The credit decision stage is the procedure of analyzing the creditworthiness of a potential borrower and determine how much can be lent. The entire credit decision-making process involves several assessments and risks analysis steps. Based on all the results of these initial analyses, finally, the financial institutes decide on how much can be lent to a certain organization or an individual.

The creditworthiness of an applicant is analyzed based on credit history, capacity to repay, capital, the loan’s conditions, and associated collateral – the Five Cs of Credit. This elaborate analysis depends on both quantitative and qualitative parameters, requires a digital decisioning system that can factor in these intricate details of a credit decision, end-to-end.

Challenges with the Current Credit Decision-making Process

Financial institutions face rapid changes in the credit risk environment especially, after a crisis like a recession or pandemic. These changes could pose major challenges for them such as customer dissatisfaction, high-risk rate, loss in profits.

Changes in creditworthiness at sector and subsector level is one of the five major changes to the credit-risk environment triggered by the pandemic crisis.

McKinsey & Company

According to FED Small Business Credit Survey 2020, “Bank applicants were most dissatisfied with wait times for credit decisions”. It shows 25% of applicants of large banks are not satisfied with the long waiting period.

challenges with lenders-credit survey

It also shows that a significant number of applicants are not satisfied with online lenders due to their high cost of the interest rate. This could potentially be due to the lack of accuracy and transparency in risk analysis. Providing an accurate personalized interest rate according to risk analysis can solve this issue which we have explained in this blog post on credit origination.

Hence, the credit decision-making process cannot be a hit and miss anymore. The need for an organization’s preparedness and ability to adjust and respond to the changes with accuracy, precision, and speed calls for end-to-end decision automation to manage and execute credit decisions, efficiently and effectively.

For instance, estimating the borrowing capacity of a potential customer is the cornerstone of the credit decision stage and credit origination. Let’s see how to create a solution that calculates the borrowing capacity accurately, and can be customized according to different lenders incorporating business rules changes.

Decision Model for Estimation of Borrowing Capacity

Let’s see an example of a credit decision using a home loan calculator which gives an estimate of how much you could borrow based on your lifestyle after the initial assessments.

Home loan calculator

We used FlexRule™ Advanced Decision Management Suite to build this calculator. It is 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 this example,

  1. We first input and validate applicant data.
  2. Provide a score according to personal data, earnings, and expenses.
  3. Calculate the burrowing capacity according to the given score.

decision model - loan calculator

We have used decision tables and natural language to add score according to applicant data. Therefore, if the business rules can be easily updated upon any change.

Decision table - scoringDecision table to score according to personal data

Now let’s discuss the benefits of this solution.

Advantages of Modeling and Automating Credit Decisions

There are several benefits of automating this process and providing a customized output.

1.      Saves time

The multi-step process of assessing the applicant contains a lot of document filling and verifying, and calculations. This takes a lot of time to be done manually. Therefore, automating this process can save time.

2.      Improves accuracy

There can be various formulas and calculations involve in this process. Giving a risk score and calculating the final borrowing capacity accordingly are some examples of that. An automated solution can do the calculations by retrieving data from various sources improving the accuracy.

3.      Easy to Update 

Each lender has its own set of business rules to assess an applicant as well as calculate borrowing capacity. These rules also can change time-to-time depending on various facts such as government regulations, lender policy changes, etc. Decision tables and the flows we used can be updated quickly to reflect those changes.

4.      Easy to Understand for Both Technical and Business Users

Lenders can easily update and maintain the business rules without having to know a specific technology or programming language. They can easily understand the decision tables which are similar to Excel spreadsheets or natural language which is closer to the spoken language. Also, the technical users have more options such as connecting to databases and query data.

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Conclusion

Costly, unreliable, and time-consuming credit decision stage put the financial institutes in danger of loss of profit and leave unsatisfied customers. These issues can be both long-term and short-term challenges that can be solved using an end-to-end decision automation technology. It ensures that you provide the best-personalized service to the customers, minimizing the risks, saving your time while giving an accurate calculation.

Published January 28th, 2021 at 08:26 am