Liftfund chooses 2River and LIFT to update automation and analytics for their in-house lending systems.
The team at 2River has great passion and dedication to technology and customer service. From start and throughout a project they journey with you and keep you informed, provide solutions, and execute a clean, professional and quick manner…punctual clear and concise great guidance and journeying for supporting project understands complex workflow thorough quick respectful.
— Celina Pena, Chief Program Officer, LiftFund, Inc.
LiftFund is a nonprofit lender serving small businesses in 13 states. Over the past 20+ years LiftFund has made more than 19,000 business loans for over $250 million dollars. What began as a three-person office in 1994 has grown dramatically to meet the needs of underbanked entrepreneurs. In 2015 LiftFund processed 2600 loan applications, of which 1,365 became loans.
LiftFund has a proprietary risk model of thousands of client files from loan application to loan completion. Every application and loan contains data to help LiftFund build on historic knowledge to support their lending.
Shopping for credit has become an “on line” activity. The wave of venture capital into technology-based lending companies has raised the bar of what small business customers expect of their online borrowing experience. Many non-profits lenders are unable to create the brand presence and customer friendly experiences that small businesses have come to expect.
Over a two year period LiftFund wanted to update its web-based online application and create the capability to process automatically all applications through its risk model and return preliminary approval within minutes of applying.
The online application is a critical component of LiftFund’s business model. In the competitive lending environment, LiftFund wanted to enhance its online application to cultivate, retain and provide capital to entrepreneurs who are underbanked. LiftFund also wanted its risk model to “re-learn” overtime as borrower’s behavior changed.
LiftFund has decades of historical data assessing the risks of extending credit to businesses of all sizes. LiftFund also has an experienced team of underwriters. Their goal was to develop credit-decisioning models that they could apply to their current business and that would enable them to move into new markets.
Why 2River and LIFT
LiftFund partnered with 2River to enhance their proprietary lending system and to integrate it with 2River’s LIFT platform. The project was completed in two phases in 2016 and 2017.
Based on our consulting heritage 2River takes a unique approach to build models and analytics systems. Our methodology includes a change management process that taps into LiftFund’s experience and culture to develop and implement technology initiatives.
2River conducted detailed interviews with LiftFund’s internal IT and credit analyst team. This created a foundation of the data and methodologies that LiftFund uses to assess credit risk. 2River benchmarked LiftFund’s approach and data with 25 other commercial and non-profit online lending applications based on our Online Loan Application Benchmark Database.
Within the first phase, 2River develop a web-based loan-application and document collection system. This created a more engaging experience for the borrower and provided a mobile solution for LiftFund’s customers. This also facilitated more automated follow-up to close loans.
As part of this first phase, 2River integrated MS Dynamics with LexisNexis’ Verification and Fraud Prevention Services (InstantID and Business InstantID), Plaid (APIs that facilitate the exchange of banking data), and PolicyMap (data service that identifies borrowers’ characteristics based on geographic location).
In the second phase, 2River integrated LiftFund’s lending system with 2River’s LIFT platform. By integrating with LIFT, LiftFund is able to combine different data sources and easily access advanced AI capabilities contained within the platform for risk and opportunity scoring.
The new system provides real-time, consistent credit-decisioning for opportunities. Through LIFT’s “micro-service style” architecture, LiftFund will also be able to expose their proprietary risk model securely to their partners as an API resource.
LIFT allows LiftFund to build and manage their proprietary risk model in a fully automated web-based system that connects in real-time with the LiftFund’s operational systems and several 3rd-party data providers.
In the first full year after deployment, LiftFund saw a significant year-over-year increase in loan volume.
LiftFund’s updated lending system integrates online loan application data, financial and verification data services and risk analytics. These automated workflows allow LiftFund to improve conversion rates and decision times. LIFT’s built-in analytics models and extensive REST APIs allow LiftFund to extend and embed analytic content into their business processes and system of record.
2River and LIFT help LiftFund accelerate the time to create value for IT projects supporting online lending because the because the system is secure in it’s handling of data, data on-boarding is easy through standard APIs, and templates for many analytical models are pre-built.
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This guide introduces key concepts and simple examples to determine if automation is right for your credit decisioning.