Seven sets of analytics are provided as standard:

All of these appear in the analytics tab. 

Analytics can be viewed by different date ranges; this month, last month, this year, last year and the current billing cycle.

The three-digit number provided is the average credit score for all applications received over the chosen time period. 

The y axis on the left shows the automated decision. The bars show changes to a decision after was review by a Loans Officer. 

Hovering over the charts reveals a tool tip, providing further insight. The example above shows that most declines remained as such. Only 10% (15 decisions out of 149) were changed to accept. The accepts had a value of £42,488. The final three digits are the average credit score. In this case the average credit score for declines to accepts decline is 500. This suggests a bad rate over close to 15%. However the credit union has recorded that these decisions were made on the basis of a loan being repaid by 

The decisions journey helps ensure that decisions being made are in line with risk appetite. If accepts are being overturned a review is recommended. Most decisions should remain red as declines. 

This is a simple count of the number of loan decisions taken over the selected date range. 

This is an overview of auto decisions by the top post code areas. Hovering over each bar provides a tooltip number of loans under consideration for that area by accept, decline or refer. The total value of those loans is displayed together with average credit score. 

Looking at the historical data helps predict likely bad rates by postcode area.

If you’re running a marketing campaign in a post code area, the data tells you the proportion of accept, refers and declines together with average credit score by district. 

As a result, you can align your marketing campaign with your bad debt budget. A higher proportion of refers and accepts compared to the overall average suggests the geographical area you are targeting aligns well with your product and risk appetite. 

The donut is a breakdown of the applications received by credit score. Hovering over the slices reveals the number and value of loans in that score range.

The credit score used by NestEgg is derived from TransUnion. The range is 471 – 770.

The breakdown is related to the policy rules. But these will vary from lender to lender. Consequently, the ranges provide an estimated level of bad debt:

A bad rate of 10% does not mean you will write off 10% of those loans. Furthermore, if the loan is being repaid via payroll deduction or child benefit your repayment methodology will reduce the bad rate. 

These bad rates are for the general population, not a specific credit union.

About bad rates

Bad rates are not the same as write off rates. A loan is considered ‘bad’ if the credit account information for the borrower shows the:

Whilst the insolvency will result in the debt being written off, a status 3 account might get back on track. Nor does a CCJ on the account mean that the loan will stop being repaid; the CCJ might relate to another account. 

You may find our guide on credit scoring useful. 

The donut breaks down applications received by purpose. Hovering over the slices reveals the purpose, total value and average credit score of applications.

You can use this data review if certain types of loan application result in higher or lower credit scores (and therefore bad rates). It is useful to see if this varies over the year. If a type of loan product results in lower quality applications, then you might focus your marketing spend on the types of application with better credit scores.. 

Finally there's an overview of your billable usage during the selected period. This is an additional way to reconcile invoices from NestEgg.

*this is a monthly equivalent, the actual number of payments missed will depend on the repayment schedule, but the borrower is at least 90 days in arrears for repayment schedules that are weekly, fortnightly, four weekly or monthly.