Lender Decisioning Engine

Enabling lenders to design, manage, and scale their own credit policies

Overview

The Lender Decisioning Engine was designed as a self-serve back-office tool that allows lenders to create, manage, and evolve their own lending policies and decision rules. The aim was to shift from a developer-dependent model—where every policy change required engineering support—to a more scalable, product-led approach that empowers lenders with direct control over their decisioning logic.

By bringing policy configuration into the product experience, the platform enabled lenders to respond faster to market changes, test new strategies, and tailor their risk models without operational friction.

My Role

Product Designer

I led the design of the decisioning experience end-to-end, including:

• product discovery and lender research

• workflow and interaction design

• prototyping and usability testing

• defining patterns within the Back Office Design System

• close collaboration with engineering and product teams

The Challenge

The problem space was inherently complex. Each lender had different internal processes, risk models, and ways of defining rules, making it difficult to design a one-size-fits-all solution.

At the same time, the existing internal tool—used by developers to configure policies—was difficult to use, inconsistent, and inefficient. It contained fragmented workflows, unclear logic structures, and numerous dead ends, making even simple updates time-consuming.

The challenge was to design a system that could:

• support complex rule logic in an intuitive way

• accommodate different mental models across lenders

• reduce reliance on engineering teams

• remain scalable and adaptable as lenders’ needs evolved

Research & Discovery

To fully understand the problem, we approached discovery in three layers.

First, we worked directly with lenders to understand how they create and manage policies in their own environments. Through interviews and on-site visits, we observed real workflows, uncovering patterns in how rules were defined, tested, and deployed. This helped identify common pain points despite variations in processes.

Next, we audited the existing internal decisioning tool, working closely with developers to understand its limitations. This revealed significant usability issues, redundant functionality, and inconsistencies that needed to be addressed before externalising the product.

Finally, we explored rule engine tools available on the market, analysing how complex logic systems are structured and visualised. This helped us identify interaction patterns and define principles for building a more intuitive rule-building experience.

Design Approach

Based on these insights, we defined a direction focused on simplifying complexity without removing flexibility.

Early concepts were explored through sketches and collaborative workshops, where we aligned on the core interaction model for rule creation and management. From there, I developed wireframes and interactive prototypes, allowing us to test the flow with lenders and validate whether the system matched their expectations.

Using clickable prototypes in interviews proved particularly valuable, as it allowed users to engage with realistic scenarios and helped us quickly identify friction points in the logic-building experience.

Design System & Visual Development

The decisioning tool was developed as part of the broader Lender Back Office ecosystem, using and extending an existing design system I had established.

As the product evolved, we introduced new components and interaction patterns specific to rule building and decision logic, which were then incorporated back into the design system. This ensured consistency across the platform and enabled faster development and iteration.

Outcome & Impact

The decisioning engine marked a significant shift towards a more scalable and product-driven operating model.

Lenders were able to:

• independently create and manage their policies

• respond more quickly to changes in risk and market conditions

• reduce reliance on engineering teams for operational updates

The product was well received and adopted by the majority of lenders, evolving through continuous feedback and iteration. Over time, additional functionality was introduced to support more advanced use cases, further strengthening its role as a core platform capability.