A US study: Utility and telecom payment data predict loan repayment capacity!

ount Delinquencies with  Utility and Telecom  Payment Data

Account Delinquencies with 
Utility and Telecom 
Payment Data

I still remember my times at CGAP when the Technology Team was working with PERC, a research center and advocacy group working on:

reducing credit invisibility, ie., “credit invisibles” without credit data are most often rejected by mainstream lenders using automated underwriting systems;

abolishing the “credit catch 22″, i.e., mainstream lenders rely on credit reports to assess credit risk and to extend a loan, plus credit bureaus only have data on the already banked population. In this dynamic to qualify for a loan, you will already have to have credit.

pointing fingers at alternative data which presents a wealth of data that could be used to drive financial inclusion. If it can be accessed, it could dramatically increase access to financial services for low income persons worldwide.

At the time, we were primarily interested in using transactional data from airtime top ups, utility payments, rental payments, etc. to assess a client’s repayment capacity and willingness. I have been interested in looking more into the power of alternative data primarily since I am convinced that it would not only help credit-seeking people in the developing world to improve and strengthen their credit scores, but also many unbanked people in the developing world to Access credit without having to have had credit before. I had written a blog “Psychometrics in credit Analysis – Threat or Promise?!” describing credit Analysis based on questionnaires yielding a person’s profile on honesty, intelligence, aptitude and beliefs. These tests facilitate lending to the otherwise called “unbankable” borrowers without a credit history, hard collateral, or an active account. At the time I asked the questoin if the thorough credit analysis in microfinance with an in-depth knowledge about the customer and his business is really possible to pack into a list of a few questions? And the blog post also describes a not so successful case Stanbic Bank in Tanzania when using psychometric data for credit analysis.

Now, coming back to PERC. PERC recently launched a study which confirms that utility and telecom payment histories (non-financial data) are predictive of future delinquencies on traditional financial credit accounts (bank cards or mortgages) or of having future derogatory public records. PERC utilized credit scores and credit file data from one of two credit bureaus (TransUnion and Experian), and socio-economic data from Acxiom (e.g., household income, race/ethnicity). The sample consisted of 4 million credit files from July 2009 to June 2010, as well as utility and telecom payment histories prior to July 2009. And they looked at credit cards issued by banks, mortgage accounts, and the incidence of derogatory public records (including bankruptcies).

But let’s quickly come to some of the results of the study:

Utility/Telecom Payment history is predictive of future credit delinquencies. Among active bank card holders the overall rate of delinquency on bank cards was 11.3% (July 2009-June 2010). For consumers with a severe delinquency on a utility or telecom account in the year prior to July 2009 the rate was 47.7%. For consumers with no past delinquency reported for utility or telecom accounts that were older than 24 months, this rate is just 4.5%.

After controlling for traditional data credit scores, Utility/Telecom Data Still Predictive: Active bank card customers with VantageScore credit scores in the 800-899 range had a bank card delinquency rate of 2.7% if they had no prior utility/telecom delinquencies reported and a rate of 11.4% if they did have one. VantageScore is the name of a credit rating product that was created by the three major credit bureaus (Equifax, Experian, and TransUnion).

Not fully reporting utility/telecom payments is unfair to those who pay on time: Utility and telecom customers who pay on time appear to have lower scores than they would if those payments were reported to the main consumer credit databases of the nationwide credit bureaus). These on-time customers are penalized by the status quo in which non-financial payment histories are not fully reported to credit bureaus. Conversely, utility and telecom customers who pay very late are higher risk than their traditional data based credit scores indicate

Utility/telecom data found predictive during financial /mortgage crises: The period examined in this study for credit/financial outcomes was from July 2009 to June 2010 and a payment history prior to July 2009. These results speak well to the predictive potential of non-financial data in times of financial crisis when risk assessment is particularly crucial.

Use of Utility/Telecom data can expand safe lending: By segmenting consumers by utility/telecom payment histories or using this data in credit scores we found that lending could safely expand over five percent. Many consumers with no credit data, who as a group might be viewed as too high risk, could be safely extended credit if their utility/telecom payment histories were considered.

 

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One Response to A US study: Utility and telecom payment data predict loan repayment capacity!

  1. Bailey Klinger says:

    Hanna-

    Let me share with you and the forum some lessons 5 years after moving from my offices in Washington & Cambridge to the trenches of implementing solutions for financial inclusion, because I think they are relevant to this posting, which is falling into some of the same pitfalls I myself fell into.

    We created a psychometric credit scoring tool because of the shortcomings of existing approaches. In our focus markets, credit bureaus covered barely 50% of the adult population in the best of cases, 0% in many other cases. Building custom scorecards off a bank’s historical application data is a good solution in theory, but in practice that data often doesn’t exist, is a mess, and is tremendously costly to turn into stable statistical insights and operationalize for lending to new applicants, particularly for smaller lenders & products. Psychometric credit scoring suffers from none of these shortcomings- it can be used to score anyone, anywhere, and because it’s a standardized application platform and dataset across banks, there are huge economies of scale in scorecard building, maintenance, and implementation. We had the solution to all the problems out there with existing approaches- a ‘game changer’.

    But taking a solution from theory to practice reveals its own unique challenges. Unlike bureaus and historical application scorecards, psychometric credit scoring involves active data collection and therefore requires strong fraud monitoring systems, not against applicants (as most people assume) but against loan officers. This was a lesson we had to learn the hard way, and develop a suite of fraud detection algorithms after having seen numerous cases of fraud. It also requires rapidly building scores off small datasets, which in cases when used on a new lending product with rapidly changing qualification criteria and applicant pools, has to be rapidly updated when the population changes. Over time as the dataset grows, that problem diminishes, but that is after five hard and bloody years fighting for data, against fraudsters, and on rapidly shifting terrain.

    So now I find myself on the other side of where I was five years ago. Psychometric credit scoring, like bureau scoring and historical application scorecards, is being used in the real world, enhancing financial inclusion and making a large number of lenders a large amount of money. But, in implementation, its practical challenges, like those of the other sources, have been laid bare. But have no fear, there is a new data source out there for alternative credit scoring that will solve all these practical problems: utility and telcom data. This time is different- it will be a game changer!

    Let’s not repeat the same mistake. These other data sources, just like those that have come before them, solve some of the problems of existing approaches, but in exchange will have their own challenges. The winners in enhancing financial inclusion will know that going in, and will take a portfolio approach.

    How do we know that is true? EFL isn’t a psychometrics credit scoring company, it is an alternative credit scoring company, pulling in any and every source of data to better understand risk and help the un/under-banked earn the credit they deserve. We have production models using psychometrics, credit bureau, and historical bank application and transaction data. And we also have existing models that can be used today for scoring with mobile phone data, online/social media data, and GIS data. But today, the psychometric score is the one having the largest impact, because we are seeing the practical challenges in implementing these other new scores as well, and taking our time to methodically fight through them.

    Take for example telcom data, cited in this report. We were able to gather CDR data from a network operator with 98% market share in a Caribbean country, match it to loan data from one of the largest MFIs in that country, and make a highly predictive model with a 72% hit rate (compared to a 10% hit rate for social media data and 0% hit rate for credit bureau in that country). Why isn’t this new model a game changer rapidly creating financial inclusion around the world? The problem is that to actually implement this at scale using a CDR, you need to work out a complicated business agreement between a mobile network operator (MNO) and a lender, which turns out to be a herculean task when you actually attempt it in emerging markets. For that one country it took us 4 years, with a single MNO. If you are working in a different country like Indonesia, you aren’t dealing with one MNO, but many. There you need the largest 5 MNOs to cover just 80% of the subscriber base. Not to mention issues of data privacy and customer consent. You can go over the network directly to the consumers and their phones, but that strategy has its own challenges.

    This is not to say that scoring using mobile phone data will not be a hugely valuable tool to enhance financial inclusion after these issues and other practical challenges of implementation that come up are solved- it will. But that will take a long time, and will expose a lot of warts. I wouldn’t be surprised if in five years there was a new blog post here dismissing mobile phone data scoring as dangerous because of these practical challenges have reared their ugly head, or because a lender using it was unsuccessful*, but this time it’s different: now there is this new thing that is the solution to all the problems out there with existing approaches- a ‘game changer’.

    A smarter approach is to realize that every existing approach has practical challenges that are known. A good new approach will solve some of these challenges and reach a new segment of the population, but in exchange will have its own unique challenges that will rapidly emerge as theory meets practice and they are rolled out. The smart players will have a toolkit of approaches and use the right combination in the right circumstances.

    At EFL we have a toolkit. Today, psychometrics is the dominant tool we are using, because despite all the problems above, it is the most practically useful tool for reaching new borrowers with controlled risk. Or to paraphrase Churchill, it the worst approach, except for all the others. We haven’t used our mobile phone or online scoring models that much yet because the pipes of data aren’t yet there in most markets where we work (nor are the pipes for water, for that matter). We’re building them, but that is a long-term project, and we can’t just wait for the future. Its 5pm right now in Lima, Peru. Today, one lender using our tool in 3 semi-formal neighborhoods in the south and eastern cones of the city has created a psychometric credit score for 84 people who would have previously been rejected because they don’t have sufficient history in the credit bureau for a traditional score. The top-scoring 51 of them got a loan. Over the past two years, that lender has seen that those top-scoring 60% are highly profitable to lend to. They gave test loans to some of the bottom scoring 40% that should have been rejected based on our score, just to see what happened, and they followed the others that were rejected in the central bank database, some of which managed to get a loan from another lender. Those bottom scoring 40% defaulted more than twice as much. This is profitable expansion of access to credit happening now, every day, only possible with psychometric credit scoring, with results audited and endorsed by the banking regulator. When the bureau is available, our client uses it. Data on utility bill payments is available, and the lender uses it. When we finish building the pipes for mobile phone data, GIS data, and social media data, we’ll use it. That future has to be created, but with open eyes to the fact that none of these new solutions by themselves will be flawless. And until then, the best tools available have to be used, one of which is proving to be psychometric credit scores.

    *BTW it is faulty logic to argue that a credit score doesn’t work because a particular lender using it lost money. Thousands of lenders using FICO’s score in the United States have lost money, despite the fact that the score has revolutionized retail lending in that country. A score is only one part of the overall credit risk assessment, which is only one part of the entire chain of policy, process, and product design for successful lending.

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