Saas Landing Solution for Loans


Our client is a FinTech company that provides SaaS lending solutions to borrowers and banks. Our client’s product helps end customers meet the financial needs of their private businesses by providing financial services including credit scores, broad lines of credit, merchant cash advances, equipment loans and business loans. Websites are tricky things to do right, especially SaaS websites where the main goal is to convert visitors into customers.


Our client had experienced limitations with their in-house engineering capability, which is why they sought an engineering partner to develop SaaS lending software. They found one a year ago, but eventually stopped cooperating with the supplier due to quality and timing issues.

Our client needed to scale quickly to avoid delays and was looking for an experienced partner with deep expertise in FinTech and SaaS platform development who could quickly grasp their idea. They needed to continue development and possibly increase productivity to catch up with the remaining backlog after the previous vendor; after all, they still had to develop the backend and frontend parts of the solution.

All of these statements not only tell in seconds what the product does and how it differs from other brands in the same market, but they also follow SaaS website best practices by staying above the fold on the product page. welcome in a clear and concise manner.


The cooperation started with three engineers from our side, but soon the Kensist team doubled – and it continues to grow to provide the customer’s SaaS lending platform. Our team is involved in most of the engineering processes and covers the entire backend development of the SaaS lending solution. This solution collects data from borrowers about their credit history, other loans, and the business they will be investing in. Ultimately, our client’s loan solution is a bridge between individuals seeking funds to grow their businesses and established banks that can provide cash to meet their financial needs.

After creating a complete borrower profile, the system completes the first round of borrower qualification and calculates each borrower’s credit score. The process of onboarding and verifying a new borrower results in the main 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 who can make a verification call if necessary.

We have also developed fault-tolerant databases based on AWS to obtain the most useful information from the data collected on borrowers and their companies. We then decided to use the collected data as the basis for the machine learning component. We now use it as a simulator to make decisions based on historical loan data. The client plans to use this machine learning feature for small loan amounts to automatically give money based on borrower data and ML algorithms.


SaaS website best practices may seem simple enough, but they are often completely ignored, especially by startups that have little or no experience optimizing a website to increase customer conversions. These are just the basics that every website owner should be aware of, but following these best practices alone won’t be enough to truly optimize your SaaS website.

Conversion optimization means continuous testing and adaptation to adapt to the changing needs of your customers and the market in general. By focusing on the fundamentals of a good website, such as quality customer data, social proof, and a strong value proposition, you can build a robust website with high SaaS conversion rates that is built to be the best possible salesperson for your business.

Now that the first version has been successfully delivered, we continue to improve the solution. We are preparing updates for user reports and, above all, improving the visualization of the data collected so that it can be used for more informed decision-making. Our collaboration grows as we fix bugs and provide new features in addition to providing ongoing support for the released version of the platform. The system works well and provides business owners with instant loans so that 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.

  • client: FinTech Company (NDA)
  • Location: France
  • Surface Area: Fintech
  • Architect: PoC Development
  • Year Of Complited: 2021
  • Project Value: 20K