Tech-Driven Strategies for Lending Capital Raising

Contributors: Stacy Dubovik, Financial Technology and Blockchain Researcher, ScienceSoft; Alex Savanovich, Senior Data Scientist, ScienceSoft; Vadim Belski, Principal Architect, ScienceSoft.

Editor’s note: Dimitry Senko, a Lending IT Consultant at ScienceSoft, overviews digital strategies that may help lenders effectively raise capital and shares how to deploy the technology tools behind them with minimal risks.

Many of my clients in lending IT these days ask me about alternative capital-raising strategies to expand their capital sources and reduce costs. With shrinking lender margins and climbing investor expectations for liquidity, it’s no surprise that even historically viable avenues like loan wholesales and classic securitization are losing their edge due to unbearably high capital costs.

Here are four digital strategies that have proven effective for banks and non-bank lenders. Based on my experience, by automating complex processes, expanding investor reach, and removing geographic barriers, these strategies can drive a meaningful reduction in capital costs and speed up funding by weeks or even months.

Strategy #1. Private Placements on P2P Debt Trading Platforms

Lenders can list loan portfolios, structured notes, and debt instruments on specialized platforms that connect them directly with institutional and accredited investors seeking alternative fixed-income opportunities. By placing debts on alternative platforms, lenders can bypass traditional fundraising intermediaries and maintain full control over terms, rates, and investor qualifications.

At the technical level, these debt trading platforms function as peer-to-peer (P2P) marketplaces (think eBay or Upwork if you’re looking for familiar examples) where a matching engine connects investors to best-fitting loan purchasing opportunities. Such platforms typically provide standardized listing formats, investor accreditation checks, document e-signing, and integrated payment features. Advanced platforms may even incorporate investment risk scoring models and portfolio composition tools, letting investors assemble custom packages of loans based on risk appetite, duration preferences, and yield requirements. Lenders can manage placements, monitor trade performance, conduct additional due diligence, and execute deal transactions via secure virtual data rooms.

How exactly does this strategy drive capital inflow? P2P venues make debt products previously available only to institutional players accessible to a broad range of investors. Simplified user experience, eliminated costs of traditional mediation, and an opportunity to yield, on average, 16% greater returns compared to traditional vehicles attract a wide investor base. For example, Yieldstreet, an investing platform focused on private credit and mortgage debt, has attracted more than 500K investors by offering tailored retail funds with affordable investment minimums. For lenders, platform placements give immediate access to pre-qualified investor networks without the costs of building their own audience and funding infrastructure. The established credibility of major platforms reduces investor hesitation, while asset flexibility and streamlined transacting result in 2–6x faster funding cycles compared to direct solicitation.

How to implement this strategy with minimized risks

• Security and compliance must be built into your plan from day one. End-to-end data encryption, multi-factor authentication, and audit logging are the minimal stack of controls to look for when selecting a debt trading platform. SOC 2 Type II certification indicates these controls are in place. Also, examine the platform’s investor verification procedures: ecosystems with robust accreditation checks protect lenders from downstream regulatory complications that could trigger rescission rights or penalties. And make sure the platform maintains SEC registration either as a broker-dealer or an alternative trading system.
• By integrating your loan portfolio management system with the chosen debt trading platform, you can automatically upload the most recent offering details to the platform and quickly pull placement performance details necessary for holistic portfolio analytics. Automation prevents data sharing delays and errors that may compromise the accuracy of your portfolio insights. Prioritize platforms that offer well-documented APIs and developer-friendly SDKs to enable quick and easy integration with your internal systems. Some of the qualifying platform options I’ve shortlisted for my lending IT clients are YieldStreet, Percent, and MoneyLion.

Strategy #2. AI-Optimized Loan Pooling for Securitization

Securitization has long been a capital-raising staple for lenders, but it mainly relied on parametric loan bundling and addressed general investor demand. Artificial intelligence (AI) is reshaping the process by creating optimized loan pools that attract investors with targeted risk-return profiles. In essence, intelligent tools analyze loan characteristics and group similar assets into pools that maximize the appeal to specific investor segments.

AI tools go beyond calculating traditional maturity and exposure score metrics when pooling loans. Machine learning (ML) algorithms can automatically capture, process, and analyze hundreds of term, risk, yield, and market variables from multiple sources. Upstart, an early strategy adopter, says that its ML models used for risk pricing and pool design can process 1,600+ loan variables. Such models can identify subtle data patterns and correlations and predict borrower payment behaviors, default probabilities, and yield curves with remarkable accuracy. Advanced models can further segment loans into tranches with consistent risk-return profiles and auto-construct investment-ready pools. Add real-time analytics to the mix, and you will be able to continuously monitor loan-level pool performance and optimize your offerings on the fly to better cater to investor preferences.

Rebeco highlights that investment entities seeking conservative returns allocate more eagerly in pools precisely calibrated to their distress risk tolerance. For lenders, creating loan pools that match investor appetites and perform predictably directly translates into capital growth. The success story of Pagaya Technologies, which relies on AI-supported asset-backed securitization (ABS) to fund auto and personal loans, is a primary testament to AI’s benefits for capital raising. By applying AI to construct tailored, market-efficient loan pools for various institutional buyer segments, Pagaya attracted $27 billion across 60+ ABS transactions, which made the company the US’ largest personal and auto loan ABS issuer in just seven years after its inception.

How to implement this strategy with minimized risks

• For now, there are no turnkey AI solutions designed specifically for loan securitization. And even if they were, no generic algorithms would be able to adequately capture the unique characteristics of your loan portfolio. In contrast, custom-built machine learning models can reason on specific loan performance patterns, borrower behaviors, and market dynamics and suggest optimized pool designs. Data scientists at ScienceSoft managed to create custom ML algorithms for our clients that showed 90% and higher prediction relevance — the rate unattainable for commercial models. If this strategy looks appealing to you, consider using frameworks like TensorFlow and PyTorch, which offer comprehensive toolkits for ML engineering. This way, you won’t need to build and train your ABS model completely from scratch.
• AI models are only as useful as the data used to train them, so meticulous preparation of the training dataset is central to succeeding in this strategy. Validate your data for gaps, outliers, and inconsistencies to ensure your training dataset contains accurate and relevant information. Manual data cleaning may take months, so it makes sense to invest in data validation tools like Informatica or Datameer to automate the process.
• Setting up a continuous reconciliation loop where model predictions are compared against the actual loan performance will help you quickly spot variance areas and progressively improve the model for better predictive accuracy.
• Explainable AI frameworks like SHAP and LIME make it easier to trace the logic behind the model’s pool construction decisions and implement targeted reasoning improvements.

Strategy #3. Blockchain-Based Debt Tokenization

Lenders use tokenization technology to convert loan portfolios and debt instruments into programmable digital assets (tokens) that exist on an immutable blockchain ledger. By tokenizing their conventional capital-raising avenues, lenders can unlock higher liquidity from traditionally low-liquid assets. The strategy is relatively new, but McKinsey predicts that by 2030, on-chain loan securitization will become a standalone, $300 billion market driving faster and more efficient capital raising for lenders.

Debt tokens can be issued on a custom or third-party platform and traded on the primary issuance venue and secondary marketplaces. The geo-agnostic token transferability on blockchain creates market liquidity that cannot be achieved with any other traditional debt trading mechanisms. Tokenization fully inherits the benefits of blockchain: transparent asset ownership records, minimized risks of fraud, and near-instant transaction settlement. Smart contracts can automatically handle token minting, trading, principal distributions, compliance checks, and other operations that take place across the token’s life cycle, ensuring efficient workflows. For example, when a borrower makes a payment, smart contracts calculate due interest, distribute principal to token holders, and update ownership records – all within seconds.

The real game-changer for lenders is how tokenization broadens investor access. Fractional asset ownership enabled by tokenization allows investors to purchase portions of loan obligations rather than entire notes. This dramatically reduces the minimum investment threshold (from hundreds of thousands to potentially hundreds of dollars), making the debt deals accessible to lower-net-worth investors previously excluded from the market. For example, by fractionalizing and tokenizing its SCOPE private credit fund, Hamilton Lane reduced the minimum investment from the usual $5 million to just $10,000.

Figure Technologies went even further and built its entire HELOC loan origination process on the blockchain rails. Straight-through loan tokenization, cost-efficient debt pooling (on average, $500 vs. thousands of dollars per iteration), and streamlined trade operations helped Figure become the US’ largest non-bank HELOC lender six years after the company was founded.

How to implement this strategy with minimized risks

• While it’s possible to build a custom solution for debt token offering, tokenizing on third-party platforms like Securitize or Polymath comes with cogent advantages. First, platform-based tokenization frees you from the need to engineer your own token distribution and compliance infrastructure, which reduces the token’s time-to-market (2–3 months vs. 6–12 months for custom development). Second, it’s 3–10 times cheaper to tokenize on ready platforms than to build your own platform. Third, established platforms have a well-developed partner ecosystem and offer integrations with multiple trading venues, including CeFi marketplaces and DeFi trade platforms built on other blockchains. For you, this means hassle-free access to secondary markets and broader investor reach without the extra efforts needed to establish third-party integrations.
• Each debt tokenization scenario requires unique tokenomics (governance rules behind token operations), so regardless of the implementation approach, you can’t avoid building tailored smart contracts to automate your specific rules. Most commercial tokenization platforms offer ready smart contract templates for quicker coding, meaning you don’t need to go fully custom. If you still need to create custom contracts for specific workflows, thoroughly vet smart contracts for accuracy and security before rollout (check the practices in detail here). Coding errors can be catastrophic here: a tiny syntax gap in smart contract logic cost Compound Finance $80M in incorrect token distribution.

Strategy #4. Loan Offering Distribution via Ecommerce Marketplaces and Payment Apps

Expanding loan distribution to non-lending channels can help lenders reach potential borrowers who might never apply for traditional financing. This strategy transforms consumer and business purchases into lending opportunities by integrating financing options at the point of decision.

Using open APIs, lenders can deploy loan offerings directly into ecommerce platforms, service marketplaces, and payment apps. In most cases, lenders don’t need to develop any dedicated software. Hosting platforms typically offer ready-to-use APIs to integrate with their solutions and provide white-label UI components that can be customized to the lender’s brand. At the same time, this strategy requires robust loan origination solutions on the lender’s side to support quick background processing and ensure frictionless experiences for shared customers.

A primary example of how this strategy can be realized is the popular Buy Now Pay Later (BNPL) model. Pioneers like Klarna and Afterpay attracted millions of customers by offering convenient installment loans precisely where and when consumers need them. Some of my clients in lending argue that the niche is too busy and competing with BNPL giants may not be worth the investments. However, the studies show that consumer demand is sufficient to justify BNLP rollout. I’ve recently come across a survey by PYMNTS and Amount, where 70% of current BNPL users said they would use BNPL plans by traditional lenders if such products were available. The same survey also found out that traditional players have the edge over fintech peers when it comes to building consumer trust. Early moves of Citi prove the strategy’s feasibility: embedding BNPL-style loans and lines of credit across merchants (Shopify, Pods, LG, and 192 more) helped the institution achieve a 7% increase in retail lending revenue.

How to implement this strategy with minimized risks

• When expanding your loan offering to alternative channels, focus on platforms that align with your target borrowers rather than just chasing high traffic. A well-matched audience will drive higher conversion rates. For example, if you offer specialty micro-financing, consider niche, contextually relevant spaces like booking platforms for travel loans or wedding event planners’ websites for wedding loans. If you focus on commercial financing, prioritize platforms where businesses make significant purchase decisions, like commercial estate or equipment marketplaces. Also, avoid crowded multi-lender environments: an exclusive or premier financing position at a lower-traffic platform will ensure higher borrower engagement.
• Implement channel-specific analytics to track borrower behaviors and evaluate the channel’s financial performance (based on cost per loan, average loan size, approval rates — you know what I’m talking about). From my experience, general lending analytics software can easily accommodate these tasks. This way, you can rapidly identify conversion bottlenecks and adjust offer positioning before problematic trends impact portfolio quality. Like in the case of private placements, prioritize distribution partners that provide go-to integrations to avoid manual data capture and leverage continuous analytics.
• As I said above, quick loan origination is a must to ensure smooth CX and minimize churn. Automated application processing, borrower pre-qualification, and underwriting are essential for straight-through loan issuance. Implementing a robust software toolkit for handling these tasks is the very first step to success in embedded lending projects.

Balancing Traditional and Innovative Capital Raising Options

Whatever appealing these strategies are, it’s best to start small with pilot projects for 1–3 lending niches where the outcomes would be the easiest to measure. This way, you can quickly confirm the efficiency of new technology-enabled avenues and confidently secure budgets for rolling them out at scale while minimizing risks.

If you need advice on the best-fitting lending capital strategies and technologies for your case, feel free to contact me or other consultants at ScienceSoft.