Case Studies

Consumer Online Credit

June 20, 2020


  • 53% increase in acceptances
  • 72% reduction in default rate
  • 97% reduction in manual effort
  • 82% reduction in fraud rate


A major, UK, consumer finance company, focused on sub-prime, on-line lending. The client was an early, fintech innovator experiencing problems in handling its rate of growth.

The client’s existing systems were constructed using mainly traditional banking components, e.g. decision engine, range of credit/default scorecards, loan management system, CRM and general ledger system etc. The client was collecting a limited range of predictive data.

Whilst the client was using well proven banking modules, in the context of on-line lending they lacked functionality and imposed a number of restrictions. The main issues were,

As a result, portfolio performance was sub-optimal and operating costs per loan were high. Plus, whilst market response was good, the system was unable to cost effectively cope with portfolio growth.

Zoral software used

Zoral decision engineZoral DE
Zoral model libraryZoral ML
Zoral analytical data workbenchZoral ADW
Zoral behavioral data warehouseZoral BDW
Zoral dynamic customer journeyZoral DJ


The subprime lending portfolio base KPIs required improvement. Zoral DE and Zoral ML were used to rapidly construct a parallel decisioning system to baseline the client’s current KPI’s. These were,


After the baseline KPIs were calculated, an enhanced customer journey was constructed using Zoral DJ, Zoral BDW and Zoral DE intelligent workflows. This captured additional, predictive data and significantly improved data collection and quality. A further range of additional predictive data was captured over a two month period. This included behavioral, social, device, and bureau data.

Zoral ML default, fraud, retention, limit management models were tuned on an enhanced range of predictive portfolio data. This was done using Zoral ADW, advanced feature engineering techniques, and robo-underwriter. The following were automated, using Zoral DE intelligent workflows, and Zoral ML,

Initial results

After three months, the client’s subprime lending portfolio KPIs improved as follows,

The client’s profitability and scalability rapidly improved. However further optimization was required for new customer acquisition costs, defaults, limit management, acceptance rates.

Plus manual processing needed to be further reduced.

Subsequent results

Zoral: consumer online credit scoring, UKZoral ML models and workflows were further tuned after four months’ data collection and a number of A/B testing initiatives,. A number of additional models and intelligent workflows were introduced, including,

These supported automated, optimized customer lifecycle management.

Also, using Zoral ML and Zoral DE, intelligent automation was applied to,

The results of implementing Zoral Platform components are summarized below:

KPIPrior to using zoral platform toolsAfter 5 monthsAfter 12 months
Average loan size£260.00£310.00£540.00
Number of online / mobile applications processed & approved per day7050010K+ per day (subsequently daily volumes peaked at over 100K per day worldwide)
% of manual processing76%15%< 2% (some manual intervention retained due to regulatory requirements)
Operating costs per new loan£8.30£2.80£1.30 (primarily data acquisition costs)
Operating costs to provide another loan for an existing customer£2.50£0.60£0.40
New customer average acquisition cost£42 (non-scalable)£126 (scalable, but expensive)< 12% of loan face amount, across all digital channels
Retention marketing cost for each portfolio customer identified for retention£2.20£0.34£0.21
Portfolio default rate28%18%Sub 8%
Acceptance rate new customersEstimated at 19%25%29%
Customer Retention Rate51%78%87%
Fraud rate6%2%< 0.1%
SummarySmall innovative finance company, sub-optimal KPI’s. platform not scalable, high operating costs per loanImplemented zoral platform robo underwriter and intelligent STP. Increased profitability, significantly improved scalability and core KPI’sOne of the most successful financial (Fintech) startups in Europe. Low operating costs, intelligent robo underwriter, effective digital targeted marketing, optimized customer lifecycle management £400+ mil online revenue