Credit Score to Wellbeing Score | TheInternetOfValue - 12
January 22nd, 2022

In the previous #threads, we've covered.

  1. Macro - Platform to LabourEconomies
  2. Micro - A model for a self-sustaining #web3 #community. #DAO.

In this, we look at the unit level - individual - a case for wellbeing score rather than #creditscore.

We also identified that the ideal way to represent an individual was by using a well-being identity, which on the network leads to a well-being score or reputation, which will act as a base for access to streams of services, including financial.

First, we must explore how the current credit system of credit scores came into being. Credit scores can make or break the economic fate of millions of individuals. The core process of deriving these scores is anything but rigorous.

History of Credit Scores:

Credit scoring in the US has developed over six decades. Initially, retail and banking staff assessed borrowers’ trustworthiness. In time, experts were entrusted to make lending decisions. But after the WW2, specialized finance companies joined in. In 1956, Fair, Isaac and Co. ( now known as FICO ) devised a three-digit credit score, promoting its services to banks and finance companies. It marketed the scores ( a range between 300 and 850 ) as predictors of whether consumers would default on their debts. The system remains powerful, though credit bureaus have developed their scoring systems. Credit scores legitimatized the complex securities at the heart of the financial crisis of 2008.

In the mid-2000s, the credit score was the key to connecting ordinary US homeowners with international capital investors eager to invest in highly rated securities. When investors purchased mortgage-backed security, they bought the right to a stream of payments. The mortgagor (borrower) shifted from paying the original mortgagee (lender) to paying the purchaser of the mortgage-backed security, usually through a service. 08 Fannie Mae, Freddie Mac, and networks of investors helped promote the credit score as a “calculative risk management technology.” Pricing according to credit scores had a dark side.

The credit score moved the mortgage industry from “control-by-screening,” which aimed to eliminate those who were unlikely to pay back their debts, to “control by-risk characterized by a segmented accommodation of varying credit qualities.”

Abuses piled up. Subprime-structured finance generated enormous fees for middlemen and those with “big short” positions while delivering financial ruin to many end-purchasers of mortgage backed securities and millions of home buyers. 11 Bank credit is essential for individuals in modern society. It gives people access to a wider variety of essentials - housing, education, healthcare, and transportation. ( Ref (…)

Obtaining a mortgage, a credit card, or a line of overdraft credit is not about living a life of affluence but rather grants basic financial stability and enables a life of security, opportunity, and dignity. It often functions as “grease for economic mobility.”

The use of consumers' personal data assessed against actual risk models trained on data sets derived from various groups of borrowers is not new. The history of a particular consumer shape the terms by which the provider will enter into a lending contract. With time, a new generation of technology and architecture, designed to economically extract value from massive volumes & variety of data, enabled high-velocity capture, discovery, and analysis to create intensified personal credit assessment opportunities. It gives lenders access to a much larger volume of data from a wider range of data sources.

Many are not obvious to the customer's financial standing, assuming that all personal data is credit data, including online behavior and activities. A borrower’s credit score may be affected by not carefully reading the online terms and conditions of the credit contract as a proxy for responsibility. Or by having “friends” on social media with problematic credit histories. This shows that the emerging credit score models do not just draw on “hard” facts about a borrower's past and current financial status but also on a range of soft behavioral facts and network-driven insights by analyzing all available info through complex algorithms.

This ‘micro-segmentation’ approach is also becoming prevalent for other financial services and products, notably insurance policies with personalized premiums or usage-based models, such as pay-as-you-go auto-insurance, and bank accounts and investment services. A related point that must be noted is that big data does not just increase the volume, source, and type of data available to lenders but also their subsequent usage. For lenders, it creates opportunities beyond making more fine-tuned assessments about the risk associated with the borrower’s financial and other characteristics through credit scoring and the resultant risk-based pricing. It allows them to engage in price discrimination, which refers to a lender offering the same product to consumers with the same risk factor. Regardless of the cost structure, depending on an assessment of the maximum price,they may be willing to pay for a particular product

We can often observe this trend when new customers are offered loans at a lower interest rate on the same loans than existing customers, whose higher rates are effectively cross-subsiding the lower ones. 23 Big data opens up the possibility of lenders being able to make more granular judgments about an individual customer’s willingness to pay a particular price for a financial product – depending on the urgency of their need, willingness to shop around or impulsiveness

Projecting this trend into the future, it’s not hard to feel that this can become pervasive, dystopian even, pretty quickly. For example, the social credit system that China is implementing.

So what's the alternative? In the absence of data trails, coop/community are slowly becoming the on-ramps for labor to go digital-first. How would this work? To begin with, members of a cooperative could be given identity cards that are Near Field Communication(NFC)enabled

Transactions between members of the cooperatives could occur through the card. Since cooperative / community members usually have regional hubs, they can be converted to on-ramps for the conversion of fiat currency to digital deposits.

Once this is done, any individual can use the card to record recurring deposits and interactions within the network of the coop/community, both offline and digital. And the transactions can be of upskilling or performing projects/ gigs and/or building the community itself. The network effects of large groups of coop members using a phygital solution for transactions between them could accelerate how they come online. This accelerates the creation of credit scores for large sections of the population today that have no access to formal banking.

For financial inclusion to indeed occur at scale, we will need technology to bring down costs of servicing end-users to a fraction of what it is today, distribution models to be drastically changed to optimize for the last mile, and the unit economics to be more inclusive

This is where we believe the road to inclusion at scale starts with cooperatives increasing a focus on collecting data about interactions between their members and storing it digitally. The next frontier for banking inclusion is not about opening more bank accounts but rather a better credit rating based on wellbeing that increases the count and nature of services such that the poorest can access these services. 32 so what makes a wellbeing score / reputation score?

Nodes to measure wellbeing

  • Physiology
  • Emotion (Energy in Motion)
  • Feelings
  • Thoughts
  • Habits
  • Performance

In the last mile where traditional fintech entities cannot cater today. Initially, they will be serviced through a cooperative banking model where the platform cooperative that brings these workers together will offer the initial services pertaining to lending and insurance

Summary: We see how credit scoring started and evolved and where it's going. A better economy would prioritize well-being as the credit score with an open-source algorithm dated and verified by the community but owned by the individual.

Tomorrow's thread will show how this well-being score can act as a base for a human stock market and help us leverage community banking and financial services.

For the other chapters and more modes to read do check out :

Arweave TX
Ethereum Address
Content Digest