Creating a SaaS lending solution that provides credit score services and connects borrowers with banks
Our client is a FinTech company that provides SaaS lending solutions for borrowers and banks. Our client’s product helps end customers meet the financial needs of their private businesses by offering financial services including credit scores, wide lines of credit, merchant cash advance, equipment borrowing, and business loans.Websites are tricky things to get right, especially SaaS websites in which the primary goal is to convert visitors into customers.
Our client had experienced limitations with their in-house engineering capacity, which is why they had been looking for an engineering partner to develop SaaS lending software. They found one a year ago but eventually stopped cooperation with the vendor because of quality and scheduling issues.
Our client needed to move fast to avoid delays and was looking for an experienced partner with deep expertise in FinTech and SaaS platform development that could grasp their idea quickly. They needed to continue development and eventually increase productivity to catch up with a backlog remaining after the previous vendor; after all, they still needed to develop both backend and frontend parts of the solution.
All of these statements not only make it clear in a couple of seconds what the product does, and how it differentiates from other brands in the same market, but they are also following SaaS website best practices by remaining above the fold on the homepage in a clear and concise manner.
The cooperation started with three engineers on our side, but soon the Intellias team had grown twofold – and it still continues growing to deliver client’s SaaS lending platform. Our team is involved in most engineering processes and is covering the entire backend development for the SaaS lending solution. This solution collects data from borrowers about their credit history, other loans, and the business in which they will invest. Ultimately, our client’s lending solution is a bridge between individuals who are looking for funds to grow their businesses and established banks that can provide money to meet their financial needs.
After forming a comprehensive borrower profile, the system performs the first round of borrower qualification and calculates the credit score of each borrower. The process of a new borrower onboarding and verification ends up with the primary decision on the user’s access to the system – yes or no. If a potential borrower’s credit score matches predetermined criteria, the loan request is redirected to a bank that can make a verification call if needed.
We’ve also developed AWS-based fault-tolerant databases to get the most useful insights from data collected on borrowers and their businesses. Then we decided to use collected data as a basis for the machine learning component. We use it for the calculator to make data-driven decisions on loans. The client plans to use this machine learning functionality for small amounts of loans to give money automatically based on borrower’s data and ML algorithms.
SaaS website best practices might sound simple enough, yet they are often overlooked altogether, especially by startups with little or no experience optimizing a website to increase customer conversions. These are just the basics every website owner needs to be aware of, but following these best practices alone won’t be enough to truly optimize your SaaS website.
Optimizing for conversions means continual testing and adapting to fit the changing needs of your customers and the wider market. By focusing on the foundations for a good website such as voice of customer data, social proof, and a strong value proposition, you can build a robust website with high SaaS conversion rates that’s primed to be the best salesperson possible for your business.
Now that the first release has been successfully delivered, we’re continuing to improve the solution. We’re preparing updates for user reports and, most importantly, improving visualization of collected data so it can be used for more insightful decision-making. Our collaboration is growing as we fix bugs and deliver new features in addition to providing continuous support for the released version of the platform. The system works smoothly and provides business owners with instant loans so they can realize their ideas. The next steps we have planned include implementing machine learning algorithms to improve the speed and usefulness of credit scores.
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