What's the actual success rate of lenders who implement better verification?

Is the Investment in Automated Verification Worth the ROI for Lenders?

The transition from traditional, manual underwriting processes to sophisticated automated verification systems represents a fundamental recalibration of how alternative business lenders and institutional executives manage risk and deploy capital. For decades, the subjectivity, high error rates, and lack of scalability inherent in human-led underwriting acted as a ceiling on growth.

The question for forward-thinking executives is no longer if they should adopt advanced verification tools, but what measurable returns these systems deliver in a highly competitive market. Data from firms implementing modern systems, which incorporate automated underwriting platforms (AUS) with specialised APIs for real-time data access (such as Secretary of State (SOS) checks, TIN verification, and integrated fraud detection), indicates a profound transformation across profitability, risk mitigation, and operational scalability.

Is the Investment in Automated Verification Worth the ROI for Lenders?

The business case for integrating enhanced verification is robust, resting on measurable efficiency gains and compounding cost reductions. Automated systems demonstrate superior economic performance compared to human-led processes, ensuring that the initial technology investment yields substantial long-term value.

1. Algorithmic Superiority Delivers Higher Profits: Sophisticated algorithmic underwriting has been proven to enhance core profitability, moving lending beyond traditional unit economics.
   
   • Research indicates that lenders implementing algorithmic underwriting achieved 10.2% higher loan profits compared to human-led processes.
   • The continuous refinement capability of Machine Learning (ML) models means underwriting quality improves over time without manual intervention, creating a compounding advantage.
   • Automated underwriting allows lenders to profitably serve market segments previously considered uneconomical, such as lower ticket sizes.
2. Dramatic Reduction in Operational Costs: Automating routine verification tasks directly converts time and labour into savings, allowing lenders to operate on leaner margins.
   
   • Lenders implementing AUS report an average 40% decrease in operational costs.
   • Cost savings are substantial across the financial sector; for instance, insurers implementing automation realised savings between 20% and 80% off previous underwriting costs.
   • Labour cost reduction is the most direct savings category, enabling institutions to process significantly higher application volumes without requiring proportional increases in underwriting staff.
3. Faster Payback Period for Strategic Investments: While technology implementation requires upfront capital, cloud-based and API-driven solutions offer rapid deployment that accelerates the path to positive ROI.
   
   • Simpler deployments using cloud-based platforms can demonstrate positive ROI much faster than full custom builds.
   • The core build time for integrating primary-source APIs, such as the Secretary of State (SOS) API, can take less than a week, drastically reducing implementation timeframes.
   • Even for major implementations, full automation projects typically achieve a payback period within four to five years.

How Does Automation Translate to Measurable Risk Reduction?

Better verification fundamentally shifts the risk equation by mitigating fraud and reducing default exposure, transforming risk management from a reactive function into a proactive, intelligence-driven strategic pillar.

1. Lower Default Rates through Precision Risk Scoring: Automated systems utilise advanced data analytics and algorithms to generate far more accurate evaluations of a borrower's likelihood to default.
   
   • Algorithmic underwriting leads to 6.8% lower default rates compared to manual processes, providing strong evidence of its risk mitigation benefits.
   • The systems combine diverse data—including cash flow analysis, credit history, and alternative data—to determine the probability of default, enabling smarter loan pricing and decision-making.
   • Confidence scoring is applied to quantify uncertainty, ensuring that highly accurate decisions proceed automatically while complex cases are flagged for necessary human oversight.
2. Fortifying Defence Against Sophisticated Fraud: Modern verification tools integrate real-time checks against authoritative primary sources, creating a multi-layered barrier against fraud and identity misrepresentation.
   
   • Specialised services like TIN Verification validate business names and Employer Identification Numbers (EINs) against IRS records in real-time, a critical step in combating identity spoofing and invalid applications. General Merchant Funding prioritises this verification to know they are dealing with a "verifiable business right from the door".
   • Automated systems detect inactive or dissolved businesses visually and confirm registered agent details directly from Secretary of State (SOS) websites, severely curtailing the risk of approving fraudulent applications.
   • The implementation of enhanced fraud detection models has helped digital-first lenders cut fraudulent activity by 40%.
3. Establishing Immutable Audit Trails for Compliance: The technology enables lenders to meet stringent Know Your Business (KYB) and Anti-Money Laundering (AML) regulations with automated, irrefutable evidence.
   
   • APIs provide timestamped visual proof, such as screenshots of the official state record, which serve as an irrefutable audit log accepted by many compliance teams in lieu of manual documents.
   • Data normalization converts inconsistent, raw data from different states into a predictable, standardised format (e.g., classifying various statuses as 'active' or 'inactive'), significantly simplifying the internal logic needed for KYB/AML workflows.
   • The precise verification of business start dates allows customers to cross "major milestones" required by funders (e.g., three years in business), leading to more competitive offers and approvals.

What Operational Efficiencies Fuel Scalable Growth?

The shift from tedious manual work to high-speed, automated processes eliminates traditional bottlenecks, drastically boosting the overall capacity and efficiency of the lending institution.

1. Revolutionary Speed of Decision-Making: Speed is a key differentiator in the modern lending landscape, meeting the borrower demand for quick turnarounds.
   
   • Automated underwriting cuts processing time from days or weeks to mere minutes or seconds.
   • Idea Financial achieves automated loan offers in 20 minutes or less, and even as fast as 30 seconds when pre-processed bank data is available.
   • Automation enables continuous operation, allowing marketplaces like 1West to service customers day, night, and weekends—a capability highly valued by referral partners.
2. Increased Processing Capacity and Scalability: Automation is the critical factor that allows lenders to achieve aggressive growth targets without the crippling cost of rapidly hiring and training large operational teams.
   
   • One SME lender was able to triple its loan processing capacity immediately after implementing automated underwriting.
   • Lenders can handle immense application volume; 1West’s Automated Business Lending Engine (ABLE) handles between 5,000 to 6,000 full loan submissions monthly.
   • For loan processors, integrating automated data retrieval eliminates manual lookups (e.g., SOS, EIN, judicial checks), freeing up processing teams and creating a "ton of bandwidth".
3. Streamlined Integration and Reduced Technical Overhead: Modern verification solutions are designed to integrate rapidly and rely on robust architecture, minimising long-term maintenance costs.
   
   • The core build for integrating verification APIs can be completed quickly; 1West's CTO estimated the implementation took "less than a week in terms of just like the core build".
   • Integration is supported by flexible protocols like callback URLs (webhooks) for asynchronous processing, preventing workflow stalls when querying slower state systems.
   • Systems employ data normalization and developer-friendly RESTful APIs to ensure seamless integration into existing CRM or underwriting platforms like Salesforce.

Does Enhanced Verification Improve the Customer Experience?

In competitive markets, the lender who provides the fastest, most transparent service wins the customer. Automated verification transforms the traditionally frustrating loan application into a seamless, digital-first experience.

1. Enabling Frictionless Customer Self-Service: Automation empowers borrowers to complete large parts of the application process independently, drastically reducing human friction.
   
   • 1West reports that 25% of its customers self-service their loans entirely through their platform without speaking to anyone from the company.
   • Borrowers simply log into online banking or payroll providers via integrated APIs to unlock necessary data in seconds, providing an added layer of convenience.
   • This rapid, seamless process enhances customer satisfaction and builds loyalty, making the digital lending experience a key differentiator.
2. Consistency Builds Trust and Reduces Bias: Automated systems ensure that every applicant is treated fairly, removing the subjectivity and inconsistencies of manual review.
   
   • By applying standardised underwriting rules universally, AUS minimises the risk of errors and subjective interpretation that can lead to compliance issues.
   • This consistent application ensures predictability and equitable treatment across all decisions, fortifying institutional reputation.
   • For high-risk or complex cases, automation flags the file for human oversight—retaining the necessary expert judgment while ensuring routine applications are handled instantly.
3. Gaining a Competitive Edge with Speed and Agility: The ability to move faster than competitors often determines market leadership.
   
   • Accelerated processing reduces applicant drop-off during extended decision periods, directly increasing conversion rates.
   • One lender using alternative data alongside traditional scores was able to offer 20% lower rates than when relying on credit scores alone, demonstrating pricing agility.
   • The operational efficiency achieved through automation is a major factor in increasing competition between emerging online lenders and traditional banks.