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Data Makes For Big Innovation

Earlier this month, this newspaper led with a headline that Safaricom’s Fuliza product lent Kshs 6.2 billion in its first month after launch. In case you’re one of those laggards that hasn’t entered the mpesa universe yet, Fuliza was launched by Safaricom in January 2019. Its objective is to help the mpesa user avoid that embarrassing “oh-no” moment when goods or services that she wishes to purchase are literally in hand but the funds to pay are not. I signed up for the product following an SMS blitz by Safaricom as soon as it was launched for no other reason than to just stop the confounded messages coming through. Two weeks later I stood at the supermarket till purchasing items via Mpesa. Lord help me because I came up short, Kshs 434.74 to be precise. Usually I would give a sheepish grin to both the cashier and to the visibly irritated customers behind me and mumble something about “please let me withdraw from my bank” and have to wait several nail biting, interminable minutes as my bank’s mobile app chooses to be slow on that day at that moment. But the Mpesa app immediately prompted me to Fuliza – which, by the way, means “continue” in Swahili. In seconds I had been allowed to overdraw my Mpesa by that amount, the transaction was completed, I got an update that I was charged the princely amount of Kshs 4.35 for the overdraft facility and I now owed Kshs 439.09 due in 30 days. Most importantly, the fellows standing in line behind me never knew that I had run out of funds. At all. The next day I withdrew funds from my bank into Mpesa. Again I got a message in a split second, the outstanding amount had been automatically deducted from my funds. And my available limit was back to the Kshs 12,000 that I had automatically been awarded when I signed up.

 

Fuliza is a testimony to those two words you see being bandied about miscellaneously: “big data”. Big data are extremely large data sets that may be analysed to reveal patters, trends and associations relating to human behavior and interactions. CBA Bank, the creators of the first Mpesa based lending product Mshwari, used mpesa usage data to feed into their credit algorithm that calculated how credit worthy the loan applicants were. It soon became apparent that about 58% of mpesa transactions failed where the user was sending money to another beneficiary. But about 85% of the same transactions would be repeated within two days, that is, payment to the same beneficiary because funds were now available. It doesn’t take a rocket scientist to see that the data was speaking to a funding gap that would be eliminated within 48 hours as cash came in. In banking-speak this is what an overdraft does: provide a short term cash bridge pending arrival of funds. In the example above, my overdraft interest rate was 1% for a 30 day facility.

 

If you were to ask the over 400,000 customers that are using Fuliza daily as to what the annualized interest rate (12%) is, they’d tell you that they didn’t care. I certainly didn’t at the point where I was standing at the till with a basket of goods already packed and carefully perched on the counter ready for my hasty exit. Actually neither do the millions of Mshwari customers who are ready to pay a flat fee of 7.5% for a 30 day loan (again, if you annualized that you would get 90%). The Fuliza product currently endures a default rate of less than 1%. So for every 100 shillings that are lent out, less than 1 shilling is lost. The automatic limit that I received of Kshs 12,000 was done without my asking and without my knowledge. The bank just used my data to generate a very important product for me.

 

 

To the Kenyan legislature, the lesson here is this: a little bit of research would have led you to see how you could force banks to use the reams of data that they have about their customers to provide a better and differentiated pricing which would have achieved the goal of lowering the cost of loans. Instead, the largely uninformed interest cap route taken has ended up drying credit supply. What these mobile loan applications are telling parliamentarians is that at the end of the day, the retail client is indifferent to the price. He just wants to “fuliza” his life!

 

[email protected]

Twitter: @carolmusyoka

 

 

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Artificial Intelligence In The Boardroom

“Algorithm appointed board director” was the title of an article on the BBC News website on 16th May 2014.  “Artificial intelligence gets a seat in the boardroom” was a similar headline three years later on 17th May 2017 on the Nikkei Asian Review news website. Both articles were referring to a computer algorithm called Vital that had been “appointed” to the board of directors of a Hong Kong venture capital firm known as Deep Knowledge Ventures. Citing the Nikkei Asian Review article, “Dmitry Kaminskiy, managing partner of Deep Knowledge Ventures (DKV), believes that the fund would have gone under without Vital because it would have invested in “overhyped projects.” Vital helped the board to make more logical decisions, he said.”

 

By using an algorithm that could sift through masses of data on past investments, the company was able to narrow down on what the least risky investments were in the biotech space that they were playing in. The article continues, “DKV started as a traditional biotechnology fund, with a team of advisers and analysts using traditional methods for trend analysis and due diligence. But the biotech sector has a very high failure rate, with around 96% of drugs not successfully completing clinical trials. DKV then acquired a team of specialists in the analysis of big data – large data sets that can be analyzed by computers to reveal patterns. The team created Vital, the first artificial intelligence system for biotech investment analysis, enabling the fund to identify more than 50 parameters that were critical for assessing risk factors. Kaminsky said: ‘ As we analyzed more and more companies, we were failing to identify those patterns and factors that made a company likely to achieve success. But surprisingly, as we began to analyze thousands of companies, we discovered certain parameters that were good at predicting the risk of failure.’ ”

 

The primary role of a director is twofold: a monitoring and oversight role of past decisions made by management and a forward looking role to oversee formation and execution of strategy. In the DKV example cited above, the role of the algorithm was to help the venture capital board make the right investment decisions. Using big data, the algorithm was able to narrow down which specific drug research areas were yielding better outcomes and provided support to the board on which drug companies to invest in.

 

How could this translate to other non-investing type of companies? It is easy to draw a parallel to the banking industry for example where bank boards have to review and approve lending decisions based on analysis that has been done by a credit manager. While smaller loans have already moved to algorithm based decision making (Mshwari is a good example), the bigger and more complex loans still require human analysis largely due to a poor use of big data within the banking industry. Not sharing historical lending data, which can be easily done on a no-name basis to protect client confidentiality, prevents the banking industry from building a critical database that can be used to provide granular risk patterns for different market and industry segments.

 

While it can be argued that the information is being shared at a credit reference bureau level, what remains to be seen is how this information can be consolidated, analyzed and churned back to the banks to use for determination of probability of repayment. But credit risk analysis which is largely technical, is mainly a management undertaking, and brought to the board for approval. Having AI sort out that decision at management level would significantly reduce the work of the credit committee of the board. One can further argue that AI can also review the entire lending book of the bank, assess the current and potential portfolio at risk, and determine what amount of provisioning is required, as is currently demanded by the new international accounting standards. Which would then eliminate the need for numerous risk analysts within bank management.

 

AI could also potentially review the financial reports produced by management (if not produce the reports themselves) for accuracy. We could go very far with this argument, which is that if machines are able to do a lot more of the monitoring role that management undertakes and reports to the bank’s board, then technically, a lot of the work of the bank board can be reduced to oversight on the formulation and execution of strategy and the more human role of oversight of  key stakeholder engagement such as employees, customers and regulators. The DKV example is really a hyped version of a management decision making tool that is being elevated to board use. But it does spur some thinking for both directors and management on how daily operating decisions can be moved to more accurate algorithm driven processes.

 

[email protected]

Twitter: @carolmusyoka

 

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Open Data Open Innovation

[vc_row][vc_column width=”2/3″][vc_column_text]I had an interesting lunch with a Tweep the other day, an indefatigable mobile Wikipedia on technology trends both locally and globally. Our conversation turned to open data and how it can be applied in the banking industry. I have to admit I had heard of the term open data but never really paid any attention to its potentially game changing application in the financial industry. “If Kenyan banks converted their records into open data, it would lead to greater financial innovation and a better product experience for customers,” said the tech pundit. I put on my fairly ignorant and thoroughly obtuse nitpicking hat on. “Banks cannot share such sensitive data, there’s customer confidentiality to be maintained and quite frankly, such information is a key intangible asset that the bank has,” I retorted. He proved to be quite unflappable and converted my healthy skepticism into acquiescence with just one question: who said that the data provided should be given with the client name?
I was an immediate convert. If banks openly shared customer data to fintech providers, the third party would have a treasure trove of information on customer spending habits, borrowing tendencies, repayment history, saving culture and basically the whole kit and caboodle of a client’s behavior. According to the Central Bank of Kenya’s latest annual banking supervision report, for the year ending December 2015, there were about 34.6 million banking accounts in Kenya and these numbers include mobile banking accounts of the Mshwari and KCB M-pesa extraction. That is 34.6 million data sets that can clearly demonstrate spending, borrowing and savings behavior within a certain age, gender, regional demographicor business segment, which can lead to finer product targeting and pricing.
The United Kingdom (UK) is a trailblazer in this area and in January 2015, Her Majesty’s Treasury launched a “call to evidence” asking stakeholders in the financial industry on how best to deliver an open standard for application programming interfaces (APIs) in UK banking and to ask whether more open data in banking could benefit consumers.
Application programming interfaces, or APIs, allow two pieces of software to interact with each other. In banking, APIs can be used to enable financial technology (fintech) firms to make use of customers’ bank data on their behalf and with their permission in innovative and helpful ways. For instance mpesa payment platforms for businesses make use of APIs supported by Safaricom.
The aim was to produce an open API standard for UK banks to drive more competition in banking and help the UK remain at the forefront of financial technology. The report was published less than 3 short months later in March 2015.
In summary the responses from the forty respondents who included a number of banks, fintechs, the Law Society of Scotland, the Association of Accounting Technicians and the British Banking Association raised concerns around privacy of customer data and fraudulent use of that data. The need for appropriate security and vetting systems for third party providers was a key concern. The respondents did note that open data in banking would enable customers make more informed decisions on which banking products to purchase and who to bank with. An Open Banking Working Group, bringing together key stakeholders such as banks, fintechs, consumer bodies and government, was then created and an Open Banking Standard (OBS) was produced. The OBS is a guide for how banking data should be created, shared and used.The group recommended that an independent authority should be established to ensure standards and obligations between participants are upheld. The authority would govern how data is secured once shared and the security, usability, reliability and scalability of APIs. It would also vet third parties, accredit solutions and maintain a whitelist of approved firms. The UK is cautiously but steadily moving towards this standard, with the key premise being that customers will have to consent to their data being shared.
Back in the +254, we have already established ourselves as early adopters in the fintech space with the amazing innovations that have been generated by the mpesa phenomena. Moving towards open data may perhaps be the key that will unlock the risk based customer loan pricing that the interest rate capping has miserably failed to deliver. It would also provide much needed customer portability on banking services generated by product pricing sense rather than brand affinity.

[email protected]: @carolmusyoka[/vc_column_text][/vc_column][vc_column width=”1/3″][/vc_column][/vc_row]

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Banks have to go mobile to stay relevant

The 2016 FinAccess Household Survey – published in February 2016 by Financial Sector Deepening (FSD) Kenya – provides the most recent data of Kenyan behavior around consumption of financial products and services and is a treasure trove of information for any banking strategist.One key finding was the use of credit. In what reflects the wealth distribution within the Kenyan population, 57.3% of the survey respondents in the research reported that they take credit to meet their day-to-day needs. The second highest need for credit was to pay school fees at 21.5% and only 15.8% were using credit to generate wealth in the form of business loans.

Having a customer who has insatiable credit needs is banking nirvana. The question is how to do so in a manner that will be cost effective with minimal loan loss potential. The FinAccess Household Survey should be read together with yet another FSD research paper titled the Financial Access Geospatial Mapping Report launched in October 2015. The report essentially tracks access to financial services across the Kenyan geography, using data from Kenya National Bureau of Statistics, with unsurprising results.

Answering the question as to how many service access points exists per 100,000 people, the report finds that there are 3 banks, 1.5 ATMs and 32 bank agents serving that population. It gets more interesting as you start to look at the extent of mobile money penetration. Mobile money access points are 54 times that of banks at 163 with mobile money agents growing from approximately 48,000 locations in 2013 to nearly 66,000 locations in 2015 which is a 37% growth. Meanwhile, population within 3 kilometers of an ATM remained stagnant at 23% in the two years. Bank branches grew a paltry 1% from 26% to 27%, while bank agents grew from 53% to 60% in the same radius.

What is the data saying? The average Kenyan uses credit heavily to support his basic lifestyle and is nearer a mobile money access point than to a bank. The growth of mobile money agents demonstrates very low barriers to entry and should inform a bank’s decision on whether to purchase an ATM – whose price ranges from Kshs 2 million to Kshs 4 million depending on whether it has deposit taking capabilities – or whether to invest in deepening its mobile banking platform to deliver products through a wider customer delivery channel (at no cost to the bank) that is growing exponentially year on year.The interest rate capping on loans may have curtailed bank appetite for formal unsecured lending, but the two mobile loan products of KCB Mpesa and Mshwari continue to enjoy unfettered demand and have survived the interest capping law due to their fee based rather than interest rate based pricing which the average borrower is apparently indifferent to. The lesson here for the proponents of the interest capping law is that the average Kenyan who is trying to survive is more interested in access to credit than in the actual cost of that credit. The growth of mobile access points demonstrates that it is the preferred mode of not only transferring money but also storing that monetary value.
The critical question bank strategists should be asking themselves is how to piggyback off the cheap mobile agent network to provide loans and take deposits. The evidence already points to the need for smaller branches, fewer ATMS and greater use of historical mobile use data to generate personal credit ratings. Developing mobile banking applications for the average Kenyan is what will separate the chaff from the rice in the future banking industry.