Big Data analytics at heart of Fuliza, M-Pesa’s success

In the recent past we have experienced re-invention of business models. At the heart of these changes are forces dictated by the regulatory environment, technological changes and shifting consumer dynamics.

The impact of these dynamic shifts is defining the disruptive state of the financial and technology sectors. We're witnessing an epic, fundamental shift in how technology integrates with, alters, and improves society and its functions.

Safaricom #ticker:SCOM is defining the way data is creating meaning. And they are using that data to churn out new products cross-sold to their customer base.

The recent Fuliza M-Pesa overdraft is such a product born out of this data insights (our transactional behaviour) and furled by data.

In the wake of its launch, Safaricom CEO Bob Collimore announced that the product had exceeded expectations, hitting one million subscriptions within the first week, while its loan book hit Sh1 billion with underwriting from Kenya Commercial Bank and Commercial bank of Africa.

The Fuliza performance signals that we are entering a new era where technology trumps regulations.

Fuliza has been able to demonstrate that through partnerships with lending institutions, one can bypass the barrier of regulatory confines and deploy products that are rich in data analytics trends.
But how can a telco be able to achieve these feat yet banks have been there much longer? Big data analytics is at the heart of this transformation.

The earliest use of large data sets does not differ substantially from how big data is being used today.

What has changed is that computers have become more powerful, computing resources have drastically dropped in price, many different sources of data are now available, and several big data technologies exist that allow for efficiently managing and extracting information from large data sets.

Telco companies by default are technology driven compared to banks. This means they have invested in latest cutting edge technologies that analyse large data sets for decision making compared to banks.

The first aspect that differentiates the two sectors is volume of data. The financial and credit scoring industries are no strangers to data. These sectors have been accustomed to data ever since credit bureaus first started gathering consumer credit information a few decades ago.

However, they seem to be struggling in the ability to analyse big data which, unlike the traditional data that they are accustomed to, is more unstructured. Voice analytics, call data records metadata and social network analytics are examples of unstructured data that telcos have at their disposal and have the ability to analyse for lending determination.

Telecommunication service providers have, for example, a massive transactional database where they record call behaviour of their customers. The second aspect is the velocity of data. This refers to the rate at which data is being collected, stored and analysed.

The traditional banking and credit score industry does batch processing of data. Hence the reason approval of loan applications takes hours if not days. In the mobile lending era, the data generated by consumers on their mobile phones is produced and processed at a faster speed.

With the rise of dynamic databases that are updated on a minute- by-minute basis, credit applicants are approved in a matter of milli-seconds.

Banking data is mostly structured while telco data is captured in both structured and unstructured formats which include information from sources such as mobile apps, telecommunications sources, social media data such as connections on LinkedIn, website clickstream and voice response logs.

This increase in the variety of data sources allows a telco to get a more detailed view of the person, compared to the more traditional data sources such as credit bureaus. Banks are increasingly in a catch 22 situation: erosion of market share, the IFRS 9 looming, and declining revenue legacies.

These forces are seeing the need for banking institutions to collect almost precision level data of their customers and find creative ways of funding infrastructure that is needed to properly collect, store, and analyse big data.

Lastly, recruit highly trained data scientists who are able to churn models from unstructured sources.