BankWest — Pricing Tool
Transforming Mortgage Pricing Processes through User-Centered Design and Automation
Bankwest, successfully optimized its mortgage pricing request and approval processes by leveraging user-centered design, automation, and data-driven decision-making. This case study explores how Bankwest transformed its pricing processes, resulting in significant improvements in efficiency, accuracy, user satisfaction and business outcomes.
Bankwest undertook a transformative initiative to streamline its mortgage pricing procedures through user-centered design and automation. This case study delves into how Bankwest enhanced efficiency, accuracy, user satisfaction, and business outcomes by redesigning its Home Loan Pricing Tool. This tool facilitates seamless pricing requests between Brokers and Bankwest, enabling swift submission and escalation for improved rates.
Facing a challenge with an overwhelming volume of pricing requests, Bankwest identified inefficiencies leading to frequent rejections and delays. To tackle these issues, Bankwest initiated a comprehensive redesign project focused on user research, data analytics, and machine learning. This approach aimed to create an intuitive and efficient tool that meets the diverse needs of brokers and internal teams involved in the pricing process.
Research Methodology
Workshops. Workshops based on the Value Proposition Canvas engaged internal teams and brokers to explore workflows, pain points, and enhancement opportunities. These sessions were structured to gather persona profiles, discuss Jobs-to-be-Done, and identify pain points and potential solutions.
Survey. An online survey followed the workshops, gathering quantitative data to prioritize features using MaxDiff analysis. This approach pinpointed critical issues and desired functionalities from brokers and internal teams, enhancing understanding across user segments.
Business Dashboard Benchmarking. Benchmarking data from business dashboards established baseline metrics for key performance indicators (KPIs) such as queue size and response times. This quantitative analysis provided insights into the current state of the pricing request process, guiding improvement targets.
Findings
Research unearthed significant challenges including a growing queue size, manual effort wasted on ineligible requests, and usability issues within the pricing tool's interface. These findings underscored the need for targeted design solutions.
Manual effort wasted on declining ineligible requests. The teams spend significant time and effort manually declining pricing requests that are ineligible based on current business rules. These include requests for business-managed customers, customers in collections or hardship, construction loans, customers who have already accepted a pricing offer directly with the bank, fixed-rate loans outside the eligible repricing window, and resubmitted requests with no change in details.
Usability and navigation challenges. The pricing tool's user interface lacks clarity, visual hierarchy, and intuitive navigation, hindering the teams' productivity and user experience.
Design Solutions
Based on the research findings, prioritization from the MaxDiff survey, and benchmarking data, the following design solutions were proposed:
Automated Filtering System. Implemented a rule-based system to automatically filter out ineligible pricing requests based on predefined criteria, reducing manual rejections and enhancing efficiency.
Intelligent Request Type Determination. Introduced a system to intelligently determine the appropriate pricing request type based on loan characteristics, minimizing errors and optimizing request accuracy.
Customer Tags and Status Indicators. Incorporated customer tags and status indicators for quick identification of crucial customer details, enhancing personalized service and workflow efficiency.
Redesigned Information Architecture. Streamlined information presentation with a redesigned IA, consolidating loan details and timelines for quicker decision-making and improved data access.
Scenario Creation and Comparison. Enabled brokers to create and compare multiple pricing scenarios, facilitating data-driven decisions and reducing iteration cycles.
Advisory Feedback Loop. Introduced real-time validation and advisory messages during request submission, guiding brokers to avoid common pitfalls and improve request quality.
These design solutions work together to create a more intuitive, informative, and streamlined Pricing Tool that empowers both brokers and internal teams to navigate the pricing request process with ease and accuracy. By incorporating customer tags, status indicators, and a redesigned information architecture, the tool provides users with the right information at the right time, enabling them to make informed decisions and deliver exceptional service to their customers.
Validation
Prototypes were validated through user stories and iterative feedback sessions with stakeholders and subject matter experts. This process ensured alignment with user needs and business objectives, refining designs for optimal usability and effectiveness.
Speed critique
The speed critique sessions provided an opportunity for rapid feedback and iteration, allowing the design team to quickly identify areas for improvement and make necessary adjustments to the prototypes. The insights gathered from these validation activities were used to refine the design solutions further, ensuring that the final product meets the needs of all users and addresses the key pain points identified in the research phase.
Results
The implementation of the proposed solutions, along with the insights gained from user experience mapping, heuristic review, and usability testing, yielded significant benefits for Bankwest. The following metrics demonstrate the measurable improvements achieved:
Processing Time
-67%
From 30 to 10 minutes
Pricing Decision Accuracy
+13%
From 85% to 98%
Manual Interventions
-80%
From an average of 25 an average of 5 manual interventions were required per 100 pricing requests.
User Satisfaction
+40%
From 3.2 out of 5 to 4.5 out of 5
Productivity
+60%
Before. The MST and HLD teams could process an average of 50 pricing requests per day.
After. The MST and HLD teams could process an average of 80 pricing requests per day.
These measurable improvements demonstrate the success of the optimization project in reducing manual effort, minimizing errors, enhancing accuracy, improving user satisfaction, and increasing overall productivity. The metrics provide clear evidence of the tangible benefits achieved through the implementation of the proposed solutions.
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