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Big data for smallholders

Technical scientific article

The Financial Identity Risk Management solution will provide consumer protection while enabling lenders to offer credit that can improve a borrower’s life and life chances.

The Financial Identity Risk Management solution will provide consumer protection while enabling lenders to offer credit that can improve a borrower’s life and life chances.

The facts are staggering – of the roughly 2.5 billion people who earn income from working on 500 million smallholder farms, nearly half must make do with daily incomes of US$1.25 or less. Supporting these farm workers by increasing access to financial services – including affordable credit necessary to build assets and generate wealth – is the quickest and most efficient way to lift over one billion people out of poverty, according to a recent United Nations report.

As always, while goals are lofty (for example, the Millennium Development Goals), resources to achieve them are invariably insufficient and most ‘solutions’ are never taken to scale. And while there have been successes in global poverty reduction efforts, owing to a variety of circumstances ranging from climate change to decades of underinvestment, smallholder farmers are increasingly unable to escape poverty and remain highly vulnerable to external shocks.

Despite the spread of microfinance and the more recent proliferation of digital financial services, roughly five in seven persons globally remain financially excluded. This is especially true for the rural poor. The twin challenge confronting lenders and financial services providers in rural areas is the lack of a reliable financial identity – in most developing countries there is neither a consistent national identification number nor anything that can be used as a proxy – and the absence of credit information for assessing credit risk, credit capacity and credit worthiness.

Unless these two problems are surmounted, efforts to support smallholder farmers and other financially excluded populations will remain substantially constrained – as is evidenced by recent troubles in microfinance and the inability of digital financial services to achieve commercial viability.

To drive financial inclusion and enhance the ability of pro-poor lenders globally, PERC, the Policy and Economic Research Council based in North Carolina, USA, and its partner Experian MicroAnalytics (EMA) have developed an innovative solution called ‘FIRM’ for Financial Identity Risk Management.

How FIRM works

After receiving prior informed consent from the borrower, lenders can invoke FIRM, which instantly pulls data from alternative data source providers (for example, agricultural cooperatives, mobile network operators, energy utility service providers, fast-moving consumer goods distributors, property management companies and public records) and transforms such data into a single report. Such a report will contain a financial identity (which ascertains that borrowers are who they claim to be) and a financial profile, including a risk score and a credit capacity estimate.

Experian manages all the interactions with the data providers and takes care of the communication, data matching, ‘deduplicating’ and data cleaning. The resulting data is assembled into a FIRM report and delivered to the inquiring party in seconds.

This information is then used by the lender in combination with other available information to make an informed decision about whether or not to extend credit, the amount and the terms. This enables fairer, more inclusive, responsible and sustainable lending.

The challenge lies with implementation. With the support of the Bill and Melinda Gates Foundation, PERC and EMA undertook an extensive feasibility analysis in Kenya. While agricultural transactional data has been digitised, the majority remains physical. Consequently, as part of FIRM’s development, a commitment to digitising the data – including substantial capacity building investments for rural data furnishers – is necessary.

There were initial discussions about potential participation in the FIRM system with the Kenyan Coffee Producers Association, the Kenyan Tea Growers Association, the Kenyan Dairy Processors Association, Kenya Power, Orange, Airtel and Safaricom, among others. These conversations were generally positive, as prospective data furnishers see value in the system that was specifically designed to address their concerns about data privacy, data security, fair compensation and restricted data use.

In some cases, memorandums of understanding were secured demonstrating a high level of interest among stakeholders in the agricultural sector in promoting the FIRM solution. It has also received a grant from the Tony Emulemu Foundation and the Rockefeller Philanthropic Fund to pursue a feasibility study for a potential expansion in Tanzania.

FIRM represents the classic ‘good news/bad news’ scenario. The bad news is that margins on FIRM are expected to be quite low and will require involvement from the philanthropic community or patient capital investors to launch. It will also involve a considerable commitment to working in the trenches to gather predictive data from currently non-digital data sources such as coffee wet mills and tea factories. The good news is that although this is a front-loaded investment, it will continue to yield economic inclusion dividends for many years.

Enhancing the financial infrastructure

FIRM is not a credit bureau, nor does it compete with credit bureaus. In fact a large part of the elegance of this solution is the fact that it complements a nation’s credit reporting system by filling vital information gaps. Because traditional credit bureaus focus on financial data, the information in their databases is overwhelmingly on people who already use banking services. In many cases, it is also primarily ‘negative’ or late payment data, such as delinquencies and charge offs. In such instances, credit bureau data cannot be a driver for financial inclusion because it does not address the ‘credit invisible’ majority. Indeed, it is negative data that is used to exclude rather than include.

By contrast, the FIRM system begins with accessing data that is not contained in the databases of traditional credit bureaus. The focus is on accessing non-financial data, that is both positive (for example, timely payments and amounts paid, income data) and negative. FIRM also focuses on data for which traditional credit bureaus are ill-suited to handle, such as pre-paid data and call log data from mobile network operators. In this manner, the system is able to provide lenders actionable predictive information for purposes of credit underwriting where none was previously available.

Each time a lender extends credit on the basis of a FIRM report or value-added service (for example, a credit score), a traditional bureau benefits from having a new account created with a new traditional credit tradeline. That is, the more widely the system is used for financial inclusion, the more information will be fed into the databases of traditional bureaus making their data more valuable and generating windfall revenue for them.

FIRM data will also be made available to parties interested in generating value-added services that use alternative data. FIRM is committed to commercial viability in a competitive market context, and believes that the broad mission of driving financial inclusion is optimally met through promoting innovative solutions from a variety of sources.

Development impact

Lenders get access to new data, available for the majority of the potential borrowers and are able to better predict credit risk and affordability. Studies show that moving from a condition of no information sharing to full file information sharing make it possible to sustain average annual growth rates in lending to the private sector of 45% or more.

PERC research shows that using non-financial payment data in credit underwriting could generate the following benefits:

  • 11 million unbanked in Africa will be able to access affordable credit during first five years (Kenya, Nigeria, Tanzania and Uganda);
  • 15.4 million people who do not use banking services in Asia will be able to access affordable credit during first five years (Bangladesh, Indonesia and Pakistan);
  • average cost less than US$2 US per person for access to affordable credit;
  • increased acceptance rates across all risk tiers for any given default rate;
  • reduced defaults for any given acceptance rate;
  • sustainable growth in lending by reaching new customers; and
  • a more accurate identification of high risk/low risk borrowers.

FIRM can be used both for ‘no-hits’ (when a borrower lacks a credit report with a ‘traditional credit bureau’) and when a borrower has a credit file. Supplementing a traditional credit report with more comprehensive predictive data will improve portfolio performance and increase profit margins.

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