RWA First Stock's Major Acquisition: Why Buy a 'Traditional' Mortgage Company?

Foresight NewsPublished on 2026-06-12Last updated on 2026-06-12

Abstract

On June 10th, Figure Technology Solutions (Nasdaq: FIGR), a blockchain-native capital markets firm, announced a $717 million acquisition of Kiavi, a leading non-bank lender for residential real estate investors. The deal involves Figure acquiring Kiavi's technology and operations for approximately $538 million, while forming a joint venture with alternative asset manager Sixth Street to purchase Kiavi's existing loan portfolio. Sixth Street also provided a $3 billion forward purchase commitment. This acquisition marks a strategic shift for Figure, known as the "RWA (Real World Asset) first stock," allowing it to expand significantly into the larger market of first-lien mortgages. Kiavi specializes in non-qualified mortgage (Non-QM) loans, such as short-term fix-and-flip (RTL) and rental property (DSCR) loans—a segment traditionally underserved by major banks. The move is expected to increase Figure's first-lien loan origination to over $7 billion annually, aiming for these to constitute about 40% of its business by 2027. Both companies leverage AI for underwriting: Kiavi uses proprietary models to value renovated properties and automate document processing, dominating the fix-and-flip lending space. Figure plans to integrate these assets onto its blockchain platform, Provenance, using its new 'Adaptor' product to standardize and tokenize the loans for institutional investors on its Democratized Prime marketplace. While the integration poses challenges—including merging dif...


Author: Sanqing, Foresight News


On June 10th, blockchain-native capital markets company Figure Technology Solutions (Nasdaq: FIGR) announced the acquisition of Kiavi for $717 million. Kiavi is a non-bank residential real estate investor lending platform founded in 2013, with cumulative loan origination exceeding $30 billion.


In the deal, Figure acquires Kiavi's technology and operational platform, funding approximately $538 million through the issuance of about $600 million in senior unsecured notes. The remaining roughly $179 million is contributed by global alternative asset manager Sixth Street, with the two parties forming a joint venture to purchase Kiavi's existing loan portfolio on its balance sheet. Sixth Street also provides a $3 billion forward purchase commitment. The two companies have been long-term partners. Kiavi's current CEO, Arvind Mohan, will join Figure as Chief Business Officer after the deal closes, leading the business integration.


Image source: Figure tweet


After the announcement, FIGR's stock price opened higher, briefly touching $30.11 during the session, but later retreated, closing at $28.07, down 0.74%, with an intraday fluctuation of about 9%.


Kiavi


Kiavi's predecessor was LendingHome, founded in San Francisco in 2013 by Matt Humphrey and James Herbert, with backing from investors like Foundation Capital, Ribbit Capital, and Renren.com.


It primarily offers two types of products: short-term transition loans (Residential Transition Loan, RTL) for property renovation investors, and Debt Service Coverage Ratio (DSCR) loans for long-term rental properties.


It rebranded to Kiavi in 2021, and in June 2025 became the first private non-bank institution in the US to reach 100,000 cumulative loans originated.


According to the official announcement, Kiavi achieved revenue of over $250 million and EBITDA of over $100 million in 2025, both record highs; full-year origination volume was approximately $7.8 billion, up about 20% from approximately $6.5 billion in 2024.


First Lien


The origin of Figure dates back to late 2017. After leaving SoFi that year, co-founder Mike Cagney turned his attention to blockchain. In 2018, he and his wife June Ou founded Figure in San Francisco. The following year, they launched their first product on their self-built Provenance Blockchain: a home equity line of credit (HELOC) originated on-chain.


Mike Cagney | Image source: Bloomberg


Subsequently, the company expanded along the same logic—loan origination, financing, secondary market trading...—and gradually migrated these processes on-chain, building the consumer lending marketplace Figure Connect and the on-chain warehouse financing market Democratized Prime.


Following its Nasdaq listing in September 2025, Figure officially stated that it currently accounts for about 75% of the global tokenized RWA volume.


The problem is, HELOCs are second-lien loans.


Second liens rank behind first liens in repayment priority upon borrower default, carrying higher risk and supporting a smaller asset scale. Figure estimates that the market size for first liens is about 25 times that of second liens.


Kiavi's RTL and DSCR products are both first-lien loans, and they operate in the Non-Qualified Mortgage (Non-QM) space, which traditional banks have long avoided due to regulatory concerns—a space with strong demand but highly fragmented supply.


Figure is proactively shifting the focus of its asset classes.


Post-acquisition, Figure expects to add over $7 billion in new first-lien loan volume annually. According to CEO Michael Tannenbaum, Figure's first-lien business proportion had already increased from 10% to 20% in 2025, and the company expects this ratio to reach about 40% by the end of 2027.


AI


Both Figure and Kiavi excel at using AI to process non-standard data that traditional financial institutions are reluctant to touch, building moats in areas where manual processes cannot scale.


Kiavi's core technological asset is a proprietary "after-repair value estimation engine" and an automated document review system. An old, dilapidated property awaiting renovation is nearly impossible for traditional institutions to risk-quantify.


Kiavi's model can predict the post-renovation market value based on historical transaction data and renovation plans, enabling the scaling of credit decisions for loans like RTLs.


This capability has yielded significant market advantages. According to the Scotsman Guide's "2025 Top Private Lenders" rankings (underlying data from Forecasa), Kiavi's fix-and-flip loan origination volume in 2024 was approximately $5.5 billion, more than three times that of the second-place lender, and it continued to expand its lead in 2025.


Image source: Kiavi


Figure aims to solve the problem of what happens to assets after they leave Kiavi: how to put them on-chain, how to circulate them, and how to attract institutional capital. Figure's newly unveiled product in this deal, Adaptor, is designed precisely for this purpose.


It supports "Agent to Agent" automated integration, standardizing the diverse data formats from different originating institutions, thereby compressing the onboarding cycle for new partners.


Kiavi's assets will become the first real-world validation scenario for Adaptor after its launch. According to Figure's investor presentation materials, the company expects this transaction to achieve approximately $35 million in cost synergies within 24 months.


Two AI systems are being stitched together, pointing towards the same goal: making non-standard real estate loans priceable, tradable, and scalable on-chain.


Integration


Figure employs a dual-class share structure. According to the IPO prospectus, Cagney and his affiliates hold Class B shares, controlling about 69% of the total voting power at the time of the IPO; as of the latest proxy statement in April 2026, he still controls the majority of voting power, and Figure continues to be designated a "controlled company" by Nasdaq.


As a growth-stage company undertaking a major acquisition less than a year after its IPO, Figure also disclosed in its S-1 filing that previously existing material weaknesses in internal controls were yet to be remediated. The large-scale integration poses a non-negligible test for the execution team.


On Kiavi's side, its assets are sensitive to interest rate cycles. During the 2022 hiking cycle, lacking the backing of government-sponsored enterprises (GSEs) like Fannie Mae and Freddie Mac, the liquidity of Kiavi's assets in capital markets tightened, leading the company to make corresponding cost and personnel adjustments.


This vulnerability has already been validated during the high-interest-rate phase, and future interest rate changes will remain a significant external variable for RTL/DSCR origination volume.


Furthermore, Figure's existing assets are still heavily concentrated in HELOCs. Kiavi's asset types, data formats, and customer base are significantly different. It remains to be seen whether Adaptor can truly reduce integration costs and whether Kiavi's non-standard assets can be smoothly absorbed by institutional investors on Democratized Prime.


The good news is that Figure stated the deal will be accretive to earnings per share, with an unlevered cash payback period not exceeding 4 years, and reaffirmed its medium-term EBITDA margin target of around 60%; Kiavi's monthly loan flow exceeding $100 million will be directly fed into Democratized Prime.


Sixth Street's managed and committed capital scale of over $130 billion, along with its $3 billion forward purchase commitment, provides considerable capital buffer for the joint venture; Mohan's entry into the executive team means Kiavi's customer relationships and industry resources are retained within Figure's management structure.


The RWA narrative has been around for several years. This time, Figure is using $717 million to migrate an institution that has operated for 13 years and processes billions of dollars in real loans annually entirely onto the blockchain. This is one of the most structurally significant acquisitions in the RWA tokenization space to date.


The potential market space Figure points to is its estimated approximately $200 billion annual RTL/DSCR origination opportunity, underpinned by the long-term renovation and rental demand generated by the roughly $25 trillion stock of aging housing in the US.


If the integration proceeds smoothly, this could be a landmark node marking the transition of blockchain capital markets from "proof-of-concept" to "scale operations." Not just for Figure, but for the entire RWA credit asset market.

Related Questions

QWhat is the main reason for Figure Technologies Solutions' acquisition of Kiavi?

AThe primary reason for Figure Technologies Solutions' acquisition of Kiavi is to pivot its business focus towards first-lien loans, such as Kiavi's Residential Transition Loans (RTL) and Debt Service Coverage Ratio (DSCR) loans. These loans represent a significantly larger market compared to Figure's existing HELOC products, which are second-lien loans.

QWhat is the significance of the 'Adaptor' product introduced by Figure in this deal?

AThe 'Adaptor' product is significant as it is designed to standardize and automate the connection of data from various loan originators, like Kiavi, onto the blockchain. It aims to reduce the integration period for new partners and facilitate the tokenization and trading of non-standard real estate loan assets on Figure's platform.

QWhat core AI-driven capabilities does Kiavi bring to the combined entity?

AKiavi brings its proprietary 'renovation-after-value estimation engine' and automated document review system. These AI capabilities allow Kiavi to assess the risk and predict the post-renovation market value of older properties, enabling the scalable underwriting of non-standard loans like fix-and-flip (RTL) loans.

QWhat are the major challenges or risks associated with integrating Kiavi into Figure's operations?

AMajor challenges include: 1) The integration complexity for Figure, a growth-stage company with recent IPO and past internal control weaknesses. 2) The interest-rate sensitivity of Kiavi's loan assets, which have shown vulnerability in high-rate environments. 3) The uncertainty of whether Kiavi's non-standard assets can be seamlessly absorbed by institutional investors on Figure's Democratized Prime platform.

QWhat makes this acquisition a structurally significant event for the broader RWA tokenization market?

AThis acquisition is structurally significant because it involves migrating a large, established, and profitable real-world lending platform (Kiavi) with over a decade of operation and billions in annual loan volume onto a blockchain ecosystem. It represents a move from conceptual proof-of-concept to large-scale operational execution in the RWA tokenization space, targeting a massive underlying market of U.S. housing renovation and rental demand.

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While the concept of Agent S is fundamentally innovative, specific information about its creator remains elusive. The creator is currently unknown, which highlights either the nascent stage of the project or the strategic choice to keep founding members under wraps. Regardless of anonymity, the focus remains on the framework's capabilities and potential. Who are the Investors of Agent S? As Agent S is relatively new in the cryptographic ecosystem, detailed information regarding its investors and financial backers is not explicitly documented. The lack of publicly available insights into the investment foundations or organisations supporting the project raises questions about its funding structure and development roadmap. Understanding the backing is crucial for gauging the project's sustainability and potential market impact. How Does Agent S Work? At the core of Agent S lies cutting-edge technology that enables it to function effectively in diverse settings. Its operational model is built around several key features: Human-like Computer Interaction: The framework offers advanced AI planning, striving to make interactions with computers more intuitive. By mimicking human behaviour in tasks execution, it promises to elevate user experiences. Narrative Memory: Employed to leverage high-level experiences, Agent S utilises narrative memory to keep track of task histories, thereby enhancing its decision-making processes. Episodic Memory: This feature provides users with step-by-step guidance, allowing the framework to offer contextual support as tasks unfold. Support for OpenACI: With the ability to run locally, Agent S allows users to maintain control over their interactions and workflows, aligning with the decentralised ethos of Web3. Easy Integration with External APIs: Its versatility and compatibility with various AI platforms ensure that Agent S can fit seamlessly into existing technological ecosystems, making it an appealing choice for developers and organisations. These functionalities collectively contribute to Agent S's unique position within the crypto space, as it automates complex, multi-step tasks with minimal human intervention. As the project evolves, its potential applications in Web3 could redefine how digital interactions unfold. Timeline of Agent S The development and milestones of Agent S can be encapsulated in a timeline that highlights its significant events: September 27, 2024: The concept of Agent S was launched in a comprehensive research paper titled “An Open Agentic Framework that Uses Computers Like a Human,” showcasing the groundwork for the project. October 10, 2024: The research paper was made publicly available on arXiv, offering an in-depth exploration of the framework and its performance evaluation based on the OSWorld benchmark. October 12, 2024: A video presentation was released, providing a visual insight into the capabilities and features of Agent S, further engaging potential users and investors. These markers in the timeline not only illustrate the progress of Agent S but also indicate its commitment to transparency and community engagement. Key Points About Agent S As the Agent S framework continues to evolve, several key attributes stand out, underscoring its innovative nature and potential: Innovative Framework: Designed to provide an intuitive use of computers akin to human interaction, Agent S brings a novel approach to task automation. Autonomous Interaction: The ability to interact autonomously with computers through GUI signifies a leap towards more intelligent and efficient computing solutions. Complex Task Automation: With its robust methodology, it can automate complex, multi-step tasks, making processes faster and less error-prone. Continuous Improvement: The learning mechanisms enable Agent S to improve from past experiences, continually enhancing its performance and efficacy. Versatility: Its adaptability across different operating environments like OSWorld and WindowsAgentArena ensures that it can serve a broad range of applications. As Agent S positions itself in the Web3 and crypto landscape, its potential to enhance interaction capabilities and automate processes signifies a significant advancement in AI technologies. Through its innovative framework, Agent S exemplifies the future of digital interactions, promising a more seamless and efficient experience for users across various industries. Conclusion Agent S represents a bold leap forward in the marriage of AI and Web3, with the capacity to redefine how we interact with technology. While still in its early stages, the possibilities for its application are vast and compelling. Through its comprehensive framework addressing critical challenges, Agent S aims to bring autonomous interactions to the forefront of the digital experience. As we move deeper into the realms of cryptocurrency and decentralisation, projects like Agent S will undoubtedly play a crucial role in shaping the future of technology and human-computer collaboration.

722 Total ViewsPublished 2025.01.14Updated 2025.01.14

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