FEASIBILITY STUDY: Determine the potential to support the work of DCA by analyzing new digital data sources.

Overarching research question

USAID/DCA is interested in understanding financial access gaps in real time, so we can use that information to inform new guarantees to fill those gaps.

The purpose of this exercise is not primarily to answer that question, but rather to determine the feasibility of using new sources of digital data for insight about Kenyans’ access to finance. The purpose of this report is to paint a picture of the big data landscape in Kenya, show some preliminary findings, and lay the groundwork for further investigation (both by highlighting the possibilities and potential challenges).

Purpose of this report

What is Big Data?

In the digital age, data is captured constantly as people go about their daily lives. “Big Data” describes this deluge. Big Data is characterized by the “3 Vs:” greater volume, more variety, and a higher rate of velocity and includes a wide variety of different data types, for example:

Social data, such as that on Twitter or Facebook Images, such as those captured via Satellite Observed behavioral data, such as use of mobile phones or services

We are still learning how to best use big data– what it can be useful for, how it complements traditional data streams, and how best to analyze it.

Big data?

Kenya’s Digital Demographics • As of December 2012, 78.0% of Kenya’s adult population had

mobile phones. • As of September 2012, it was estimated that only 7% of these

phones were smart phones • But in January 2013, Safaricom’s launched a new that sold out in

less than two weeks. • As of December 2012, internet stood at 9.4 million subscriptions,

representing a growth of 75.1% of internet subscriptions over the same period the previous year.

• Including non-subscribers, 41.1% of the population was accessing internet by December 2012.

Phone Ownership in Kenya

2009 survey showed that there is some mobile ownership among every income bracket in the country.

DCA Client Survey

Global Pulse surveyed 10 DCA clients from Kenya Commercial Bank. All of these clients were farmers, from peri-urban or rural areas in Central Kenya.

Building the Monitors What is a monitor? Monitors filter the billions of Twitter posts according to particular specifications, including geography and topic. Global Pulse used the Crimson Hexagon platform to build the monitors.

Building the Taxonomy Step One • Survey bank clients to find relevant key words to form the

backbone of the initial exploration

Step Two • Test and refine taxonomy iteratively by exploring at Twitter data Step Three: • Exclude words that create “noise” in the data (ie. irrelevant

posts) • Example:

– #STOLEN Hooker walks into an Equity branch... "nataka loan ya kupanua

biashara." (by @MweuDeh 40 times in one day)

monitor (loan OR loans OR mkopo OR wakopo) AND ("Top up" OR "Payback period" OR installments OR expansion OR mpesa OR mbesa OR financing OR "business financing" OR biashara OR dairy OR msoto OR red OR doh OR qualify OR stocking OR application OR maximum OR duration OR interests OR delay OR security OR "land title" OR deed OR "deposit dates" OR tembelea OR "fixed deposit receipts" OR secured OR "calculated interest" OR interest OR guarantees OR guarantor OR lawyer OR Agricultural OR agriculture OR development OR application OR procedures OR payback OR improvement OR n’gombe OR wakora OR repay OR balance OR "agreement letter" OR period OR clear OR siri OR security OR sambaza OR defaulted OR "cooperative society" OR Faulu OR credit OR Agrovets OR mfugo OR zidisha OR "penalty charges" OR penalty OR Emergency OR "ketes temiship" OR inflation OR expectations OR capital OR terms OR payment OR "nilitemelea banki" OR farm OR status OR assets OR asset OR mshwari OR land OR animal OR animals OR "long term" OR "short term" OR "mini statement" OR "mini statements" OR ministatements OR "shamba shape ups" OR "fixed accounts" OR mshwari OR zidisha OR bank OR banki) AND -helb AND -@MweuDeh AND -hooker AND -@helbpage AND -Hooker AND -@HELBpage AND –“car-jacker” AND -Chelsea AND –Manchester

General Loan Monitor Initial Categories Final Categories I want a loan General Loan, positive -Business General Loan, negative -Personal Supplying information about loans I have a loan, negative Seeking information about loans -Business -Personal Jokes and unrelated news were excluded I have a loan, positive -Business

-PersonalI have a loan, neutral -Business -Personal Information seeking Information provision

Not enough data to distinguish between “I want a loan” and “I have a loan”. Not enough data to distinguish between “I want

a loan” and “I have a loan”.

It is clear that much of the growth in chatter about loans is related to M-shwari.

General Nature of Loan Chatter Jan 1 2013 to March 14, 2013 (before and after M-shwari, a mobile lending service, is launched)

I need a bizness loan..interest is double

Understanding the positives and negatives

surrounding bank loans help people make the wisest

decisions when they need loans.

Loans by Sector

Specific DCA Partner

Name blanked

Google Trends Google Trends makes tools publically available to track the volume of searches over time by country. Using Google Trends, it is possible to: • Track relative changes in search volumes over time. • Compare different search volumes.

Limitations • Can’t create subcategories within one search term • Only one word that commonly occurs with the initial keyword is

given.

Searches for “loan”

There is no straightforward way to create sub-categories with-in overall loan searches. In Google Trends there are two ways to approximate this. First is to specify a full search phrase in quotes, for example “business loan” or “personal loan.” No data was returned. Second is to exclude words from the search, for example “Loan -student.” Google Trends shows the top co-occurring word with “loan,” which is HELB, the student loan authority.

Other digital data In addition to easily accessible data, a great deal of digital data exists that requires additional work to have access to. This includes mobile phone data and information found on disparate websites. Mobile services Partnerships or subscriptions may be required to access the data Website A great deal of information is available online which is updated as things change. While it doesn’t make sense to gather this information by hand, a “scraper” can be built to automatically collect the data and integrate it into a useable format.

Example of Relevant Website: Equity Bank

Key Opportunities • Twitter is being used to seek and share information about loans, especially

mobile loans. • Twitter is being used to share and comment on the news related to personal and

business loans. • Monitoring the news, as well as how the news is being perceived, might be of

interest. • Much of the data is related to M-shwari and other Safaricom related topics. A

future iteration of the monitors could focus solely on non-traditional banking or exclude M-shwari to focus solely on traditional bank loans.

• While there is not enough data to train the monitors into further subcategories, the low number of posts makes it possible to filter the tweets and actually glance through them. This does not constitute anything statistically significant, but could serve to bring in new ideas, similar to the role of some focus groups.

• For banks that have a robust social media strategy, monitoring the specific Twitter handle of the bank could provide insight into (1) products and services available at the bank, and, to a lesser extent, (2) information seeking behavior.

Key Challenges • Across all monitors, the overall volume of chatter is low, so small

changes in Twitter behavior, for example due to a popular retweet or the behavior of one Twitter user, can create spikes in the charts.

• There is very little general chatter about loans (i.e., chatter is largely driven news/ events or information seeking).

• There is little demographic data available, so even where there may be Tweets that are directly related to barriers to accessing loans, it is difficult to know whether they represent underserved entrepreneurs.

• There is a lot of noise in the data. For KCB, this noise includes sports chatter. For both banks, this includes news item related to the overall business of the bank, not necessarily directly related to bank services.

What comes next? Big data projects work best when they are iterative experiments. It is good to be imaginative in how this data can be used. DCA will meet with UNGP team to discuss findings. Most of the growth in loan chatter in Kenya is related to mobile banking– in particular to information seeking behavior. Focusing the monitor on mobile banking might shed light on non-traditional banking. Other digital data might be more useful at this stage than Twitter, but accessing the data may require new partnerships (in the case of mobile data) or new capacities within USAID (in the case of scraping data).