than it looks.  Most startup policies include sublimits  that quietly cap: Regulatory defense at $250K Employment-related claims at $100K Nothing at all for third-party bias or consumer allegations unless specifically endorsed So while founders may think, “We’ve got $1M in D&O,”  what they actually have is a fragmented policy - barely enough to defend , and often nothing left to settle. From a plaintiff attorney’s perspective, real settlement leverage starts at $3M to $5M+ in total limits  across D&O, Tech E&O, and excess. That’s when they know there’s room to negotiate without bankrupting the company or risking board resignations. But here's the trick most founders miss: Attorneys know how to construct claims that blur the line between what’s covered and what’s not. They’ll include allegations that trigger the policy - while weaving in uncovered elements (like discrimination, regulatory theories, or intentional acts) that: Create conflict between the company and the carrier Force the company to consider partial denials Introduce the risk of personal liability for executives That’s where the real pressure builds - not in court, but in the boardroom and the carrier’s claims department . And when a company has a $100M+ valuation , that perception becomes fuel. Jurors don’t think in terms of burn rate or future capital needs. They assume a company worth that much can (and should) afford to pay. So while the startup is calculating runway, the plaintiff is calculating the exact moment when the board will walk away with a policy-limit settlement  just to make it all stop. Because even if the claims are weak or the facts are gray, once legal costs spike and reputational risk surfaces, settling within the insurance tower becomes the path of least resistance. But Is This Actually Happening Yet? It's not making headlines, but it’s coming. Right now, most plaintiff attorneys are still watching and waiting. The companies are early-stage. The claims are complex. And there aren’t enough public rulings yet to support a full wave. But once companies reach Series B or C , and more outcomes become public, lawsuits will follow. The claims will get simpler. Patterns will emerge. And eventually, the first few cases will become templates. That’s when the second-tier firms get involved - not the elite litigators, but the high-volume operators  who thrive on filing dozens of similar cases at once. It happened with wage-and-hour law. It happened with data privacy. It’s only a matter of time in tech and AI. What They’ll Look For These early cases won’t require a perfect set of facts. They’ll only need: Rejected borrower(s) without clear explanation Flagged user(s) who looks like an edge case A marketing claim that doesn’t line up with internal controls From there, attorneys can apply pressure through regulators, the press, and settlement strategy. The goal is not typically trial. It’s leverage. What Founders Should Take From This None of this requires bad intent. In fact, the real risk is building fast, automating early, and trusting a model that no one inside the company can fully explain. That’s what makes these cases appealing - not because they’re egregious, but because they’re gray areas that will play badly in court . And once you're in litigation, the language of “good faith” gets overpowered by the language of outcomes. If You’re Building in This Space Start documenting early. Know what your model is doing and who it might disadvantage. Don’t settle for insurance that’s “standard” for startups. It won’t hold up under real pressure. Because once litigation reaches your doorstep, preparation is the only thing you can’t buy retroactively. What URM and Undr AI Do Differently At URM , we don’t just shepherd quotes - we engineer insurance around litigation strategy. We work with AI and fintech companies who know that standard coverage won’t hold up when the claims get serious - especially when you’re scaling, onboarding users, or preparing for your next round. That means: Audit-ready insurance architecture  that anticipates where coverage breaks: sublimits, exclusions, and gray areas. Custom D&O and Tech E&O towers  aligned with real-world exposure - not back-of-the-envelope benchmarks. Litigation-informed coverage  based on how attorneys actually structure claims - not what underwriters assume is low risk. And behind it all is Undr AI  - our proprietary risk intelligence system that helps us analyze exposure, benchmark peer companies, and forecast how litigation could unfold against your product. Undr AI turns legal theory into actionable underwriting insight, and URM builds your coverage around it. Because once a claim hits your inbox, it’s too late to rethink your insurance. And when the lawsuit comes from someone who knows how to engineer pressure, your best defense is having already anticipated the strategy. Upward Risk Management LLC Where Litigation Strategy Meets Insurance Design. Steven Barge-Siever, Esq. Founder | CEO steve@upwardriskmanagement.com www.upwardriskmanagement.com

By Steven Barge-Siever, Esq. In the world of venture-backed startups, failures aren’t rare. But what happened to Get Together Inc. (operating as IRL) wasn’t a typical collapse. It was a collision of founder misconduct, investor pressure, board-level control, and regulatory scrutiny - all of which converged into one of the most high-stakes D&O cases in recent memory. This wasn’t a matter of one party clearly at fault. It was an alleged failure across multiple fronts. And if you're a venture capital firm, a board member, or a founder in today’s market, it's the kind of case you need to study closely - not just for its drama, but how dual fiduciary roles can turn from boardroom formality to personal liability, how to protect yourself and where insurance is designed to fail. The Collapse of IRL: Dual Fiduciary Liability and A Multi-Front Litigation Storm IRL, short for "In Real Life," was a social app launched to help users discover and coordinate real-world events with friends - promising to counter digital isolation by promoting real human connection. The company attracted over $170 million in funding from prominent VC firms including SoftBank, Goodwater Capital, and Floodgate, positioning itself as a breakout contender in the post-Facebook social platform era. But behind the scenes, that growth was allegedly propped up by paid downloads and inflated user metrics. The app's claims of 12 million users were called into question, culminating in a public admission - possibly orchestrated by new leadership - that 95% of accounts were fake or bots. In July 2024, the SEC charged IRL’s founder and former CEO, Abraham Shafi, with defrauding investors. The complaint alleges that Shafi misled investors about user growth, routed advertising payments through third parties to conceal spending, and used company credit cards for personal expenses. These are serious allegations, and if proven, they would invalidate most D&O coverage via standard fraud exclusions. But just as that regulatory storm was brewing, a different kind of lawsuit emerged. In March 2025, the Delaware Court of Chancery allowed a separate suit to proceed against three major IRL backers: Goodwater Capital, SoftBank, and Floodgate. That lawsuit doesn’t accuse them of fraud. Instead, it alleges that their board representatives breached fiduciary duties by orchestrating a shutdown that benefitted preferred shareholders (including their own funds) at the expense of common shareholders. The claims include: Breach of fiduciary duty Tortious interference Improper removal of the founder Bylaw violations and voting control abuse The court allowed most claims to proceed, citing credible allegations that the VC board members prioritized investor recovery over the company's best interest. The Dual Fiduciary Duty Dilemma Venture firms often install partners or principals on the boards of portfolio companies. That makes sense. But it also creates a structural tension: these directors owe duties both to the company and to their fund. That’s fine in periods of growth. But in distress? Dual fiduciary duties often become incompatible. In IRL, the plaintiffs allege that VC directors prioritized their funds by forcing a shutdown, declaring most users were bots (a statement they claim was knowingly false), and redirecting remaining cash to preferred holders. Whether or not those claims prove true, the case highlights how easily governance decisions can turn into litigation. And it raises a critical question: when these roles conflict, which insurance policy responds? Which Policies Are Triggered? In a situation like IRL, there isn’t one policy in play - there are several, each with its own terms, exclusions, and priorities. Each party may be relying on different (or the same) towers of coverage: The company’s D&O policy , likely purchased by IRL itself The founder’s access to Side A coverage  under that policy VC directors’ reliance on Side B or Side A coverage  through the company Fund-level D&O/E&O policies  at Goodwater, SoftBank, and Floodgate Any tail policies  in place post-termination Understanding which of these policies applies, and how they interact, is key. For the Founder: D&O Insurance (Side A coverage)  may have initially responded to cover regulatory defense costs against the SEC charges. However, the SEC’s allegations of fraud and personal enrichment likely trigger the personal conduct and fraud exclusions , which may allow the insurer to deny or rescind coverage if those claims are proven. If Shafi used corporate funds for personal expenses, as the SEC claims, that would be grounds for denial under most D&O policies. That means he might have initially had coverage, only to lose it retroactively once conduct is adjudicated. For the Company: Entity coverage (Side C)  might be triggered in the event of securities-related litigation (e.g., by investors or regulators), but the SEC’s involvement typically falls under regulatory exclusions or regulatory defense sublimits , often capped at $250K–$500K. If IRL faced any private shareholder derivative suits or parallel claims, Side C could help, but would likely be limited by allocation disputes. For the VCs and Their Directors: Named directors (Chi-Hua Chien, Serena Dayal, Mike Maples) may have initially expected indemnification from the company , triggering Side B coverage  under IRL’s D&O. However, if the company became insolvent or conflicted out (unable to fairly indemnify directors in a suit about their own conduct), directors would need to rely on Side A coverage , which could be limited or contested. If company-level coverage is exhausted, they may turn to fund-level D&O or E&O policies , which often don’t cleanly cover portfolio board seats unless specifically endorsed. Even if those policies respond, questions around other insurance (IRL's), excess layers, allocation, and priority of payments arise quickly. These overlapping layers raise practical questions: Who notifies the insurer? Who controls defense? Whose policy pays first? This complexity is why litigation over the coverage itself  is not uncommon. The Most Common Gaps in Complex D&O Claims Regulatory Sublimits : In a case like IRL's SEC complaint, coverage for regulatory investigations is often limited to a small sublimit - typically between $250K–$500K. That may sound reasonable, until you realize that federal securities defense often burns through that in a matter of weeks to months. Indeed partner rates for regulatory attorneys are hover around $2,000/hr. If the company or individual expects full defense under a $5M tower, they’ll be surprised to find most of it unavailable once the sublimit kicks in. Duty to Defend - can IRL even select their own counsel? If this is a standard, off the shelf policy, then IRL probably cannot select their counsel, or will have rates capped at around $350 - $500 / hour. This is especially common when working with digital insurance brokers or insuers where coverage was not negotiated. Insured vs. Insured Exclusion : Most D&O policies exclude lawsuits brought by one insured person against another. This becomes a critical issue when a former executive - like Shafi - is suing current directors. Without a proper carve-back for derivative claims, whistleblower actions, or suits brought after a change in control, coverage may not respond. Derivative Demand Coverage : Many policies omit or underwrite poorly the costs of investigating and responding to a shareholder demand. If IRL’s common shareholders had issued a demand before filing suit, and the policy lacked this coverage, board members could be left paying out-of-pocket for early-stage legal advice and governance review. Personal Conduct Exclusion : Nearly all D&O policies exclude coverage for fraud, criminal conduct, or personal profit - but only after final adjudication.  If Shafi is ultimately found liable for intentional misconduct, his insurer can seek to claw back previously paid defense costs. Until that ruling, they may still advance funds (unless rescinded). Policy Allocation Disputes : When multiple parties are sued - like a founder, VC-appointed directors, and the entity itself - there’s often no agreement about how defense costs or settlement funds should be allocated between them. Carriers may delay or underpay while pushing responsibility across other policies, creating costly delays in reimbursement. What Sophisticated Firms and Boards Should Do Audit Coverage Structures Across Portfolio Companies : Ensure policies clearly address dual fiduciary roles, have Side A-DIC capacity, and include proper carve-backs for shareholder suits. Review Fund-Level Coverage : Don’t assume company-level D&O will protect your board seats. Your own fund’s D&O/E&O policy must be structured to catch spillover. Scrutinize Sublimits and Definitions : Words like "claim," "loss," and "insured" are where most disputes begin. Plan for Multi-Carrier Coordination : If your director sits on five boards, each with its own insurer, you need someone coordinating response before the lawsuit arrives. Upward Risk Management We founded Upward Risk Management because most insurance programs in venture aren’t built to withstand real conflict. We bring public company and private equity experience to the venture and growth stage - translating governance complexity, litigation trends, and investor dynamics into tailored insurance strategies that actually perform under pressure. We operate as your outsourced risk department. That means you get structured, negotiated, board-ready insurance coverage - without adding friction to your team. By the time most firms reach their Series B, the stakes are too high for boilerplate policies. That’s where we come in. Because in venture capital, your influence is your liability. And in complex claims, the first thing challenged is your coverage.

Understanding Private Equity and Venture Capital Portfolio Programs Executive Summary Over the past two decades advising private equity and venture capital firms, I’ve seen insurance evolve from a compliance task to a true strategic lever. When executed well, it protects board members, streamlines operations, and unlocks real savings. When handled poorly, it creates hidden risk, wasted spend, and reputational exposure. If your firm manages five or more portfolio companies , you already have enough leverage to implement a portfolio insurance fund program  that reduces risk, consolidates cost, and gives you clarity across the board. The only barrier has been execution. That’s why I founded Upward Risk Management , and why we built Undr AI : to eliminate the friction that made portfolio insurance strategies impractical - until now. Why Portfolio Fund Insurance Matters for PE and VC Firms A portfolio insurance strategy consolidates procurement, review, and renewal  across your investments. Done right, it transforms insurance from an operational burden into a strategic advantage. Economies of Scale By combining premium volume across portfolio companies, you gain real pricing power. We consistently see 20–40% savings versus standalone placements - plus better terms and stronger leverage at claim time. Standardized, Board-Protective Coverage Inconsistent D&O terms, silent exclusions, or missing EPL coverage expose board members and GPs to unnecessary risk. We structure Side A/B/C D&O, EPL, and Crime  coverage with best-in-class terms across the portfolio. Central Oversight Without Extra Admin With portfolio-wide monitoring , you can instantly track renewals, spot coverage gaps, and benchmark risk posture- without adding burden to CFOs or internal teams. Why Most Portfolio Fund Insurance Strategies Fail Despite the clear benefits, most firms never get a program off the ground. Why? Manual, repetitive application processes Dozens (or hundreds) of PDF policies No system for tracking limits, renewals, or policy language Fragmented broker relationships No bandwidth to manage it internally Legacy brokers  lack technology. Insurtechs  lack the expertise. URM was built to provide both. The URM + Undr AI - Fund Insurance Advantage At Upward Risk Management, we pair expert brokerage services with purpose-built AI tools designed to handle the complexity of portfolio insurance. Prefilled Applications We scrape structured data and reuse prior submissions to complete 70 - 90%  of each application - cutting down CFO input and speeding up quote turnaround. AI-Powered Policy Review Our platform reads policies like an underwriter to: Identify hidden exclusions  and sublimits Benchmark terms against industry best-in-class language Flag outdated or missing protections Real-Time Portfolio Dashboard Our clients get instant visibility across all companies: Track upcoming renewals Flag missing or expired policies Monitor coverage limits and carrier appetite Identify at-risk companies before claims happen Even if you don’t move to a full portfolio structure, this visibility alone creates clarity and control. Tailored Approach: Private Equity vs. Venture Capital We don’t take a one-size-fits-all approach. Our team has deep experience supporting both PE and VC investors, and we tailor execution accordingly. Investor Type Strategy Private Equity With management control, PE firms can enforce portfolio-wide mandates. We provide the framework and do the heavy lifting. Venture Capital VCs typically influence (not control) insurance decisions. We support portfolio companies directly with fast quoting, prefilled apps, and benchmarked policy options. In both models, the investor becomes a value-add channel - delivering trusted insurance placement to their companies. Final Word: Execute Without the Friction If you manage five or more companies, you qualify for a portfolio strategy today. The only question is whether you have the right partner to execute it - without noise or added admin . As a lawyer and broker, I built URM to deliver what legacy firms can’t - and what insurtech doesn’t understand. Upward Risk Management When expertise isn't optional.

What Mobley v. Workday  reveals about product design, legal risk, and the urgent need for specific AI insurance coverage By Steven Barge-Siever, Esq. When Mobley v. Workday  survived a motion to dismiss this year, headlines focused on a narrow point: AI vendors might be directly liable for discriminatory hiring outcomes under federal civil rights law. That alone was historic. But the implications are bigger. This case exposes a fundamental problem that exists far beyond employment discrimination: AI systems are being deployed into high-stakes, real-world contexts without a corresponding understanding of how legal, social, and operational risk actually manifests.  The people building these systems are often far removed from the end results, until a lawsuit drags them in. The Problem Isn’t Just AI Bias Risk - It’s Blind Spots We talk a lot about AI “bias.” But bias is only one category of legal risk. What Mobley  shows is that AI systems, and especially enterprise-grade tools sold to other businesses, can be legally and ethically problematic even when they are functioning as designed. Why? Because they’re usually built in technical or product-centric environments where the focus is on functionality, not liability. Vendors optimize for efficiency, throughput, automation. Clients optimize for ease of use. No one is optimizing for legal defensibility or downstream harm until it’s already happened. That’s how we get tools that: Automate decisions but obscure accountability Filter applicants using proxies that correlate with protected traits Score individuals or organizations using opaque logic no one can fully explain And then , when a claim arises (discrimination, regulatory overreach, or negligent deployment) the attorneys arrive. Not the engineers. Not the product managers. The lawyers. And the narrative changes from performance to blame. What Mobley  Actually Signals for AI-Centered Litigation Risk In Mobley , the plaintiff alleged that Workday’s hiring software played a gatekeeping role in rejecting him based on race, age, and disability. But what matters more than the allegations is how the court framed the vendor’s role . Workday wasn’t just a toolmaker. It was plausibly an agent  - a party that shaped employment decisions on behalf of its clients. That theory opens the door to direct liability - not only for AI in HR, but for any AI system that exercises functional control over regulated decisions. This applies not just to hiring, but also: Credit scoring tools  used by fintechs Underwriting engines  used by insurers Patient triage models  used by healthtech platforms Moderation systems  used by social platforms In each case, the vendor often claims: we don’t make decisions, our clients do.   But courts may soon say: if your product meaningfully replaces human judgment, you're in the decision loop, and on the liability hook. The Design Gap: What Happens When Legal Risk Isn’t a Feature of Your AI? AI products aren’t typically designed by lawyers. In most companies, legal gets looped in late. Sometimes post-launch. Often post-incident. That delay is a design flaw. If your AI product touches regulated activities (employment, finance, healthcare, housing) you need to build legal foresight into  the product. Not as a compliance checklist. As a core design principle : How does this system track and document decision logic? Who owns the risk of errors or disparate impact? Can a regulator or judge understand how this model arrived at an outcome? Will a court view your tool as a neutral platform - or an agent making real-world calls? Right now, too many AI products answer these questions after  things go wrong. That is the dynamic Mobley  is warning us about. The Shift Ahead: From Tech-First to Accountability-First The early AI boom was driven by scale, speed, and novelty. But the next phase will be shaped by legal structure, public trust, and traceable decision logic . This won’t slow innovation. It will separate serious builders from everyone else . Vendors who train their models on quality data, and show their work, will win larger, more sophisticated clients. The kind with attorneys on deck. Systems with clear audit trails and explainability features will become default. Legal, compliance, and product design will stop being silos. In short: the AI companies that think like regulated companies will survive as AI becomes regulated. Final Thought: Mobley Was the Warning Shot If you’re an AI vendor, Mobley  isn’t just a headline - it’s a preview of how courts, regulators, and plaintiffs will view your role going forward. And if you’re building AI that faces the world, and not just internal workflows, you need to assume that your product is going to end up in court someday. The only question is: Will your system be defensible or just defensively built after the fact? About Upward Risk Management At Upward Risk Management, we don’t just broker AI coverage - we helped shape it. Our founder is a former insurance attorney who has drafted AI-specific endorsements, advised on claim strategies, and placed coverage for some of the most complex AI-driven platforms in fintech, SaaS, and enterprise tech. We work directly with underwriters, legal counsel, and founders to ensure your insurance program actually matches how your AI operates - and how it will be scrutinized in court. When AI risk becomes real, URM is already there.

By URM | Upward Risk Management Executive Summary The financial services industry is undergoing a transformation. Human-led lending processes are being replaced (or augmented) by software, automation, and artificial intelligence. With that change comes a growing wave of legal exposure that most companies aren't insured for, and many barely understand. This white paper explores the evolving world of lender liability , how it intersects with LenderTech platforms , and why traditional insurance structures fail to respond. Whether you are a fintech company, digital lender, or bank relying on third-party technology, you need to understand how these risks develop - and how to ensure your insurance protects you. At URM, we combine legal, technical, and underwriting expertise to map these risks, model their financial impact, and structure policies that actually respond. Our CEO, Steven Barge-Siever, is an attorney, former general counsel, and leader in both traditional brokerage firms and insurtech startups. He’s led this transformation from every angle: the law, the boardroom, the claim, and the product design meeting. We built Undr AI to close the coverage gap - and make insurance work the way it should. From Bankers to Code: How Lender Liability Has Evolved Traditionally, the loan process was human. Loan officers handled intake. Underwriters made decisions. Compliance teams reviewed disclosures. Everyone understood their roles and liabilities. Today, technology handles these same processes - introducing LenderTech  platforms and algorithmic decisioning into the lending stack. But the liability hasn’t gone away. It’s just (partially) shifted. The traditional loan lifecycle included: Marketing and intake Application review Human underwriting Disclosure generation Funding and servicing Collections or disputes Now? LenderTech platforms automatically evaluate creditworthiness UX-driven disclosures update dynamically APIs and integrations handle payments and data AI flags fraud or recommends declines These automated processes still expose companies to lender liability - especially when errors cause consumer harm. What Creates Lender Liability in a LenderTech World? Borrowers don’t care whether they’re dealing with a human or software - they care that their rights were violated. Courts and regulators define liability by function , not form. Ask: Did your platform determine loan approval? Did you display or generate loan terms? Did your product participate in servicing, repayments, or collections? If yes, your LenderTech platform may be considered a functional lender under law - even if a partner bank funds the loans. Common Lender Liability triggers include: UX errors that misstate interest rates Automated denials that disproportionately affect protected groups Systemic failure to issue required disclosures Misleading advertising or marketing representations Advertising and the Expanding Front of Enforcement One of the clearest signals of regulator intent came in January 2025 , when the CFPB fined Wise, a global fintech remittance platform, nearly $2.5 million  for deceptive marketing practices. Wise had promoted its international transfers as faster and cheaper than competitors, but failed to accurately disclose fees and exchange rates. The CFPB found that these advertising misrepresentations gave Wise an unfair market advantage - and harmed consumers. This action wasn’t about backend software failures. It was about messaging, user experience, and the promises made on landing pages, in app flows, and through marketing emails . Wise was also penalized for failing to properly refund fees when payments were delayed. It’s a stark reminder that marketing risk and operational risk are often inseparable in LenderTech. If your platform makes promises - about speed, ease, cost, or access - those promises must be accurate. And your insurance must be structured to defend you if a regulator or plaintiff attorney claims they weren’t. The CFPB and other regulators are now treating marketing and advertising  as part of the lending lifecycle. This is especially dangerous for LenderTech companies, who may: Advertise approval likelihood based on soft data Promote “instant approval” or “zero fees” while burying critical terms Use personalized offers that misrepresent eligibility Misrepresentations in ads, emails, landing pages, or app flows  can trigger enforcement even before a loan is issued. Example 1: A recent CFPB action targeted a digital lending platform for promoting fast access to funds - when delays were common and systemic. Example 2: Another platform was scrutinized for marketing optional tips and donations as charitable or user-driven, when in fact they were used to support operating costs. Regulators claimed these practices constituted unfair and deceptive marketing, and that the companies misled borrowers regarding true borrowing costs and terms. If your Tech E&O only covers operational issues, but not the promises made in your product marketing, you may be completely uninsured when these claims arise. LenderTech Risk Profiles: Who’s Liable for What? There are two common configurations in the LenderTech ecosystem: 1. Technology Performing Lending Functions You control decisioning, disclosures, servicing If something goes wrong, you're directly liable 2. Technology Powering Bank-Led Lending The bank is the named lender But if your tech causes borrower harm, you’ll still get pulled into lawsuits or investigations In both cases, lender liability is real, but for different reasons. In scenario 1, you are the lender  in practice In scenario 2, you’re the cause of the harm Banks rarely build their own software. When something fails, they look to recover from their LenderTech partners . What Happens When a Claim Hits? Stage 1: Regulatory Inquiry You receive a CID or investigative letter Outside counsel is brought in Legal spend begins - insurance likely doesn’t respond yet Stage 2: Public Enforcement or Consent Order Penalties, monitoring, and reputational harm Your name appears in CFPB enforcement actions Private litigators begin circling Stage 3: Class Action or Third-Party Litigation Borrowers file suit Partner banks claim breach Insurance matters - if you have the right coverage Most LenderTechs assume Tech E&O will protect them. It often won’t. If the lawsuit involves APR errors, discrimination, misleading advertising, or disclosure failures, it may fall into the Lender Liability Exclusion (or absense of coverage). The Coverage Intersection: Tech E&O, Lender Liability, and D&O Tech E&O  - Covers software errors, like bugs or outages Lender Liability  - Covers financial harm from lending conduct D&O  - Covers governance-related lawsuits against executives Most companies in the LenderTech space need all three . Without coordinated coverage, there are gaps where no policy responds. For example: A LenderTech automates disclosure generation. A UX change causes noncompliance with TILA. A CFPB investigation turns into a class action. Tech E&O says it’s lending conduct. Lender Liability says it was a tech failure. Result? Denied claim. Full exposure. What URM Does Differently: Legal-Led Risk Architecture URM isn’t a traditional broker. We: Map exposure using regulatory triggers and tech algorithms Score litigation risk across company specific risk profiles Model class action and regulatory exposure based on real-world CFPB actions Benchmark insurance structures used by similar LenderTech platforms Build customized Tech E&O + Lender Liability programs that actually respond We also integrate AI-specific coverage  into our models, because we know: Machine learning models make credit decisions Personalization tools affect disclosures AI can trigger discrimination claims without obvious intent And most insurance policies exclude or ignore AI completely. Final Thoughts: Insurance for LenderTech and Modern Financial Platforms As more lending activity migrates to technology, and as regulators increase scrutiny of the LenderTech sector, your coverage must evolve. Lender Liability and Tech E&O are no longer optional - they’re essential. But more importantly, they must work together . URM offers deep insight, technical fluency, and underwriting-focused firepower in one partner. Our solution isn’t a quote gathering, it’s a system of risk management. About the Author Steven Barge-Siever is the founder of URM. He’s a licensed attorney (CA, NY), former general counsel, and has led legal and risk teams at leading firms and VC-backed insurtech startups. He has structured hundreds of insurance programs and helped defend, settle, and prevent litigation at every level - from CFPB actions to complex securities class action disputes. Steven created Undr AI to turn that expertise into a digestible offering for complex clients - one that evaluates legal exposure, maps coverage gaps, and automates insurance precision for modern risk. 📞 Want to know where your liability lives? Let us show you. [Request a Custom Risk Review ] | [Get an AI powered Policy Gap Analysis Score] | [ Talk to Us ]

By Steven Barge-Siever, Esq. If you’re a tech company with fewer than 250 employees, there’s a good chance AllDigital is on your insurance program. And if you’re in California, be ready for a nonrenewal letter. Over the past few weeks, we’ve seen something many brokers and clients didn’t anticipate: formal non-renewal notices on D&O and EPL policies - often with no warning, no negotiation, and very little explanation. The catalyst? AXIS has pulled its admitted management liability capacity from AllDigital Specialty in California, citing deteriorating small business EPL conditions. But this change came quietly. We've found no public announcement, no statement from the carrier , and only one article (behind a paywall) acknowledging what happened. And it's not limited to EPL. What Do the Axis/AllDigital California Nonrenewals Actually Say? Despite EPL being the catalyst, here’s the AllDigital non-renewal language some clients are receiving: “We will not renew this policy when it expires. Your insurance will cease on the Expiration Date shown above.” “The reason for nonrenewal is that the risk exposures have materially changed and do not meet current underwriting criteria.” But the companies I’ve spoken with haven’t materially changed. The risk hasn’t changed. Insurer appetite did. Clients Want Reliability One of the most durable lessons I took from placing large, complex Fortune 500 programs at Aon and WTW: You don’t just compare pricing - you evaluate ability to pay. To do this you determine whether a carrier will: 1. cover a large loss ( policy language ), and 2. be able to absorb large losses and still be there when it matters ( financial stability ). Because what good is insurance that doesn’t insure? Every few years, a new offering - most often, via MGA - enters the market. They lead with pricing. They flood into specific sectors, win business through efficiency and discounting, and then, inevitably, face a hard decision: raise rates significantly or exit entirely . That’s what we’re seeing now. Carriers that priced small business and tech D&O/EPL at ultra-low levels are now walking away. Not tapering. Not renegotiating. Leaving. Sometimes the Discount Is  Worth It There is a valid counterargument to stability. Even Fortune 500 companies sometimes take a discount knowing it may not last. For most companies, two or three years of deeply discounted pricing is a simple win. It’s a form of opportunistic arbitrage. And that logic holds up - as long as the client understands the risk and the broker has a contingency plan . The problem is when clients take that risk unwittingly , based solely on quote comparisons, without any real awareness of downside exposure. Again, the ability to 1. cover a large loss ( policy language ), and 2. absorb large losses and still be there when it matters ( financial stability ). That’s where the broker’s job changes. No just to eliminate this year's risk - but to understand the tradeoffs and, most importantly, to have a contingency plan. The Math Behind the Moment Over the years, I've seen AllDigital quotes priced 30 – 50% below the rest of the market - specifically on California tech risk. It looked great. It was also somewhat questionable as I led the Fintech Practice at Vouch (an insurance company that competes with AllDigital). But the math is tough to actuarially justify : One D&O or EPL claim  can easily cost an insurer $1M. A $2,000 annual policy premium would require: $1,000,000 ÷ $2,000 = 500 clean policies to cover a single loss And that excludes admin costs, commissions, taxes, and reinsurance - making the real number closer to 750 - 1,000 clean policies to cover a loss. Perhaps even more importantly, losses typically don’t appear in isolation - they occur in clusters, triggered by macro shifts like layoffs, regulation, and employment volatility. When the claims hit, carriers without years of reserves face forced exits . The Risk of Over-Reliance We’re now seeing the fallout from brokers (and clients) relying too heavily on a single underwriting channel. And this problem is even more acute when considering the complexities of D2C (broker/underwriter) MGAs - these models fundamentally rely on a single underwriting channel (and reinsurance that is outside of their control). When that channel shuts off, your broker needs to have a sufficient bench of insurance carriers as backup - and experienced brokers to market the accounts with diligence. We’ve had clients come to us asking two questions: Can you help replace this coverage? What actually happened? The answers: 1. Yes - we access all insurance companies without playing favorite. 2. A program built to scale distribution - not sustain volatility. Side Note: The MGA Business Model Mirrors the VC Playbook Many/most clients weren’t buying AXIS directly. They were buying coverage through an MGA (AllDigital Specialty ) that built a platform for rapid D&O and EPL issuance. And while MGAs are a valuable part of the market, they are distribution models, not balance sheets . They don’t underwrite volatility. They don’t hold reserves. And they don’t always price for longevity. The model is eerily similar to early-stage startups: grow fast, scale revenue, hit distribution numbers. But insurance doesn’t pivot well . Especially not admitted business governed by rate filings and regulatory oversight. The Takeaway There is nothing revolutionary or unprecedented happing with this year's Axis/AllDigital nonrenewals. And this isn’t about traditional vs. insurtech. It’s about the fundamentals of insurance economics : Risk must be priced to survive the cycle. Reserves matter. Capacity is not infinite. Cheap premiums feel efficient until they disappear. And when carriers leave the market, they don’t send press releases. They send non-renewal notices . If you get a notice of non-renewal (they will come 90 days before your policy expires) reach out. At URM, we build every program assuming this can happen. If your EPL or D&O coverage has been disrupted, or you want a second set of eyes on what’s next, we’re ready to step in. If you want to understand your risk, we have analytical tools geared for tech. Upward Risk Management LLC When expertise is non-negotiable. By Steven Barge-Siever, Esq. URM | Founder & CEO steve@upwardriskmanagement.com www.upwardriskmanagement.com

Why most insurtechs still aren’t ready for the enterprise insurance market. By Steven Barge-Siever, Esq. In insurance, it all sounds the same at first. A policy is a policy. A broker is a broker. Property & Casualty (P&C) covers everything, right? But once you're on the inside - especially at the enterprise risk level - you see things for what they are: Not all risk is created equal. Not all coverage is “commercial.” And not all brokers are in the same business. Yet many insurtechs continue to treat billion-dollar companies and sidewalk cafes like they're buying the same product. And this is why my professional colleagues see no threat from insurtechs. If you want to build real tech for real risk, you need to start with the fundamentals: Understand the risk. Learn the language. Respect the complexity. Not All Insurance Is Commercial. Not All Brokers Are Equal. At the small business level, insurance is mostly standardized. A general liability policy for a boutique or barbershop can be quoted online in minutes. It works — for commoditized risk. But once your business has: A board of directors Venture investors Sensitive data or AI decisioning Multi-jurisdictional employees Enterprise customers with indemnity clauses …you’re in another league. You’re not buying coverage. You’re protecting capital, leadership, and reputation.This isn’t “commercial insurance.” This is corporate risk. Visual Contrast: BOP vs. $15M D&O Tower Barbershop BOP Series B Fintech D&O Tower $500/year premium $15,000,000 in policy limits Covers slip-and-fall Covers regulatory subpoenas, securities claims Standard ISO wording Custom manuscript language Retail quoting platform Broker-led negotiation + legal review Application: 1 page Application: financials, cap table, governance Low-impact litigation Board/investor-level personal liability One isn’t better than the other. But they are not the same product. And yet, most insurtechs treat them as though they are. Where Language Collapses, So Does Credibility We see it all the time: platforms built for scale that collapse everything into the P&C or “commercial” bucket. Nuance disappears. Risk is flattened. Vocabulary collapses. And it’s not just a branding issue. It’s a functional problem. “When I hear a founder say they’re automating the ‘entire commercial insurance market,’ I know they haven’t spoken to a single wholesale broker.” - Managing Director, National Brokerage A Real Breakdown: When Generic Coverage Meets Complex Claims I worked with a Series B Fintech company whose previous broker bundled Cyber and Tech E&O into a generic “commercial package.” Sounds good, feels efficient. The problem? The policy's Definition of Professional Services to excludes financial services. My first thought - their broker is out of their depth. That’s what happens when you treat real risk like a template. Language Is  the Product In this world, language is risk . Policies are legal contracts. Claims are legal disputes. Words determine whether you’re covered or denied. If your AI can’t correlate: Company specific operations  with risks and related coverage requirements, Professional liability vs. Lender Liability vs. Cyber vs. Media Liability What a company needs to consider in regulatory coverage …then you’re not just behind. You’re dangerous. Where Upward Risk Management and Undr AI Come In At Upward Risk Management , we serve complex clients. We’re not a volume shop. We’re the firm that gets called when things are complicated. We work with: Growth-stage companies with board-level liability Fintech and AI companies navigating novel claims VCs and GCs who want audit-ready, defense-tested coverage Brokers seeking expert partners for E&O, D&O, Cyber, and EPL That’s why we built Undr AI  - to do what generic insurtech tools can’t. It doesn’t just extract terms from policies. It interprets them. Understands them. Flags risks before they become claims. A Final Word for Founders Building in Insurance If you're building in this space, ask yourself the following, and feel free to reach out to us . Could a top broker rely on your platform without rewriting the outputs? Would a seasoned underwriter trust it to explain a coverage tower? What is it designed to handle - a $500 BOP or a $15M D&O Side A layer? If not - slow down . Talk to brokers. Learn from litigated claims. Build with humility, not just speed. Because the insurance industry doesn’t need faster clicks — it needs smarter systems.And it won’t trust you unless you earn it. The Bottom Line When the risk is small, any broker - or bot - might do. But when it gets complicated? You need someone who speaks the language, understands the liability, and sees around corners. That’s what we do at Upward Risk Management . And that’s why we built Undr AI . Upward Risk Management When expertise is nonnegotiable.

Venture Capital Insurance Hidden Exposures When startups offer equity as compensation, they are entering securities territory.  As valuations rise, and especially when companies face downturns, misrepresentation claims tied to employee equity are emerging as a high-risk litigation trend . These lawsuits don’t just impact the company - they will name board members , including VCs with active roles or board seats .  Without proper risk management and tailored D&O coverage, these exposures are personal. The New Litigation Trends that Impact Venture Capital Insurance As valuations climb and liquidity timelines stretch, employees begin scrutinizing the value of their equity. That scrutiny turns to legal action when: Companies go through down rounds, distressed M&A, or wind-downs; Equity is diluted, repriced, or rendered worthless; And internal messaging around “upside” starts to look more like misrepresentation. We’re seeing a clear trend: securities-based lawsuits brought by former employees  who claim they were misled about the value of their options or RSUs. And these claims don’t stop at the company - they often name individual board members , including VCs with active oversight or compensation roles. The Legal Theory: Misrepresentation, Fraud, and Fiduciary Breach Employees granted equity are not accredited investors. Yet they are often handed offer letters, internal decks, or verbal assurances that portray stock options as a sure path to wealth - without any mention of: Dilution mechanics Liquidation preferences or waterfall structures The speculative nature of private company equity This disconnect creates fertile ground for securities fraud and misrepresentation claims , especially under state Blue Sky laws and federal Rule 10b-5. These claims argue that companies and their leadership failed to disclose material risks , effectively treating employees as sophisticated investors - while withholding the very information those investors would have needed to make an informed decision. Why This Risk Is Personal - Especially for VCs These lawsuits aren’t just a corporate liability. They are increasingly naming individual directors , especially venture partners who: Sit on compensation committees Approve equity grants Participate in exit or financing discussions Oversee internal communications to employees In many cases, plaintiffs argue that these directors had a duty to ensure fair and transparent disclosures , especially when they encouraged or approved messaging that promoted equity upside. D&O Insurance: Often Inadequate, Sometimes Useless Most D&O policies don’t automatically cover  securities claims tied to employee equity. And even when they do, coverage may be lost due to: Fraud exclusions  or prior knowledge clauses Employment-related exclusions  if the claim is tied to hiring or compensation Narrow definitions  of what constitutes a “claim” or “wrongful act” Worse, these policies are often never reviewed  for the unique blend of securities, employment, and board governance exposures that arise in these cases. The Real-World Impact: Reputational and Financial For VC firms, these claims create cascading risk: Reputational damage  when failed exits lead to employee lawsuits Direct financial liability  if Side A coverage is denied or exhausted Fund-level exposure  if partners are named and indemnification is unavailable This isn’t theoretical. These lawsuits are happening. Boards are being blindsided. And in many cases, coverage gaps are only discovered when it's already too late . If You’re On a Board, You Need to Hear This: If your company promotes the upside of equity, it must also disclose the downside.If your firm takes a board seat, it inherits disclosure and fiduciary obligations  - and with them, personal liability. Upward Risk Management At Upward Risk Management , we specialize in protecting venture-backed boards from precisely this kind of exposure. We’ve designed coverage structures for companies navigating: Complex cap tables Secondary sales Tender offers Exit scenarios with challenging waterfall dynamics And we’ve placed custom securities and Side A coverage  designed for the realities of startup litigation. Want Peace of Mind? Let us review your current D&O program.We’ll identify gaps, assess your securities exposure, and structure coverage that protects your board - not just your balance sheet. Reach out for a confidential review. Because the only thing worse than a lawsuit… is learning you’re not covered.

Key Insurance Gaps for LenderTech Companies (And Why Specialized Coverage Matters) Introduction Lender-focused fintechs face complex, technical insurance risks that are often misunderstood (and underinsured) by standard startup policies. This guide highlights the three most critical gaps we commonly see in D&O and Tech E&O insurance for LenderTech companies: lender liability exclusions, regulatory sublimits, and restricted attorney selection. If your company originates loans, uses proprietary credit models, partners with banks, or operates under regulatory oversight, understanding these gaps is critical to protecting your business. Three Critical Coverage Gaps for LenderTechs 1. Lender Liability Coverage: Often Excluded What It Is: Lender liability refers to lawsuits or regulatory actions arising from alleged misconduct in loan origination, servicing, or collections. If your company interacts with borrowers (directly or through partners) you can be held liable for issues like: Misleading loan terms Servicing errors Unfair or deceptive collection practices Why It Matters: Many standard D&O and Tech E&O policies exclude borrower-facing risks unless lender liability coverage is specifically added. It doesn't matter if you use partner banks, fintech servicers, or outsource collections — regulators and plaintiffs will treat your company, and your board, as responsible if something goes wrong. Bottom Line: If you're touching the borrower experience, you need lender liability coverage. Technical structures won't shield you from regulatory enforcement or borrower litigation. 2. Regulatory Coverage: Capped by Sublimits What It Is: Regulatory coverage indemnifies the company and executives against defense costs, investigations, and settlements tied to actions from agencies like: CFPB (Consumer Financial Protection Bureau) FTC (Federal Trade Commission) SEC (Securities and Exchange Commission) State Attorneys General Why It Matters: For LenderTechs, regulatory claims often involve: Alleged violations of lending laws Consumer protection statutes (like UDAAP) Data privacy issues Discriminatory lending algorithms Yet most D&O policies cap regulatory claim coverage  with low sublimits - often $250K to $500K , even if the overall policy limit is several million. Bottom Line: Facing a CFPB or FTC action with only a few hundred thousand dollars of defense coverage can leave your company dangerously exposed. Regulatory investigations are among the most serious risks LenderTechs face — and most startup policies don't automatically cover them fully. Regulatory Sublimit A regulatory sublimit is a lower cap within your D&O policy  that specifically limits how much coverage you have for regulatory investigations and actions. Example: You buy $3M of total D&O coverage. But your regulatory claims sublimit is only $250K–$500K unless negotiated otherwise. Without attention to sublimits, companies risk feeling fully insured but being dangerously underprotected  when the real claims hit. 3. Defense Costs: Limits on Attorney Selection What It Is: When a regulatory investigation or serious litigation hits, having the right defense counsel is critical.Top-tier regulatory defense firms charge $1,500–$2,000 per hour — and early-stage defense work can shape the entire outcome of an enforcement action. Why It Matters: Many insurers include panel counsel requirements  in D&O policies.This means: You must select legal defense from a pre-approved list of firms. Non-panel firms require special approval — or are outright restricted. While panel firms may be sufficient for basic claims, complex regulatory investigations often require highly specialized counsel  with direct agency experience. Bottom Line: If regulatory risk is material to your business, your insurance should allow flexibility to pre-approve non-panel counsel  or negotiate flexibility to hire specialized regulatory defense teams when needed. Upward Risk Management When expertise isn't optional.