Analytics are available by clicking the icon on the left side of the Dashboard:

Visuals can be filtered by:

Some visuals can be toggled between number of decisions and value of decisions. Click this button to change that view:

The data returned is for hard credit checks only. These were applications actually made. Soft checks are (currently) excluded because they may result in an accept, but the applicant doesn't continue with the application. Including soft checks would therefore inflate the value of accepted loan decisions.

To make informed adjustments to risk appetite and business plans, it is important to assess loan book growth when reviewing financial performance

These donuts show the number and value of decisions over the chosen period. Clicking a type of decision will removes it from the donuts.

In the example below the pass and fail types are excluded, removing ID-only checks from your analysis.

Decisions may be viewed over the time period selected. In the example below a day to day view was required, therefore the date range was last month (February):

Longer-term trends are useful to assess performance, for example over a one year period:

Tip: Compare the value of accepts to your business plan and adjust projections. If the accepts are below target, consider whether other products with higher accept rates might fill that gap. Fine tune marketing efforts accordingly.

This section is designed to help you understand how you're growing.
​
​Planning marketing campaigns and evaluating market trends, helps you understand the demand for different types of loan. As a result, you can tailor products or marketing efforts (for example, on a seasonal basis)

The visuals provide an overview of loan purpose and show this over time. You can toggle between value and number.

Clicking on different loan purposes helps identify trends for specific types of loan. In the example below, car loans are in demand in the early part of the year and late summer:

Decisions can be viewed by geography and sorted by the outer postcode area or value:

Tip: Examine trends by loan purpose to identify seasonality. If certain types of loan are in demand at specific times of the year, focussed timing of marketing campaigns will maximise take-up.

This section helps you assess whether growth is within your risk appetite
​
​When reviewing loan applications, you'll want to know the proportion of accepts, refers, and declines across different risk brackets to better align decisions with your bad debt tolerance

This chart provides an overview of the value of decisions by accept, refer or decline using different credit profiles (very poor, poor, fair, good and excellent).

Note: if you're using soft credit checks this chart will include refers as not all borrowers will submit an application.

The table below summarises key risk indicators displaying the average:

Viewing decisioning rules alongside the final decision enables you to identify when certain rules almost always result in an accept or decline.

In the example below, around two-thirds of referrals result in a final decline decision. Logically, one third are therefore accepted. As a result those refer rules should be maintained:

In this second example, however, the refer rules for bankruptcies in the last 24 months and six years almost always result in a decline. On this basis, changing that rule to a decline would result in more automation:

Furthermore these visuals ensure that automated decisions are not being overridden. In the example below, sufficiently few decline reasons are overridden to be able to fully automate declined applications.

Tip: automated results may vary from product to product. For example, a recent bankruptcy may not be an issue for a Family Loan but might be significant for consolidation borrowing. Adjust rules by product to compensate for these variations.

When evaluating your processes, you will want assess the efficiency of automated decisions, so you can improve accuracy and reduce manual interventions.

The first visual is called a Sankey Chart. These are used to understand the 'decision journey' from one state to another. Click on any bar to reveal the number or value of decisions that move from one state to another. Results can be filtered by clicking on a status to remove it from the chart:

These charts can be used as a measure of efficiency. In the example below we can see that a clear majority of referred loan applications are declined. This presents an opportunity to speed up loan decision making by examining refer reasons and changing these to auto declines.

This final chart is loan turnaround time. You can select:

For example if a loan application arrived on a Tuesday at 16:00 and the decision was finalised on a Wednesday at 15:00, the elapsed hours would be 23. However the working hours would be 7.