Economic Transparency in the Crediting Space
Because credit is such an important aspect of any economy the government would want a keen eye into all lending services available. Whether small such as credit cards or larger such as auto loans or mortgages, all would have to be closely monitored.
It’s here that credit assets would have to be transparent to both their interest yield rates in addition to thresholds for issuing credit. Because everything would be realtime the government would be able to see debt ratios for each market segment as transactions happen. Each credit asset would also represent a given market such as mortgages or auto loans meaning that receiving a pulse on each sector would be an aggregation of all assets in a particular category.
Adjusting rates from the macro perspective would then have one primary variable, collateralization ratio or cash to debt ratio for adjusting interest rates. This would be the critical cog in the overall market machine.
In the case of collateralization ratios, this could be in the form of a government controlled software code shared by all credit assets. Because it would be a component, when it is changed for one (law) it would instantly go into effect for all creditors using said component. This is how the Fed would adjust debt ratios for given industries going forward.
The causality of adjusting collateralization ratios would have a direct impact on interest rates pending how much cash (primary blockchain) each creditor would have. This is where the next lever of economics would come into play, the threshold or percentage of cash on hand to potential debt in the market.
In the traditional market scenario excess liquidity would be seen as a negative as interest rates could only go so low. This would result in inflation of items to make up the difference for what would be a negative interest rate. However, because interest is now flipped, excess liquidity only amplifies the yielding from liquidity pools which would in turn increase the rate at which debt is paid off.
Because this system always has cash in movement, debts are always being paid. The only variable, again, is the speed at which they are being paid. Regulation would be needed to foster an even playing field for creditors in the space as larger liquidity pools would have more money available to them to provide higher interest rates back to merchants.
An algorithmic number such as, say, 150% would mean that creditors would be limited to only taking on 150% cash to debt ratio but this would not mandate that they have to be at the cap.
Competitiveness of overhead cost management would then be a factor as higher shareholder yields would result in lower overall interest rates available to merchants. However, by keeping the threshold for percentage of cash to debt ratio regulated via a shared software component, it evens the playing field for both newcomers and established institutions.
Newcomers, not having capital but having no equity obligations would have the same liquidity interest but due to lower capital, lower merchant interest rates. Institutions would again have the same relative interest rate but due to their higher capital, offer a higher merchant interest rate. By capping a firm’s cash to debt ratio you incentivize both investment within institutions but also now mandate their need for operational efficiency as they can only afford so much stake in their overall liquidity pool before reaching merchant interest rate levels of those not offering stake within the company.
Using the above scenario, a startup Fintech may have $100 in capital yielding 13% interest or $13 a day with $100 of debt outstanding. A larger institution would have $150 and, again, $100 debt outstanding but the same 13% interest making them $19.50 a day. However, the additional capital was raised by investors entering their liquidity pool which means they have an obligation to disperse the interest back to investors.
This would be done by assessing fees to given investors. Because the startup is leaner than the institution, their operating expense is only a $2 fee or 2% which still yields merchants 11% interest. The institution being larger has an operating expense of $5 a day which is a 3.33% fee however, their interest payout is only 9.67%.
Fees and interest rates would adjust in real time relative to ongoing operations and how quickly they could bring in capital once it is spent. With blockchains updating as quickly as every 5 seconds, fees and payouts could be paid out within the same interval giving the most accurate heartbeat of not just an institution but also the entire economy. More likely, though, these would be adjusted daily.
By shifting the economic lever towards a ratio of capital to debt, you’re still incentivizing investment within companies as higher yields are possible. However, without increasing merchant debt load through higher interest rates or a more compelling product, creditor companies would not be able to increase their capital. This now incentivizes good business practices in the form of fees or operating expenditures.
Credit Rewards, Incentivizing Early Payments
In the above scenario there exists great incentive for both merchants and creditors to go into business together. Even more so, it benefits consumers as interest is now paying off their debt, not adding to it. However, there exists even more capability for consumers to use credit in the form of rewards.
As creditors wish to have as much possible money in their primary blockchain account for its automated yielding, consumers paying off their debt/credit assets as quickly as possible is ideal as it gives their credit asset the possibility to mature quicker than its current interest rate. Merchants would come to know that not only is an interest rate ideal but even better is that it may start making them money sooner than its maturity.
In essence, by correlating data of past early payments by a consumer, a creditor can identify transactional credits that may pay sooner than its interest rate and therefore increase the value of its credited asset. Furthermore, with the use of data, certain merchants who do more business with the given creditor may wish for higher yields. Rewards would then be targeted at these transactions.
Rewards would fall in line much the same way they do with credit cards currently. Creditors would spend a fraction of their operating expenses raising fees. Not enough to shrink their merchant interest pay out substantially but enough to boost their value proposition to engage merchants.
Keeping purely statistical, discounts could be offered on purchases made and paid off via credit accounts relative to their overall maturity rate, something more commonly seen within large sourcing companies today. A separate point system could also be created by the creditor, again, as another blockchain asset. These points would only be redeemable through select merchants with the varying point to dollar yield being a derivative of the merchant/creditor early payout ratio and point allocation being the aforementioned discount.