A number of concerns have arisen about credit information sharing (CIS). Many of these are indeed myths about credit reference bureaus. John Lenon once said, ‘’ I believe in everything until it is disproved. So I believe in fairies, the myths, dragons. It all exists, even if it’s in your mind.’’

The first myth arises from the concept called blacklisting. We have heard it from many quarters, including the petitioner who has approached Parliament asking for disbanding of CRBs on account of the blacklisting of borrowers. This is retrogressive, and as we shall demonstrate below, blacklisting of consumers does not actually exist in today’s era of full-file data sharing, meaning sharing of data on both performing and non-performing accounts.

In February 2014, all banks commenced sharing of records about all their borrowers, not just defaulters.  According to Central Bank of Kenya Risk Classification Guidelines, loan facilities are classified into 5 categories: Normal (borrowers who have no arrears), Watch (arrears of up to 90 days), Substandard (arrears over 90 days), Doubtful (arrears over 180 days) and Loss (arrears over 360 days). All these accounts are shared with the credit bureau. Lenders update the credit bureau every month to reflect repayments during the last one month. After every update, many borrowers oscillate from performing to non-performing and back to performing, depending on payment of monthly instalments. No record remains permanent, unless repayments have stalled permanently. At any one time, more than 90% of records in bureaus are about performing loans. Clearly, CRBs are not custodians of any blacklist.

If more than 90% of all accounts shared with the credit bureau are performing accounts, this is further testimony of the fact that credit bureaus are not about blacklisting borrowers. This majority has not asked for disbanding of credit bureaus, because they have positive records that prove their credit-worthiness. The existence of their credit records at the bureaus gives lenders comfort when granting them credit. They enjoy faster turn-around time when they apply for credit. They benefit from the quick and automated mobile micro-lending products which are providing credit efficiently to an ever-increasing number of Kenyans. They enjoy unsecured lending.

Amongst bad debtors, there are two categories. Between 1% and 3% of total debtors consist of Serial Defaulters – professional defaulters who borrow from several lenders and default in all of them. Fortunately, serial defaulters are rare nowadays, thanks to the introduction of CRBs. In the 1980s and 1990s, they roamed freely, bringing down dozens of banks, causing immense agony to depositors. No petitioner, unless he is one himself, would want to protect serial defaulters. It is serial defaulters, not the CRBs as alleged by the petitioner, who have the potential to threaten the stability of the financial system and must be denied credit until they are made to repay loans that originate from accumulated savings of many small depositors.

But there are bad debtors (about 5%) who, due to acceptable reasons, are unable to repay their debt. Even though their information is in the CRB, lenders still grant them credit, depending on the risk appetite of the banks. This explains the growth in loans that we are witnessing in banks today. The gross loans in banks alone increased from Ksh.1.53 trillion in December 2013 to Kshs 1.88 trillion in December 2014, thanks to the fact that banks are willing to lend. This growth would never have happened if the banks’ first duty is to avoid lending to people who have defaulted in any way. They are often asked to repay their loan before being granted additional loans. Many do repay, and after obtaining clearance certificates, are granted more credit. Others are able to restructure their loans, depending on the policies of the lender.

There is nothing in the law (Central Bank of Kenya Act, Banking Act, Sacco Societies Act, CRB regulations 2013) that prohibits lenders from extending credit to people with any default history. I quote ‘Regulation 33 Sub regulation 3: Information kept in accordance with sub regulation (1) may not be used solely to affect the customer’s chances of obtaining credit but as one of the factors to inform the decision making process’.  If a bank is unwilling to lend on account of poor credit history, the borrower only needs to shop around for a lender who has the risk appetite for borrowers with a default history.

Just like most solid pieces of infrastructure, the fruits of CIS take a while to reap. For Kenya, a number of benefits have already emerged. Serial defaulters are properly being identified. Digital finance is experiencing tremendous growth. Incidences of over-borrowing can now be tracked and, as a result, credit bureaus are protecting good borrowers from over-indebtedness and financial inclusion is being expedited at an unprecedented scale.

The way of getting the most of out the mechanism: Get more Data!! Data from more sources is a sure way to neutralise biases in the credit bureaus. Consistent payments of water, power and phone  bills, and loans from chamas, SACCOs, microfinance institutions, etc, is a sure way of adding credibility to the system as it will give a more comprehensive view of you the borrower.  Let us make this system work for all of us. Access your free credit reports once a year as entitled by law. If you have noticed an error in your credit report, contact your lender, or lodge a dispute with the credit bureau. If you are still dissatisfied, contact Tatua Center our dispute resolution center for free mediation. Let’s not throw out the baby with the bath water.

Jared Getenga is a credit risk expert, and the CEO, CIS Kenya. Tweet : @GetengaJ

The economy is not dominated by large and multinational corporations. SME‘s are greatly responsible for economic growth and therefore are an important sector to focus on. Creditinfo Georgia has invested to develop specific tools to help companies provide better, more effective decisions to enable greater access to finance for SMEs.

In Europe, for instance, 99% of businesses are small and medium enterprises, and at the same time they contribute 58 cents in every euro in economy. This situation is not specific to the Europe.

This problem has attracted attention for the longest time. Investment companies over the world invest a lot of resources into development of SME sector. For instance, only in 2014 the Inter-American Investment Corporation approved 64 operations totalling $426.3 million to support development of SME sector.

In Georgia the role of SMEs is very significant but the access to finance is very limited.  To illustrate the role of SMEs, in 2010 shares of registered taxpayers were distributed as follows: large business was less than 1%, medium business is 5-25%, and small business was 70-95% (Tax Reforms, 2011; p.26).  According to National Statistics office, SME sector created 44% of job places and produced 18% of total turnover in 2014.

Figure 1 shows the results of a survey to SME from 2011. One of the central questions of the survey was about the existing barriers to the development of the small busi­ness in Georgia.  It is clear that access to credit is the highest with 72.3% of those responding citing this reason.

Figure 1: Macro factors: Related to the government policy (in percentages)

Source: The Role of SME Sector in Georgian Economy, Tatiana PAPIASHVILI, İlyas ÇILOĞLU

According to World Bank Country Profile 2013, approximately only 14 % of SMEs use bank finance for investment. But even for those SMEs who have access to credit, loans are granted with very unfavourable conditions: 96% of accepted loans require collateral, which cover at least 220% of the loan amount.

Creditinfo Georgia provides efficient decision making tools as well as access to credit information (positive and negative). It has been doing so for over 10 years to facilitate lending in the bank and non-bank financial sector but also other forms of lending sectors such as telecoms and trade finance (business to business payment terms). Creditinfo Georgia database contains information of over 2 million individuals and over 56 000 companies, as well as data of large number of sole proprietors registered under personal IDs.

Creditinfo Georgia believes that a core role is to provide the support tools to enable financial institutions to improve their lending, and blended scorecards have proved to be a successful way to do it in numerous markets across the world.

Let’s look at small enterprises from Creditinfo Georgia Credit Bureau perspective. Around 55% of them have no credit history and, according to World Bank data, around 70% of them use internal finance for investments. People often have a problem to get business loan so they apply for personal loan to fund their business. Therefore, finances of the owner and the business are frequently intertwined and SMEs are often dependent on the owner‘s personal actions.

So the logical solution here was to evaluate company’s risk taking into account information about connected individuals: owners, shareholders, board members.

This is the principal that is used when combining all the information about the business and individuals history.

The concept of Blended Score is simple. Imagine that you have to decide, whether or not you will grant a loan to this company illustrated in Figure 2 below.

Here is some limited information about the company. Especially if the company is quite young, in that it is operating on a market only during the last 3 years, but it’s making a good profit and showing very fast financial growth. Sounds like good company to have a deal with. Loan approved.

Now let’s look at the information about company owner illustrated in Figure 3 below.

It’s turned out he is a well-known fraudster, who is responsible for default of couple of firms and now he is going to spend next 150 years in a jail. Would you change your decision now?

Blended scoring would combine information about both company and owner to identify high risk when it now visible at the first glance.

What information about SME can we get? There is some demographic data such as company age, industry and address and there is credit history.

Then there is the linkages information which is recorded on all the owners and directors within Creditinfo Georgia.  For each individual we have the credit history and credit score which can be combined with the business data.  Where the owners are “formal” but are known by the lender this information can be added.

So how does the final scorecard look like? Figure 5 shows the approximate makeup of its components. Brown sectors refer to variables looking at the company information (around 65%) and the green ones (around 35%) represent variables looking at information of individuals.

Talking about efficiency, it is significantly higher than efficiency of classic model based only on commercial data. The blended scorecard should be integrated and combined with internal processes and procedures to achieve best results. Acceptance rate after implementation will increase and default rate will decrease.

This model provides Banks, Microfinance Institutions and Trade Lenders an excellent opportunity to break down some of the existing barriers to lending and extend the access to credit for SME, an essential part of expansion of all economies.

As Bruce Mau once said, when everything is connected to everything else, for better or for worse, everything matters.

• Inter-American Development Bank; http://www.iadb.org/en/inter-american-development-bank,2837.html
• Transparency International; http://www.transparency.org/
• The Banking Association South Africa; http://www.banking.org.za/index.php/our-industry/small-medium-enterprise;
• European Comision Annual Report on European SMEs 2013/2014; http://ec.europa.eu/growth/smes/business-friendly-environment/performanc…
• National Statistics Office of Georgia; http://geostat.ge/?action=page&p_id=211&lang=geo
• World Bank Group Surveys, Georgia Country Profile 2013; http://www.enterprisesurveys.org/~/media/GIAWB/EnterpriseSurveys/Documents/Profiles/English/Georgia-2013.pdf
• The Role of SME Sector in Georgian Economy (Tatiana PAPIASHVILI, Ilyas CILOGLU); http://www.journal.ibsu.edu.ge/index.php/jbm/article/viewFile/314/287

The experience of the over-indebtedness crisis amongst the poorest in Kosovo and other regions such Andhra Pradesh in India is still a clear memory to those who had to manage the human and financial fallout from these situations.  This type of crisis is caused by 2 factors; excessive liquidity largely from donor funded institutions and secondly no credit bureau infrastructure, or usage of it.

Are we about to witness the same cycle in sub-Saharan Africa with the highly funded race to provide loans through the trend of alternative/big data, at a time when MFIs are yet to be integrated into the “credit bureau culture”?

The approach to use telco data and social media data to access stable and trustworthy individuals is a sound and positive approach to provide small loans to the poor.  The concept mirrors the successful use of “voters roll” data in the 1980’s by the UK credit bureau to deliver stability indicators prior to establishment of positive credit bureau.

This delivers an excellent approach to broaden financial inclusion as long as it is done responsibly, with the use of credit bureaus to ensure multiple organisations are not serving the same financially unaware customers, as was the cases illustrated by CGAP in their excellent report “Too Much Microcredit? A Survey of the Evidence on Over-Indebtedness” By Schicks & Rosenberg.

“In a review of four countries, Chen, Rasmussen, and Reille (2010) reported that delinquent loans, which averaged 2 percent of portfolio in 2004, skyrocketed to 2009 levels of 7 percent in Bosnia-Herzogovina 10 percent in Morocco, 12 percent in Nicaragua, and 13 percent in Pakistan. In some of these countries, subsequent levels have risen quite a bit higher. More recently, collection has collapsed in the Indian state of Andhra Pradesh.” 

What are the key indicators that this situation may happen again?  In the previous cases it was high donor liquidity to traditional micro finance and non usage of credit bureau.  In this case the high liquidity is coming for support for “alternative/big data lending” and those MFIs using these approaches not using credit bureaus.

There is a misconception that credit bureaus in developing countries will create a barrier to new entrants to the formal financial system, as it is viewed only those with a credit record will receive credit in the future.  This is not true. Credit bureaus create an environment that supports unsecured or non-guarantor credit which does not exist in markets prior to credit bureaus.  This new form of credit then becomes the universal form for the masses that do not have assets.  The exclusion situation of those without credit records will only exist in highly developed markets such as the US where lending is 100% bureau dependent.  Having said this, the process can be accelerated by alternative data in parallel with credit bureaus.

The focus in recent years has been largely on research and development of alternative data lending.  The international donor organizations like The Gates Foundation invested heavily in projects in Sub-Saharan Africa focused on alternative data usage for creation of greater financial inclusion. There are number of companies, which with such support, developed various scoring models, majority being based on telcos data, which enabled some of the credit providers to extend their loans to the unbanked populations. As much as it is a positive trend, one shall always remember that such data needs to be shared with private credit bureaus so not to create silos in the economy and allow for all lenders to have access to it.

On the credit bureau side, significant steps forward have been made with the support of the IFC and World Bank plus other donors such as KfW, SECO or DFID to mention just a few.  Over the last 10 years credit bureaus have been developed in 18 countries in Sub-Saharan Africa.  These initiatives are largely driven by central banks and governments licencing private credit bureaus.  The involvement of central banks ensures that regulated credit providers (which are all banks, in certain cases some of the MFIs or even other financial institutions), are usually enforced to submit data to credit bureaus as well as use credit reports for their credit risk assessment.

However, this leaves a large tranche of the financial sector that frequently resists the usage of credit reports whether due to reluctance to change, lack of knowledge or frequently fear associated with dealing with new data. This unregulated credit providers sector, whether it be Telcos, insurance companies, retailers, donor funded MFIs or privately funded MFIs it is likely where a surge in liquidity will lead to over-indebtedness and misery, and this is the exact group of people, whom donors are trying to help – the ones at the bottom of the pyramid.

The obvious parties to drive change would seem to be Central Banks and private credit bureaus.  In reality Central Banks often have little real interest in an area that will not create systematic risks for the financial sector.  When it comes to credit bureaus without other stakeholder’s involvement credit bureaus will face a reaction of “sales people”. That means that they may be perceived, especially by MFI sector, as just another company selling some services and not seeing the overall benefits it brings to the industry.  That is why this issue needs a concerted effort from all parties.

The stakeholders in this change process shall look at some key actions:

• Independent education and capacity building programs amongst the management of MFIs about the benefits of credit bureaus.
• Central Banks and Government to take measures to ensure credit bureaus receive data from all financial services providers.
• “Alternative/Big data” credit providers integrating credit bureau data into their decision process.
• Donors and investors working with MFIs or “alternative/Big data lenders” insisting on responsible lending practices being followed.
• Credit bureaus to facilitate the addition of MFIs by minimizing IT and development costs.

With this approach we can avoid the errors of the past and achieve the goals of financial inclusion with responsibility.

For more information about this article, please contact:

Agata Szydlowska

Head of Financial Inclusion & CRB Awareness Africa, Creditinfo Group hf

Email:  agatas@creditinfo.com  •  Skype: agatacreditinfo • Mobile: +254 72299758