4.4.3. Alternative Credit Scoring and Rating


“The International Committee on Credit Reporting (ICCR) expands on alternative data as ways to collect and analyze data on creditworthiness based on information readily available in digitized form but ‘alternative’ to conventional methods such as documented credit history. It has been broadly categorized as: Structured data, e.g. utilities, mobile phone, rental information and taxes; Unstructured data, e.g. social media and internet usage, emails, text and messaging files, audio files, digital pictures and images (Source: How can alternative data help M/SMEs).”


  1. Ability of borrowers to provide the details/documentation needed to get a loan
  2. Ability of borrowers to show formal registration
  3. Access to reliable credit information on borrowers
  4. Burden of banking/finance regulations and supervision
  5. Transaction costs associated with originating and managing loans to target sector/region


Provides informal sector business an increased chance of procuring working capital due to more access to banks or digital platforms.


Coming soon.


Coming soon.



Emerging and developing economies have a large number of micro-firms and some large firms, but far fewer growth-oriented Small and Medium Enterprises (SMEs) compared to developed economies. Despite evidence of high returns, these firms face critical problems in accessing finance due to high transaction costs as well as actual and perceived risks. In addition, lenders want to serve more applicants but lack many of the elements needed to predict credit risk.


The Entrepreneurship Finance Lab (EFL) and several private firms are attempting to use technologies to reduce the barriers to finance for “the missing middle” to unlock the entrepreneurial potential in developing countries. EFL is evaluating psychometric testing as a new approach to screening and risk evaluation. Psychometric screening tools measure future upside potential rather than traditional risk management tools used by banks for debt contracts, which only measure downside risk. New dimensions of information such as personality, intelligence, ability, and character can provide a complete and accurate understanding of credit risk which is resistant to manipulation. A better and more cost-effective understanding of risks better serves lenders and borrowers alike, increasing profitable lending to “the missing middle.”


Using psychometric tools like those developed by EFL can save banks in the developing world time and money when assessing creditworthiness of potential borrowers in environments where there is no established credit history and/or limited collateral. In 2011, EFL was a winner of the SME Finance Challenge, sponsored in part by USAID. In 2015, USAID DIV awarded the firm a $1.5 million grant to support technology and platform innovations. By the end of 2015, EFL had enabled or optimized more than $1 billion to entrepreneurs and individuals around the world. EFL continues to improve its psychometric credit scoring capabilities while simultaneously innovating with new alternative data sources such as mobile phone usage data, social network data, and location data. In addition to its existing online and tablet platforms, EFL has significantly increased its reach by expanding to mobile and SMS.


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