
Non-Bank Lending
Client
The client is one of the largest multi-channel non-bank lenders, providing home, car, business and investment loans throughout Australia and New Zealand.
Brief
The brief was to develop a consolidation multi-entity planning model that allowed the group of companies to forecast out their near-term growth, strategy and treasury modelling.
The model suite needed to support:
- modelling of the front and back books of all business units;
- analysis of the operational growth and development of the front book;
- automated monthly historical-forecast as well as intra-month analysis;
- detailed treasury ruleset modelling to assess the funding options across the group;
- automated warehouse and SPV funding projections; and
- comprehensive three-way analysis across the different presentations of the business.
Solution
Our solution comprised of the following three models to facilitate the scale of the business, it’s data and reporting requirements:
- Data Compression file;
- Core Planning and Analysis model; and
- Reporting Book model.
The model suite was based around the following key modules:
- business channels – analysing the loan book and forecasting income down to net contribution;
- warehouses – to fund new loan generation in the first instance;
- SPVs – to bundle loans together and offer a fixed term deal funding opportunity to investors;
- Consolidated 3-way financials for a multitude of different companies within the parent; and
- A CFO-style one-pager dashboard that allows the user to flex key assumptions and perform what-if analysis in a clear and concise way.
Modano Benefits
From an end user perspective, the Modano software automated the following model development processes:
- monthly roll forward;
- adding an entire business channel;
- adding an SPV funding unit;
- creation of comprehensive board pack analysis including:
- business channel analysis;
- budget and prior year comparisons;
- non-financial metric analysis; and
- intra month daily non-financial metric analysis by business channel, whereby each day, updated data could be extracted from a data warehouse, imported into the model and used to analyse within the month whether targets are being met.
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