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How to build an effective decision workflow and implement it quickly
uFlow · November 3, 2022 · 2 min read
Learn what a decision flowchart is, how to create one and how to implement it with a decision engine to process thousands of credit applications.
A flowchart is a visual representation of a process as a series of interconnected boxes. Flowcharts are ideal for documenting processes, decision trees and other kinds of logical operations. When it comes to making credit decisions, having a polished, effective process matters.
What is a decision flowchart?
It is a specific type of flowchart that visually describes the decision-making process in relation to different variables: an applicant's income, payment history, compliance and so on. These variables shape the final outcome: approval or decline, amounts, repayment terms.
Why should you build a decision flowchart?
It helps you rule out alternatives quickly and more objectively, focus on the most relevant factors and weigh them against each other. The result is better decisions in less time, and it becomes easier to communicate and collaborate with others.
How to build a decision flowchart
Start by identifying the problem or the applications you need to resolve. Identify the variables that influence the decision, along with their pros and cons. Once you have all the information, draw the chart: sketch the main box (the decision) and add boxes representing the variables, connecting them with arrows. The uFlow visual editor is one of the most intuitive on the market.
How to implement a decision flowchart
Implementing it is easy when you handle few applications. But what happens when you get 10,000 applications an hour, with dozens of variables? That is when the odds of human error spike and you need a decision engine, which can process far more applications per hour with no margin for error.
How does the uFlow credit decision engine work?
Another advantage is how flexible and fast it is when you need to make changes. It can incorporate any data source and modify existing models, adapting instantly to new rules. Machine learning and AI keep your lending business running, whether you work with 10 variables or 1,000.
Conclusion
Thanks to AI, machine learning, data sources and No-Code editors, the future of lending businesses lies in implementing decision engines to process thousands of applications in seconds, reducing the margin for error and response times.