• Samuel White, Regional Director of Asia and the Middle East at Creditinfo Group


• 24.08.2022 02:15 pm
#data

Weighing up risk is a key part of deciding who gets access to financial services. Most financial institutions (FIs) around the world work out the risk profiles of potential customers by looking at ‘traditional’ data sources – such as utility bills and credit card statements – and using these to work out a credit score.

However, many people have no bank account and so have no way of running up a credit history. In fact, over 2 billion people have limited or no access to financial services and the majority of these people are living and working in emerging markets.

Financial inclusion is a universal right. It’s key to helping less economically developed areas to grow and mature, alleviating poverty for the population and helping to improve the overall quality of life, which is why the United Nations has it as goal number seven of its sustainable development goals.

How can FIs tap into the banking insights of the unbanked?

With this in mind, how can financial institutions calculate risk among the billions of people who are still underserved because they have never had a bank account or opened a credit line?

It’s time to think outside the box and work out what information can be used. There is an abundance of information out there about the underbanked and the true risk they pose to financial institutions. For example, in the Philippines, only 34.5% of people have their own bank accounts. Yet there are over 1.5 mobile phones in the country per person.

Market-level credit scores can be built using alternative data gathered from sources in emerging markets such as prepaid mobile phone plans, fintech applications, and even people’s social media use. In this way, financial institutions can tap into insights outside of traditional sources and use these to widen the pool of potential customers in these regions without increasing their own risk profile.

How does this work in the real world?

The data is out there. The real problem is equipping banks with the capability to collect and interpret this information in order to make a properly informed lending decision. In Pakistan, for example, alternative data is currently being utilised to facilitate access to loans.

Pakistan’s traditional system of granting housing loans currently isn’t working for its inhabitants or its economy. While 10 million of Pakistan’s citizens have access to formal credit, around 105 million of the adult population does not. This has meant the number of housing loans that have been granted in the country is insignificant compared to the number of people living there. But banks have had little formal credit data to base their decision-making off, which has seemingly left them with their hands tied when it comes to granting more loans.

In a period of global economic downturn, the government in Pakistan is considering all its options when it comes to bringing more liquidity into the market and ensuring a flow of capital helps to keep the economy buoyant.

How alternative data is improving lending in Pakistan

A lack of access to housing loans is currently a big obstacle to the country’s economic growth.

The government has therefore decided to support an initiative set up by the Pakistan Banks’ Association (PBA). The project combines traditional data sources such as internal banking data and credit bureau data with alternative data sources – such as mobile metadata – to widen the pool of potential applicants for housing loans.

The PBA partnered with Creditinfo to lead the credit bureaus and rating agencies towards the initiative’s goal by bridging the gap between financial institutions and accessing this alternative data. It also provided financial organisations with the tools to analyse the data and incorporate it into their lending decisions.

The proportion of the population of Pakistan that has a data footprint capable of informing robust credit risk decisions for financial institutions has been actively increased through the joint development of a market-level application scorecard and income estimation model. Even those in low-income segments previously excluded from traditional housing finance now have the chance to access to loans, while the economy is being boosted by the increased flow of capital.

The project is the first of its kind in the country, and its success has prompted the government to back other initiatives which it hopes will also accelerate economic activity and increase job opportunities. For example, the PBA and Creditinfo project will specifically support the Naya Pakistan Housing Programme (NPHP), which aims to provide affordable housing to deserving individuals in Pakistan.

Alternative data will be key to replicating the success

As the economic effects of the global pandemic continue to be felt around the world, emerging markets are going to be hit the hardest as financial institutions look to reduce the risk they take on and tighten their belts. Leveraging alternative data to support financial institutions and credit agencies and help people to access credit will be crucial to economic recovery as well as subsequent growth in these regions.