Credit decisioning is the procedure of analyzing the income, expense, and credit data of a potential borrower and determining the eligibility of borrowing along with how much can be lent. This is where organizations can benefit tremendously from using a credit decision engine.
Since this entire process involves several steps, assessments, and decisions, it comes with a list of challenges.
- Takes a longer time to make decisions
- Prone to errors due to complex calculations
- Impossible to adapt quickly, keeping up with the regulation changes
- Hard to maintain compliance
As a result, you will end up wasting a lot of resources, time, and cost having a negative impact on your organization in a fast-phased business world. Fortunately, the traditional approach of credit decisioning can be overhauled using a credit decision engine.
First, let’s see an example.
Determining the Borrowing Eligibility – An Example of Credit Decisioning
This example was built using FlexRule™ Advanced Decision Management Suite. It shows the decision hierarchy of first home buyer credit decisions. Based on the income, expenses, and credit score, we can determine whether this applicant is eligible to get a loan. You can clearly see how each decision was made and what are the business rules behind them to meet the final outcome of credit decision for a first home buyer.
Let’s say government regulations changed and you need to add more decisions. Without breaking the system by making major changes to the entire solution, you can simply add a decision node to this with its calculations.
The following figure shows how to determine efficiency status using a simple decision table.
How Credit Decisioning can be Improved with Credit Decision Engine?
There is a number of advantages of automating credit decisions. It removes a lot of manual tasks which require a long waiting period out of the process. That means less time to finalize the decision, making the complete procedure more efficient. Efficiency always brings more revenue to the organization.
It also provides great transparency at each step. For example, the above sample DRD (Decision Requirement Diagram) maps the hierarchy of decisions. You can clearly see how each decision is made and the reason behind it.
Being able to maintain the systems keeping up with new regulations, adapting to new changes is another benefit. You can scale as necessary without shattering the existing system.
All these advantages of a credit decision engine always bring more profits to the organizations enhancing efficiency, scalability, and reliability.
Adopting automation can boost profitability and increase customer satisfaction by cutting costs for complex, unreliable, and error-prone processes. Further, it helps to maintain compliance aligning with business goals. As a result, you will have more time, effort, and cost to invest in more important innovations. With the help of a credit decision engine, you can transform business rules into profitable decisions.
Last updated November 10th, 2021 at 12:59 pm, Published October 7th, 2021 at 12:59 pm