In the previous post, we discussed the quality improvement of credit decisions in the loan origination process. Now let’s understand one of the initial processes which is essential for borrowers – assessing their borrowing capacity.
Knowing the borrowing capacity before starting the process of going for a mortgage provides a guide to what a customer can buy. Lenders need to be able to assess borrowers’ strengths thoroughly depending on their financial strength and credit history.
Determining the borrowing capacity before lending reduces the potential risks for the customer and lender; hence, the assessments and calculations take a considerable amount of time. In this step, a lender gets a chance to know about the capacity limitation. It helps them to assist the customer to make a borrowing decision while mitigating any risk to the customer. In the end, they can make an informed borrowing decision.
The following chart is from Deloitte’s report ‘The Value of Mortgage Broking’. It shows that the mortgage lenders spend approximately 36% of their time during the application process for the initial assessments. That is around 1/3 of the complete process, just to verify data and determine borrowing capacity.
Moreover, these assessments contain different business rules for different lenders making the brokers spend more time on understating various lender requirements before the product recommendation step. Therefore, the report also says, “Brokers typically invest a significant amount of time and effort in pre-screening applications and matching customers to lenders and their products, commonly resulting in applications that are more likely to be approved by a lender.”
So how can we create a solution that calculates the borrowing capacity accurately, and can be customized according to different lenders as well as business rule changes? How can we create a faster, more seamless, and consistent experience for borrowers, lenders, and employees?
Estimating the Borrowing Capacity
Let’s see this example of a home loan calculator created using FlexRule’s Decision Automation Platform which gives an estimation of how much you could borrow based on your lifestyle after the initial assessments.
Of course, there are several parameters and decisions in an actual calculator. This is only a simplified version that takes the customer details including the income and expense data as an input and shows the amount that can be borrowed as the output.
In the following Decision Requirement Diagram (DRD), you can see the decision hierarchy.
- First, calculate risk score based on personal data, earnings, and expenses.
- Then calculate total income.
- Finally, based on risk score and total income, calculate the borrowing capacity.
It is clear to understand the dependencies of decisions. Each decision can be justified by making the complete hierarchy transparent.
We have used decision tables and natural language to get risk scores according to applicant data.
Decision table to score according to personal data
Natural language to score according to the earnings
Being able to interpret the business rules in a human-readable way makes it easier to update upon the changes by both business and technical users.
Moreover, adopting a decision automation platform has proven benefits to financial institutes. Wisr, a consumer lending service mentions their intelligent credit decisioning process could respond rapidly to COVID-19 and implement changes accordingly. Therefore, its revenue performance was up by 92%, from $21.9 million in the previous corresponding period to $42.2 million.
Advanced decisioning tools can enable lenders to use tailored pricing and preferential packages to target specific segments or loyal customers, helping to address issues around customer retention and undifferentiated products.
Accenture: The future of mortgage decisioning is taking shape today
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Conclusion
As Forbes mentions, the experts predict that people will have more confidence to sell their properties and the low mortgage rates encourage the buyers to buy more properties, increasing the competition among mortgage brokers and lenders. They also say that there will be an annual sales increase from 5% in 2020 to over 10% in 2021. With this competition, the lenders must keep up with the market challenges. An end-to-end decision automation platform not only ensures a precise borrowing capacity determination and streamlined loan process but also the ability to provide the best service to the customers and minimize the risk of a profit-loss. It gives a competitive edge to the lenders to meet customer demands at speed on a real-time basis while staying productive and agile.
Last updated June 16th, 2022 at 03:52 pm, Published March 25th, 2021 at 03:52 pm
Product Engineer at FlexRule – She focuses on analyzing and understanding customer requirements and identifying process. She also works in the emerging areas of technology such as Decision Robotics, testing and ensuring that the product functionalities align with the customer requirements.
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