Spinwheel will be integrated into the Algebrik AI platform so that financial institutions of all kinds can access verified consumer debt information and leverage integrated payment processing in their lending routines.
Algebrik AI has partnered with Spinwheel, a developer of real-time consumer credit data AI and smart payment tools, to integrate real-time consumer debt data and payment tools directly into the loan origination process. This move aims to make loan applications faster and more accurate for lenders such as credit unions, community banks, and fintechs.
Spinwheel’s technology will now be built into Algebrik One, the company’s cloud-native, AI-based lending platform. This integration allows lenders to see a borrower’s current credit and debt balances using just a phone number and date of birth. It covers credit cards, student loans, mortgages, and more, removing the need for borrowers to upload documents manually.
Lenders can use this data to assess credit risk with greater accuracy, make decisions faster, and offer more tailored loan options. Built-in payment capabilities also allow borrowers to pay down existing debts or transfer balances as part of the application, helping them manage obligations more easily.
The partnership reduces errors, speeds up approvals, and improves compliance, while giving borrowers a smoother and more transparent experience. For lenders, it means stronger decision-making, better customer engagement, and improved operational efficiency.
AI-powered automation is one of the main drivers of progress in alternative lending practices, called to fill in the gaps created by traditional credit scoring and loan-linked routines.
When lenders lack real-time consumer debt data, they rely on manual document collection and outdated credit reports, causing delays and errors in borrower data. In addition, credit decisions are then based on incomplete or stale debt information, weakening risk assessment and potentially leading to mispriced loans. Nevertheless, only around 10% of banks reported having credit‑scoring systems that automate underwriting fully in 2024, revealing a major need in real‑time data usage.
