Funding institutions in general earn (70- 85) % of their income from lending. So it is expedient that the funds should be priced adequately. Because the risk involved with improper pricing is that they might end up losing all the volumes that they have. It is more important for loans to get volumes as some missteps can cause unintended consequences. So it is better to keep track of every single product and this can be done with the help of data analytics in which prices are provided as input.

Here is an example from one of a credit union (13 B in asset) that provides a guide to price consumer loans and manage the complexity of the pricing involved.

• A look at the following 4 components monthly would help in reducing complexities:
  1. Cost of funds -- which are ever-changing
  2. Risk cost -- what has happened in the past and the same may happen in the future.
  3. Operational cost -- every channel has a different p cost associated with it.
  4. How much does a funding institution want to make on its loans?
     
     
•  Making predictions on all the components that are mentioned above can be better done with the calculation of the Return of Assets (ROA). In the past 18 months, the ROA has increased by 173% and the direct order ROA by 42%. So by just looking at ROA, the complexity is less but it is still a little overwhelming.
  
  
•  Now focussing on an important part i.e. competitors; they are other credit unions, banks, capitalists, etc. and with ROA no two programs are the same. Since every lender is going to put its little twist on its program say, for instance, on the tax they pay, the discounts offered by them. But it is like a challenge when this is done monthly as every lender would give its flavor to its program to have higher competition in the market.
  
  
• The art of doing this is maintaining a profitable market position while optimizing every pricing component. This can be done by comparing the pricing of ‘apples to apples’. The analysis is at the highest level when pricings are studied this way. For instance, a bank can better take pricing and rank itself against all the competitors and strategically decide its position in the ranking. The ranking should be in such that it is neither too high nor too low so that an eye can be kept on the switching ranks. So when going up, it becomes higher-priced, thus, losing its value. But within 90 days, after observing its competitors’ moves, the bank gets back to the ranking that it wanted. As a result, the loans have a greater return and the lost value is recovered.

• This way the concerned company can try fixing a price that it wants to set against its competitors and make adjustments to it accordingly. So this is not simply mathematics but it can also be incorporated by a company in all its decisions; making sure that it is getting pre-decided returns.

To conclude, by using data analytics, a profitable way can be devised to price consumer loans and manage the complexity of the pricing. This will lead to higher earnings with fewer investments and the higher the ROA.