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Big Data: big risk, big return

Published on 12th Dec, Edition 50, 2016


How ‘big’ is Big Data? This question is often confronted with a flurry of statistics. One out of every 3 people on earth has access to mobile internet. One out of every 5 has an active Facebook account. By 2020, the amount of data generated by the world will be 44 times the data generated in 2009.

Clearly, there is no debate about the volume of data we are generating. The current debate about how ‘big’ Big Data can get is centered on how well it can be used to predict future behavior.

Big data analytics in Fintech

For financial service providers, like banks and microfinance institutions, Big Data’s predictive power can open doors they have never walked through before. Armed with the right data and algorithms, financial service providers could assess clients in terms of their future potential, rather than their past behavior. Conceptually, this is a game changer. Traditional credit scores are calculated on the basis of previous credit re-payment behavior. A forward looking, data driven approach could be groundbreaking in markets where otherwise credit-worthy individuals do not have any traditional credit history.

Research shows that in six largest emerging economies alone, leveraging Big Data may provide over half a billion individuals access to formal credit markets for the first time in their lives.

Weighing risk vs return

Despite this potentially large pay off, there are many challenges to scaling predictive power. The biggest current challenge is pairing the strongest predictive algorithms with the right data sets. The most intuitive place for leveraging Big Data for financial inclusion would be traditional banks with rich historical data. However, banks are not designed to be agile, data-mining institutions. Much of their internal data remains scattered and inaccessible. This is a challenge that must be surmounted, as banks play a critical role in financial inclusion.

Fortunately, many players innovating with credit scoring algorithms are positioning themselves as partner organizations to traditional financial institutions. However, not all banks have the risk appetite to jump at customers with poor or no traditional credit scores. As a result, many banks are cautiously waiting for newer models to prove their business value.

This conservative approach is not completely unjustified. This brings us to the second big challenge facing the financial inclusion industry – all of the Big Data players suffer from what is called a ‘recency’ bias. Most Big Data algorithms have only been in play post-2008. Traditional credit scoring, on the other hand, has withstood the test of time and matured through many credit cycles.

The profitability of Big Data analytics hinges on its ability to predict consumer behavior. Knowing the likelihood to default, for example, allows financial institutions to correctly price loans for non-traditional clients. In a changing macro environment, such as a credit boom and bust cycle, these predictions may not remain stable.

As these models scale and become more prominent, they will also attract a wider range of clientele with a different risk profile than the clients the algorithms were initially tested on.

These challenges together pose the Big Risk, Big Return conundrum holding back the data revolution for financial inclusion. What is at stake is an opportunity to finally build a sustainable financial ecosystem for the 2.5 billion individuals who are currently unbanked.

In the past, the poor have simply been excluded from the mainstream financial system. Their transactions were too small, the distances were too long, and, generally, it was just too costly to reach them.

In the last few years, however, the digital revolution has changed the cost calculation radically, to the point where banks are starting to rethink all their presumptions.  Mobile phones have become ubiquitous as today 85% of the world’s population has access to one. For the first time, transaction costs are being cut dramatically, and distances are no longer insurmountable. At the same time, the global microfinance industry has grown to serve hundreds of millions of the world’s poor and their families.

So, a safe, affordable place to save is vital, and access to appropriate credit, insurance, and payment mechanisms are arguably more critical for this class of client than any other.

In the developed world, many on the margins are rejected by banks, not because they have a bad credit history but because they have no credit history at all – or at least, none that was easily discernable. Big data can increasingly make the invisible visible, which will benefit everyone involved. By utilizing available data – from cell phones, social media connections, utility payments, government statistics, and elsewhere much-needed financial services such as loans, savings vehicles, and insurance to new segments of customers could be extended.

There are plenty of reasons to be cautious, responsible, and measured in the use of big data, but that should not stop the potential for great progress. Privacy concerns, for example, are real, and there could be a healthy societal debate on what data is and is not appropriate to use for what purposes.  But there is no question that banks can use the data that is increasingly available to rethink the way they deliver financial services for those who have been excluded in the past.

In the near future, customers will be able to receive better-tailored offers and products, while working with financiers who have a more thorough understanding of their needs and circumstances. And the competition to bring in more new customers will generate lower prices and better services for those at the base of the global economic pyramid. Surrounded by so much potential, it is hoped that technology, big data, and our experience with microfinance will have a transformational impact on the world’s unbanked and financially excluded populations.

Big data in Pakistan

In the context of Pakistan’s unbanked, telecom data and utility bill payment could be used to rate potential borrowers upon the timely payments of their utility and telephone bills. A recent research shows that non-financial data such as utility and telecom payments could predict financial defaults. This could be because an individual under financial stress is likely to de-prioritize non-financial payments such as utility bills.

Another reliable source for measuring credit worthiness could be Over the Counter (OTC) transactions data, which currently dominate mobile money transactions in Pakistan. As per the State Bank of Pakistan, last year, 66.1 million OTC transactions were carried out amounting to $2.15 billion.

Approximately 50 percent of such OTC transactions are initiated by users sending regular remittance payments to friends and family through micro-remittance corridors. It is likely that these individuals earn in cash and then the money is spent in cash by their friends and family as well. In many cases these earnings are from the informal economy and these OTC transactions provide a crucial formal link that could be analyzed more carefully to sift out unbanked individuals with regular and stable remittance history.

The unbanked in Pakistan are devoid of an integral financial security service provided by modern banking through loans. On the other hand banks are unable to tap a market segment that could potentially payback loans. A probable disconnect seems to be the way in which credit worthiness is being measured at the moment. The availability of big data coupled with the rising sophistication in data sciences implies that this disconnect can be addressed through analysis of non-financial data in a manner that is beneficial for both the banks and borrowers.

The writer is a Karachi-based freelance columnist and is a banker by profession. He could be reached on Twitter @ReluctantAhsan


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