Federal Reserve Proposes Targeted Revisions to Mortgage Related Capital Requirements, Including MSR and Residential Mortgage Treatment Everyone’s Talking About AI. What Does It Mean for Your Deals? ERISA in ABS Transactions SPRING 2026
STRUCTURED FINANCE SPECTRUM, SPRING 2026 | 1 Editor’s Note As we move from winter into spring, a season often characterized by growth and renewal, we are pleased to welcome Marcus Lovatt, Steven Krivinskas, and Patrick Lightbown to Alston & Bird’s London office. Their arrival further strengthens our cross-border structured finance capabilities and reflects the continued expansion of our London platform. On both sides of the Atlantic, structured finance markets continue to operate at the intersection of regulatory evolution, technological acceleration, and a shifting political landscape. While broader economic uncertainty persists, market participants are adapting quickly, assessing risk, recalibrating strategies, and integrating new tools into increasingly compressed transaction timelines. The articles in the Spring 2026 Structured Finance Spectrum reflect that dynamic. We begin with an analysis of the Federal Reserve’s proposed targeted revisions to capital requirements for mortgage-related assets, including the recalibration of capital treatment for mortgage servicing rights and a more risk-sensitive framework for residential mortgage exposures. We then turn to an area receiving significant attention across the industry: artificial intelligence (AI). While AI often features in abstract or aspirational terms, our second article focuses on how AI is already being deployed in practical, transaction-driven contexts, offering concrete examples that we hope will resonate with readers navigating real-world deal execution. Our final article addresses Employee Retirement Income Security Act (ERISA) considerations in asset-backed securities (ABS) transactions, revisiting plan asset analysis, fiduciary exposure, and prohibited transaction risk in light of the Second Circuit’s recent decision in Powell v. Ocwen. Although ERISA is a familiar feature of the structured finance landscape, the decision serves as a timely reminder that ERISA issues continue to require careful, deal-by-deal analysis, particularly where certificate interests, servicing discretion, or participation thresholds are involved. As always, we hope the Structured Finance Spectrum provides practical insight and a useful lens through which to view current market developments. We look forward to continuing the conversation in the months ahead and, for those of you planning to attend, at our Structured Finance Forum in New York City on Wednesday, April 22, 2026. Katrina Llanes Partner, Finance Thomas Dunn Senior Associate, Finance SPOTLIGHT ON GROWTH: Welcome to Alston & Bird Steven Krivinskas Partner, London Finance Marcus Lovatt Partner, London Finance Patrick Lightbown Senior Associate, London Finance
STRUCTURED FINANCE SPECTRUM, SPRING 2026 | 3 Federal Reserve Proposes Targeted Revisions to Mortgage-Related Capital Requirements, Including MSR and Residential Mortgage Treatment This revised approach responds to comments received through the Economic Growth and Regulatory Paperwork Reduction Act review process, which called for removal of the deduction threshold in favor of a more transparent and riskbased framework. By: Katrina Llanes, Partner, Finance Anissa Malik, Associate, Finance The Federal Reserve Board has issued a proposal to recalibrate capital requirements for mortgage-related assets, including mortgage servicing rights (MSRs). The proposal follows the direction previewed in Vice Chair for Supervision Michelle Bowman’s February 16, 2026 remarks and The Board subsequently voted to release the proposal for public comment on March 19, 2026. Comments are due by June 18, 2026. The proposal includes several targeted changes affecting MSRs, accumulated other comprehensive income (AOCI), and the risk weighting of residential mortgage exposures. Revised Capital Treatment for MSRs and AOCI Alignment Across Banking Organizations The proposal would eliminate the long-standing capital rule requiring banks to deduct mortgage servicing assets once they exceed 25% of common equity tier 1 (CET1). Instead, all MSRs would be subject to a uniform 250% risk weight, with no corresponding CET1 deduction. This change simplifies the capital framework, removes a key regulatory barrier that had been cited as disincentivizing banks from holding MSRs, and makes the treatment of MSRs more consistent across banking organizations. This revised approach also responds to comments received through the Economic Growth and Regulatory Paperwork Reduction Act review process, which called for removal of the deduction threshold in favor of a more transparent and risk-based framework. AOCI Treatment for Category III and IV Banks The proposal would also require Category III and IV banking organizations to recognize most elements of AOCI in CET1 capital, aligning their capital treatment more closely with that already applicable to Category I and II institutions. Net unrealized gains and losses on available-for-sale debt securities would flow through to CET1, subject to limited exceptions, thereby increasing the sensitivity of regulatory capital ratios to changes in interest rates and market conditions. As a mitigation measure, the agencies propose a five-year transition period for Category III and IV institutions that previously elected to exclude AOCI from regulatory capital. During the phase-in, the amount of AOCI recognized in CET1 would be gradually increased according to a prescribed schedule, beginning with partial recognition and culminating in full inclusion. Institutions would be permitted to reflect both positive and negative AOCI during the transition, smoothing the potential impact of interest-ratedriven capital volatility while allowing time to adjust capital planning, risk management, and balance-sheet practices. Revised Loan-to-Value–Based Risk Weights for Residential Mortgages The agencies also propose a more risk-sensitive capital treatment for residential mortgage exposures by assigning risk weights based on loan-to-value (LTV) ratios and whether repayment depends on property-generated cash flows. Loans with lower LTVs would receive lower risk weights, while mortgages whose repayment is dependent on these cash flows—such as those supported primarily by rental income—would be subject to higher capital charges reflecting their greater credit risk profile. The proposal would replace the existing 50%/100% standardized risk-weight framework with a set of LTV-based tiers, allowing mortgage risk weights to more closely reflect underlying risk characteristics. Eligibility conditions would apply: to qualify, residential mortgage exposures must be prudently underwritten. Mortgages that are 90 days or more past due, on nonaccrual status, restructured, or secured by junior liens would continue to receive 100% risk weight. LTV ratios would be calculated using the outstanding loan balance rather than original principal, such that applicable risk weights may change over time as loans amortize and borrower equity increases, creating ongoing compliance and monitoring considerations throughout the life of the loan.
STRUCTURED FINANCE SPECTRUM, SPRING 2026 | 5 By: Lindsay Klein, Senior Associate, Finance Introduction It’s 11:30 p.m. on the night of closing. The loan tape has changed again, the custodian has just sent the exception list, and there are still disagreements over the covenants. This is where artificial intelligence (AI) is quietly starting to matter. As AI tools move from experimentation to real-world use, deal participants are beginning to ask not whether AI will be used, but how, by whom, and with what implications. Structured finance has always been data-heavy and document-intensive. Information relevant to a single transaction is often scattered across loan tapes, asset Everyone’s Talking About AI. What Does It Mean for Your Deals? files, legal agreements, performance reports, emails, and third-party platforms. As transaction timelines continue to compress and deal complexity increases, deal participants are increasingly exploring whether AI can help streamline processes, surface insights, and reduce execution risk without compromising judgment or control. So what does AI realistically mean for structured finance today? Where can it add value? Where are its limits? What should deal participants be thinking about as these tools become part of transaction workflows? Owner-Occupied vs. Non-Owner-Occupied Properties The proposal further differentiates between owner-occupied and non-owner-occupied properties in determining whether repayment depends on property-generated cash flows. Mortgages secured by a borrower’s principal residence would generally not be treated as cash-flow dependent, while loans secured by non-owner-occupied properties would be presumed to be cash-flow dependent unless relying exclusively on the borrower’s personal income and resources at origination. In addition, the proposal includes targeted clarifications and conforming changes within the broader mortgage capital framework, including operational refinements related to mortgage servicing assets and coordination with other proposed capital adjustments. Conclusion Taken together, the proposed revisions represent one of the most significant regulatory shifts affecting mortgage servicing rights in more than a decade, eliminating the MSR deduction threshold, applying a standardized risk-weighting approach, and introducing an LTV-based framework for residential mortgages designed to reduce structural capital disincentives. The Federal Reserve’s approach reflects a recognition that the existing capital framework has unintentionally pushed mortgage servicing activity toward nonbanks. As Bowman has noted, banks serviced approximately 95% of outstanding mortgage balances in 2008, compared with roughly 45% in 2023—an evolution she attributed in part to the “overcalibration” of capital requirements following the 2013 Basel III changes, particularly as applied to mortgage servicing rights. If adopted, these proposals are expected to materially reduce the capital intensity of MSR holdings and improve the relative economics of bank-owned servicing, potentially incentivizing greater bank reengagement in the servicing market. Over time, this shift could enhance liquidity and pricing dynamics and contribute to a meaningful recalibration of the MSR market as banks resume a larger and more active role in mortgage servicing. n
STRUCTURED FINANCE SPECTRUM, SPRING 2026 | 7 Why Structured Finance Is an AI Stress Test Compared to many other areas of finance, structured finance is uniquely demanding. Documents are often nonstandardized and heavily negotiated, and deal structures are frequently bespoke. Many decisions depend on context, judgment, and experience rather than bright-line rules. The tolerance for error, whether legal, operational, or creditrelated, is low. At the same time, the volume of data involved in a single transaction can be enormous. Servicers, custodians, trustees, rating agencies, originators, and other counterparties all generate information, often in different formats and on different timelines. Human review remains essential, but it is also time-consuming, resource-intensive, and increasingly vulnerable to fatigue and inconsistency, particularly under tight deadlines. AI’s potential advantage lies in its ability to process, analyze, and synthesize large volumes of disparate data at scale. When properly deployed, machine-learning models can help organize information, identify anomalies, and prioritize issues for human review. But because structured finance is judgment-heavy and relatively unforgiving of mistakes, AI adoption in this space necessarily looks different than in many other financial contexts. Real-World Hypotheticals The impact of AI in structured finance is best understood through concrete, transaction-driven scenarios. In practice, AI is best viewed as a triage tool rather than a substitute for contractual interpretation or fiduciary judgment. For example, instead of relying solely on static monthly reports, a servicer might use AI tools to continuously scan loan-level data and flag deviations from historical payment patterns, occupancy changes, or other emerging trends. These signals could prompt earlier human review, well before a formal default or covenant breach would otherwise occur. In this scenario, AI functions as an early-warning and prioritization layer; it does not act as an autonomous decision-maker. Custodians may leverage AI to assist with asset-file review across multiple transactions by flagging missing, inconsistent, or anomalous documents. AI tools can also help cross-reference documents against deal requirements, reducing manual checklist work and improving turnaround times, while leaving final determinations with human reviewers. Deal teams may use AI to consolidate data from multiple sources during diligence, reducing the time spent reconciling versions and tracking down information. This allows professionals to spend more time on issue analysis and less time on document hunting. Investors, particularly in residential mortgage-backed securities (RMBS) or commercial mortgage-backed securities (CMBS) contexts, may use AI-assisted analytics to model performance scenarios across large pools, helping identify outliers or trends that warrant deeper review. Across these examples, AI augments existing workflows – it does not replace expert judgment, but it may change where and how that judgment is applied. What AI Is Not Doing (Yet) Despite rapid advances, it is equally important to be clear about what AI is not doing in structured finance today. AI tools are not negotiating deal terms or replacing credit judgment. They are not resolving ambiguous drafting or reconciling conflicting contractual provisions. They are also not eliminating diligence; instead, they are refocusing it. Understanding these limits is essential to deploying AI responsibly and defensibly within transaction workflows. Overstating AI’s role risks both operational errors and misplaced reliance at precisely the moments when human expertise matters most. From Data to Decisions At a practical level, AI’s role in structured finance often begins with mechanics and ends with better-informed decisions. AI-enabled data aggregation can pull together fragmented information from PDFs, spreadsheets, emails, and other platforms, reducing version conflicts and manual reconciliation. Natural-language processing (NLP) tools can assist in extracting key terms, obligations, and triggers from legal documents such as mortgage repurchase agreements (MRAs), guaranties, and indentures, enabling faster issuespotting across large document sets. Pattern recognition applied to performance data may surface trends that signal emerging risks or opportunities earlier in a deal’s lifecycle, supporting earlier and more productive risk conversations. Insight generation through dashboards, summaries, and alerts can improve escalation by helping teams focus on what matters most, rather than simply producing more reporting. Together, these capabilities can translate into tangible benefits such as time savings through automation of repetitive tasks, deeper insights by identifying regularities and irregularities that manual review may miss, and scalability that allows participants to handle greater volume or complexity without a corresponding increase in headcount. One often overlooked benefit relates to training and onboarding. AI-assisted review tools can serve as a second set of eyes, helping standardize issue-spotting across reviewers and reduce variability in fast-moving or multideal environments. Over time, these tools may also help institutionalize knowledge and support greater consistency in execution. Data Rights and Ethical Considerations As AI becomes embedded in live transaction workflows, questions around data rights and ethics shift from abstract policy discussions to deal-specific risk considerations. What happens if AI tools are trained on restricted deal data? Who owns outputs generated from third-party documents? How do representations and warranties intersect with analyses produced using AI-processed information? Practical considerations include source transparency— understanding of where data comes from and whether it is reliable—as well as permissions and intellectual property rights associated with document use. Data sensitivity also matters. Although data rights may be more straightforward for nonconsumer assets, consumer-related transactions raise additional privacy and regulatory concerns. This revised approach responds to comments received through the Economic Growth and Regulatory Paperwork Reduction Act review process, which called for removal of the deduction threshold in favor of a more transparent and riskbased framework. Data hygiene remains critical. Inconsistent or poorly structured data can undermine AI performance, reinforcing the importance of standardization and disciplined information management.
STRUCTURED FINANCE SPECTRUM, SPRING 2026 | 9 By: Fahad Saghir, Counsel, Compensation, Benefits & ERISA When ERISA plans purchase notes and certificates in assetbacked securities (ABS), do the underlying assets become ERISA plan assets? A recent Second Circuit opinion, Powell v. Ocwen Fin. Corp., provides important guidance. What Is ERISA? The Employee Retirement Income Security Act of 1974, as amended (ERISA), regulates almost all privately sponsored employee benefit plans in the country, including pension, profit-sharing, and other retirement plans. Plans governed by ERISA are subject to the statute’s strict fiduciary responsibility and prohibited transaction provisions. ERISA fiduciaries must act solely in the interests of plan participants and beneficiaries and with the skill and prudence of a knowledgeable person acting in a like capacity and familiar with these matters (generally referred to as the “prudent expert” standard). Breach of these duties can result in personal liability for the fiduciary. ERISA also prohibits fiduciaries from engaging in certain transactions (such as ERISA in ABS Transactions the sale or exchange of property, the extension of credit, or the furnishing of goods or services) with certain parties related to the plan. A prohibited transaction could result in excise taxes and would require unwinding the transaction. The Relevance of Being Deemed ERISA Plan Assets As a general rule, when an ERISA plan invests in another entity, the plan’s assets include its investment in that entity, not the entity’s underlying assets. A“look-through”exception to the general rule, however, applies when a plan acquires a 25% or greater equity interest in the entity and the security is not publicly offered or the entity is not an operating company. In that case, ERISA plan assets include both the investment in the entity and an undivided interest in the entity’s underlying assets. As a result, the manager of these assets is subject to ERISA’s fiduciary responsibility and prohibited transaction rules. Department of Labor (DOL) regulations define an “equity interest” as any interest other than an instrument treated as indebtedness under local law and lacking substantial equity Finally, data hygiene remains critical. Inconsistent or poorly structured data can undermine AI performance, reinforcing the importance of standardization and disciplined information management. Key Risks and Practical Watchouts AI adoption in structured finance is not without risk. Firms integrating AI into regulated or fiduciary workflows should be prepared to explain not just the outputs, but also the controls surrounding their use. Over-reliance on AI and poor data quality remain persistent risks. Human oversight is essential, and AI should augment, not replace, expert judgment. Poor inputs will produce poor insights regardless of a tool’s sophistication. Regulatory scrutiny is also evolving. As AI use becomes more prevalent, regulators may increasingly focus on governance, transparency, and accountability. Firms should monitor developments closely and be prepared to adapt as expectations begin to take shape. Looking Ahead Rather than seeking definitive answers, firms may benefit from asking a few practical questions now: Where are the biggest diligence or monitoring bottlenecks? Which processes are truly judgment-heavy, and which are primarily process-driven? What data already exists within the organization but is not being fully used? The AI toolkit will continue to evolve, but effective implementation will require cross-functional collaboration among legal, operations, technology, and business teams, as well as a cultural shift. Structured finance professionals will need to become more data-savvy and tech-curious, without losing sight of the judgment and experience that underpin successful transactions. Conclusion AI offers meaningful potential for structured finance, but success depends on thoughtful implementation, continued human expertise, and robust data governance. When used well, AI can help teams focus attention where it matters most and execute deals with greater confidence. Firms that invest early in AI literacy and infrastructure will be better positioned not only to operate efficiently in a data-driven environment, but also to deploy AI in a way that strengthens, rather than undermines, confidence in deal execution. n
STRUCTURED FINANCE SPECTRUM, SPRING 2026 | 11 features. A beneficial interest in a trust, however, is an equity interest. In an ABS transaction, if the underlying assets constitute ERISA plan assets, a person exercising discretionary authority over those assets would be an ERISA fiduciary. Certain activities involved in servicing the pool assets and administering the issuer could be deemed prohibited transactions. The District Court Decision in Powell v. Ocwen In Powell v. Ocwen, an ERISA plan invested in notes and certificates in six residential mortgage-backed securities (RMBS) trusts. The notes were issued under indenture agreements, while the certificates were issued by real estate mortgage investment conduit (REMIC) trusts. The plan trustees filed suit alleging that the servicer of the underlying mortgages breached ERISA fiduciary duties by mismanaging the mortgages and committed prohibited transactions by engaging in transactions with related entities. The threshold question was whether the mortgages constituted ERISA plan assets. If they did not, the servicer could not have acted as an ERISA fiduciary or committed prohibited transactions. Applying New York and Delaware contract law, the Southern District of New York held that both the notes and certificates were properly treated as indebtedness and lacked substantial equity features. The court therefore found that the notes and certificates were debt and that the underlying mortgages were not ERISA plan assets. The Second Circuit Decision in Powell v. Ocwen On appeal, the Second Circuit upheld the district court’s conclusion that the notes were debt but reversed its ruling as to the certificates. The court found that the certificates were equity and remanded the case to the district court to determine whether Ocwen acted as a fiduciary with respect to the mortgages underlying the three REMIC trusts. Notes The Second Circuit reviewed the traditional distinction between debt and equity, including the unqualified obligation to pay a sum certain at maturity date along with a fixed percentage of interest payable regardless of the debtor’s income and equity’s realistic possibility of upside potential. The court also reviewed eight factors identified by the Internal Revenue Service (IRS) in Notice 94-47. The court concluded that the notes reflected a traditional debt structure, exposing noteholders only to class-based credit risks. It rejected the plaintiff’s arguments that the trusts’ thin capitalization increased the risk that the noteholders will not be repaid or that the notes were subordinated to the trusts’ general creditors. Certificates The court found that the certificates in the REMIC trusts were equity under the plain language of the DOL plan asset regulation, which provides that beneficial interests in a trust are equity interests, rather than under a common-law debt-versus-equity analysis.. The court found that having a beneficial interest in a trust constitutes an equity interest regardless of whether the security qualifies as indebtedness under local law. The court also found that the ERISA plan held a beneficial interest in the REMIC trust because it benefited from the property held in the trust. Therefore, the court concluded that the plan’s investments in certificates were equity interests. Implications for ABS Transactions ERISA practitioners have traditionally treated certificates as equity in ABS transactions. Issuers typically avoid holding plan assets either by prohibiting ERISA plans from acquiring certificates or by limiting plan investments to less than 25% of the value of each class. In some transactions, issuers allow ERISA plans to acquire interests in certificates without tracking whether participation has been limited to less than 25%, effectively assuming that the underlying assets are ERISA plan assets. This creates the risk that parties exercising discretion over the issuer’s assets are ERISA fiduciaries and may engage in prohibited transactions when dealing with related parties. The risk of ERISA fiduciary status may be reduced by limiting the activities that a servicer can engage in. Under ERISA, a person is a fiduciary to the extent that the person exercises any discretionary authority or control over the management or disposition of plan assets. If a servicer simply follows the terms of the servicing agreement without exercising discretionary control, it may not be acting as a fiduciary even if the mortgages are plan assets. On remand, the district court will now address this issue in Ocwen. With respect to the prohibited transaction issues, the industry standard has been to comply with the“Underwriters’ Exemptions,” a set of prohibited transaction exemptions issued by the DOL that allow certain transactions otherwise prohibited, provided that specified conditions are satisfied. The Second Circuit upheld the district court’s conclusion that the notes were debt but reversed its ruling on the certificates. The court found that the certificates were equity and remanded the case to the district court to determine whether Ocwen acted as a fiduciary for the mortgages underlying the three REMIC trusts. Takeaways The decision underscores that ERISA plan asset issues must be carefully analyzed at the time of the transaction. Issuers should consider whether to allow ERISA plans to acquire certificates at all or limit participation to less than 25%. When ERISA plans are allowed to acquire certificates without any limit, it is important to ensure that servicers do not exercise any discretion over managing those assets and that the conditions for any prohibited transaction exemption are fully satisfied. n
STRUCTURED FINANCE SPECTRUM, SPRING 2026 | 13 Katrina Llanes Partner, Finance Guest Editor Thomas Dunn Senior Associate, Finance Guest Editor Contributing Authors Anissa Malik Associate, Finance Fahad Saghir Counsel, Compensation, Benefits & ERISA Lindsay Klein Senior Associate, Finance Katrina Llanes Partner, Finance Finance Steve Blevit Partner +1 310 228 3863 stephen.blevit@alston.com Tara Castillo Partner +1 202 239 3351 tara.castillo@alston.com Shanell Cramer Partner +1 212 210 9580 shanell.cramer@alston.com Aimee Cummo Partner +1 212 210 9428 aimee.cummo@alston.com Anna French Partner +1 212 210 9555 anna.french@alston.com Steven Krivinskas Partner +44 20 8161 4425 steven.krivinskas@alston.com B.K. Lee Partner +1 212 905 9138 bklee@alston.com Katrina Llanes Partner +1 212 210 9563 katrina.llanes@alston.com Structured & Warehouse Finance Multipractice Team Marcus Lovatt Partner +44 20 8161 4418 marcus.lovatt@alston.com Peter McKee Partner +1 214 922 3501 peter.mckee@alston.com Joe McKernan Partner +1 212 210 9476 joseph.mckernan@alston.com Jon Ruiss Partner +1 202 210 9508 jon.ruiss@alston.com Pat Sargent Partner +1 214 922 3502 patrick.sargent@alston.com Kristen Truver Partner +1 212 210 9567 kristen.truver@alston.com Robin Boucard Counsel +1 212 210 9454 robin.boucard@alston.com Valerie Clark Counsel +1 212 905 9152 valerie.clark@alston.com Solmaz Kraus Counsel +1 310 228 3832 solmaz.kraus@alston.com
STRUCTURED FINANCE SPECTRUM, SPRING 2026 | 15 Structured & Warehouse Finance Multipractice Team Derek Marks Counsel +1 202 239 3046 derek.marks@alston.com Emily Redmerski Counsel +1 212 210 9589 emily.redmerski@alston.com Karen Wade Counsel +1 214 922 3510 karen.wade@alston.com Charlene Yin Counsel +1 212.905.9033 charlene.yin@alston.com Carly Bennett Senior Associate +1 212 210 9597 carly.bennett@alston.com Christina Braswell Senior Associate +1 404 881 7364 christina.braswell@alston.com Brenna Dorgan Senior Associate +1 212 210 9439 brenna.dorgan@alston.com Thomas Dunn Senior Associate +44 20 8161 4373 thomas.dunn@alston.com Donald Gallino Senior Associate +1 212 905 9034 donald.gallino@alston.com Chris Juarez Senior Associate +1 213 576 1095 chris.juarez@alston.com Ashley Kennedy Senior Associate +1 212 210 9516 ashley.kennedy@alston.com Lindsay Klein Senior Associate +1 212 210 9513 lindsay.klein@alston.com Patrick Lightbown Senior Associate +44 20 8161 4421 patrick.lightbown@alston.com Mark LoBiondo Senior Associate +1 212 210 9455 mark.lobiondo@alston.com Sarah McClellan Senior Associate +1 212 210 9557 sarah.mcclellan@alston.com Krishna Pathak Senior Associate +1 202 239 3638 krishna.pathak@alston.com Jacob Walpert Senior Associate +1 212 210 9489 jacob.walpert@alston.com Aryeh Wolosow Senior Associate +1 212 210 9599 aryeh.wolosow@alston.com Jaime Turcios Zacarias Senior Associate +1 212 210 9406 jaime.turcioszacarias@alston.com Shazell Archer Associate +1 212 210 9467 shazell.archer@alston.com Lydia Balestra Associate +1 212 905 9073 lydia.balestra@alston.com Jonathan Fenster Associate +1 212 905 9077 jonathan.fenster@alston.com Jacob Koenigson Associate +1 212 905 9192 jacob.koenigson@alston.com Ann Lee Associate +1 212 905 9370 ann.lee@alston.com Anissa Malik Associate +1 212 210 9468 anissa.malik@alston.com Michael McEvoy Associate +1 212 905 9080 michael.mcevoy@alston.com Akruti Patel Associate +1 214 922 3497 akruti.patel@alston.com Kirby Shilling Associate +1 212 210 9488 kirby.shilling@alston.com Financial Restructuring & Reorganization Peter Amend Partner +1 212 905 9166 peter.amend@alston.com Stephen Blank Partner +1 212 210 9472 stephen.blank@alston.com William Hao Partner +1 212 210 9417 william.hao@alston.com Jacob Johnson Partner +1 404 881 7282 jacob.johnson@alston.com Leah McNeill Partner +1 404 881 7822 leah.mcneill@alston.com Will Sugden Partner +1 404 881 4778 will.sugden@alston.com Structured & Warehouse Finance Multipractice Team
STRUCTURED FINANCE SPECTRUM, SPRING 2026 | 17 Consumer Regulatory Anoush Garakani Partner +1 213 576 1181 anoush.garakani@alston.com Steve Ornstein Partner +1 202 239 3844 stephen.ornstein@alston.com Nanci Weissgold Partner +1 202 239 3189 nanci.weissgold@alston.com Morey Barnes Yost Counsel +1 202 239 3674 morey.barnesyost@alston.com Josh Dhyani Senior Associate +1 202 239 3160 josh.dhyani@alston.com Bank Regulatory Cliff Stanford Partner +1 404 881 7833 cliff.stanford@alston.com Tax John Baron Partner +1 704 444 1434 john.baron@alston.com Kendall Houghton Partner +1 202 239 3673 kendall.houghton@alston.com Clay Littlefield Partner +1 704 444 1440 clay.littlefield@alston.com Andi Mandell Partner +1 212 905 9374 andi.mandell@alston.com Ashley Menser Partner +1 919 862 2209 ashley.menser@alston.com Structured & Warehouse Finance Multipractice Team Benefits, ERISA & Executive Compensation Blake MacKay Partner +1 404 881 4982 blake.mackay@alston.com Kyle Woods Partner +1 404 881 7525 kyle.woods@alston.com Fahad Saghir Counsel +1 202 239 3220 fahad.saghir@alston.com ‘40 Act Martin Dozier Partner +1 404 881 4932 martin.dozier@alston.com George Silfen Partner +1 212 905 9106 george.silfen@alston.com REITs Don Hammett Partner +1 214 922 3413 donald.hammett@alston.com Sarah Ma Partner +1 202 239 3281 sarah.ma@alston.com Structured & Warehouse Finance Multipractice Team Securities Matthew Mamak Partner +1 212 210 1256 matthew.mamak@alston.com Litigation–Lending & Structured Finance John Doherty Partner +1 212 210 1282 john.doherty@alston.com Litigation–Trusts Elizabeth Buckel Partner +1 212 210 1289 elizabeth.buckel@alston.com Alex Lorenzo Partner +1 212 210 9528 alexander.lorenzo@alston.com Chris Riley Partner +1 404 881 4790 chris.riley@alston.com Jared Slade Partner +1 214 922 3424 jared.slade@alston.com
STRUCTURED FINANCE SPECTRUM, SPRING 2026 | 19 Structured & Warehouse Finance Multipractice Team Insurance Mona Bhalla Partner +1 212 905 9029 mona.bhalla@alston.com Tejas Patel Counsel +1 404 881 4987 tejas.patel@alston.com Funds Tim Selby Partner +1 212 210 9494 tim.selby@alston.com Heather Wyckoff Partner +1 212 905 9137 heather.wyckoff@alston.com 144A Justin Howard Partner +1 404 881 7758 justin.howard@alston.com CDOs/CLOs Patrick Hayden Partner +1 704 444 1453 patrick.hayden@alston.com Bradley Johnson Partner +1 704 444 1460 brad.johnson@alston.com Corporate Debt Paul Hespel Partner +1 212 210 9492 paul.hespel@alston.com Corporate Trust Drew Peterson Partner +1 704 444 1369 drew.peterson@alston.com Private Credit Kate Moseley Partner +1 214 922 3434 kate.moseley@alston.com Corporate Transparency Act Chip More Senior Associate +1 202 239 3282 chip.more@alston.com Delaware Trust Jason Solomon Partner +1 704 444 1295 jason.solomon@alston.com Derivatives Cheryl Isaac Partner +1 202 239 3043 cheryl.isaac@alston.com Private Real Estate Funds Amie Benedetto Partner +1 404 881 4830 amie.benedetto@alston.com Mergers & Acquisitions and Joint Ventures Alison LeVasseur Partner +1 404 881 4475 alison.levasseur@alston.com Jeremy Silverman Partner +1 404 881 7855 jeremy.silverman@alston.com Private Equity Scott Kummer Partner +1 704 444 1077 scott.kummer@alston.com William Snyder Partner +1 650 838 2119 william.snyder@alston.com Structured & Warehouse Finance Multipractice Team Real Estate Eric Berardi Partner +1 404 881 7863 eric.berardi@alston.com Ellen Goodwin Partner +1 212 210 9447 ellen.goodwin@alston.com
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