The growth of the PNG capital market, and ultimately the growth of the PNGX public market, is reliant upon the growth of the MSME sector. It is in PNGX’s interests to help develop MSMEs in the hope that, ultimately, a small number might successfully become listed on PNGX. Knowledgeable businesswomen in this sector are important.
Obtaining access to finance is a starting point for growth.
A good credit score is crucial to seek and obtain loans at reasonable interest rates from a bank or microfinance institution. But traditional credit scoring models put many businesswomen at a disadvantage. It’s time for lenders to adopt new credit scoring methods. A huge segment of women-owned micro, small and medium-sized enterprises and solo traders are unserved by banks and microfinance institutions (lending institutions) in PNG.
Having a good credit score is vitally important to access finance at reasonable interest rates from lending institutions in PNG. Often, businesswomen and new businesspeople who do not own land or other personal property which can be used as security (collateral) cannot obtain a business loan which must be secured by such collateral. This creates a situation where these groups of borrowers without collateral are unable to enter the borrowing system by firstly borrowing a small amount, repay the loan on time, and then seek a larger loans, to build the credit history crucial to credit scoring models used by lending institutions.
Some businesswomen may be able to call on family support for small loans. Many seek loans from other lenders at very high interest rates. Both systems significantly limit and may even curtail, business growth.
A challenge for PNG’s emerging MSME sector is to provide access to credit to these unserved groups at reasonable interest rates.
Lending institutions use credit scoring models to calculate a credit score. These models study past relationships between borrower groups’ characteristics and behaviours and that group’s loan repayment history to predict whether today’s borrowers from a similar background or group will repay a loan. Most of the historic data used in these models was collected in ‘traditional’ credit processes. Those processes analyse financial statements, credit history and collateral of different borrowing groups. They also make subjective judgements of a borrower’s perceived ‘character’ to ‘guestimate’ likely repayment.
Credit scoring using these models help lender institutions to better estimate a customers’ credit worthiness. But the model’s reliance on historic data makes it more likely to disfavour borrowers from different backgrounds who were excluded in the past, but who have the potential to be good customers. These groups include many businesswomen who do not have a bank account, let alone a credit history, and have poor bookkeeping records.
To overcome these challenges, it is imperative that lending institutions identify new and ‘alternative’ data models. These new models could use sources of data that give insight into the financial lives and capacity of the unbanked so that lending institutions can offer them access to appropriately designed credit products, without the need for credit history and land or other property to be used as collateral. What do lending institutions need to do? Adopting a gender-responsive credit scoring model that considers women specific data for the algorithms can help to better evaluate the creditworthiness of businesswomen who do not own property or own assets or who have not been able to borrow from lending institutions.
Lenders could use new alternate data streams combined with artificial intelligence computing as substitutes for traditional payment patterns and credit history to evaluate creditworthiness of businesswomen borrowers. Such ‘datadriven’ lending models need not, and cannot, follow the traditional credit scoring systems. Data such as housing data, education data, career and workplace history data, community data could all assist assessing the creditworthiness of a woman entrepreneur who doesn’t have the traditional financial history used for credit scoring.
Digital transaction data such as the number and size of online sales and purchases, mobile phone/wallet transactions, and on-time payment of bills are objective records of financial behaviour. Sector specific data such as the purchase of agricultural inputs as a share of the income from harvested crops, or purchasing or selling behaviour on e-commerce platforms like FaceBook marketplace, PayPal, and Amazon can also indicate potential repayment capacity and the willingness to repay. Credit bureaus typically use such criteria to measure credit history. Businesswomen need to understand this and have this information ready when applying to a lending institution for a loan.
Better business skills and bookkeeping practices, greater use of digital technology and moving to the formal sector will also support more positive credit scoring for many women-led businesses.
PNGX, PNG’s national stock exchange, together with its collaborating partners, the PNG Digital ICT Cluster and Unkapt Capital, is conducting training workshops under the Business Skills Boost for Women Entrepreneurs Program (2022 DEFINE SKILLS Program). The Program provides complimentary training to women-led MSEs in Papua New Guinea to boost their business and digital literacy skills and accelerate their business development and growth. Development of better business skills can assist understanding how to get better access to credit under the existing or any new credit scoring system.
Registration is now open for intermediate and advanced level workshops at DEFINE INITIATIVE website https://www.defineinitiative.org/.
The information in this article is general in nature and you should take care to inform yourself about the specific characteristics of a particular investment before making a decision to invest in it.
PNGX recommends discussing your investment objectives and needs with a stockbroker or qualifi ed fi nancial adviser. In PNG, you can either contact JMP Securities Limited (firstname.lastname@example.org) or Kina Securities Limited (email@example.com).
By following these articles and reading the information available on the PNGX website (www.pngx.com.pg) or following PNGX on LinkedIn or Facebook you can learn more and build your wealth by investing in PNG.
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