Making technical assistance work in Africa: Three keys to success for financial services providers

Making technical assistance work in Africa: Three keys to success for financial services providers

16 October, 2019    

It’s not easy delivering data-driven financial services to the financially excluded in Africa’s emerging economies.

That’s why technical assistance (TA) can be essential to the success of the region’s financial service providers (FSPs). Comprised of various forms of non-financial assistance provided by outside specialists, TA can take the form of information and expertise, instruction or skills training, and consulting and other services – all with the goal of optimising the effectiveness of an organisation’s use of data and advanced analytics to inform decision making.

Over the course of the insight2impact (i2i) programme, we provided technical assistance to seven financial service providers across Africa, to build their capacity to incorporate client-centric research and data into their decision-making and to help inform the direction of their businesses. There were two aims for this TA: The first was to advance financial inclusion through better use of client research and data. The second was to understand the benefits and costs of different models to effectively deliver TA in the continent’s changing financial services landscape.

To understand the latter, we tested three different TA models: full TA programs, “TA lite” and fintech TA. The full TA model provided funding to the FSP for assistance from data science consultants and experts, who would then work closely with an internal resource within the FSP to upskill them. The TA lite model funded a dedicated assistant (for example, a data science intern) to work on a specific time-bound project within the FSP for at least 12 months. It’s considered “lite” because it is less intense than the six-month approach conducted by a consultancy company in a full TA program. The fintech TA model was similar to the TA lite approach, with an identical implementation model, but it involved placing a data scientist in a fintech, under the guidance of a data science expert.

We found that there were some common learnings across these three TA models that can be leveraged to more effectively deliver TA in the future:

Operational buy-in is as important as executive buy-in

When we started approaching FSPs to offer TA at the beginning of i2i, we targeted executives – as it was accepted best practice that executive or strategic buy-in was critical for TA programmes’ success. But when we went to implement the programs, we realised something: While executive buy-in is important, if there is no support during project implementation – either via direct involvement from the executives or a data science champion – the TA can still crumble.

For example, with one of the FSPs we worked with (to protect their identity, let’s call them Company A), we had significant buy-in from the executive management and leadership. But there was no operational support for the data scientists provided by i2i, resulting in significant implementation challenges. For example, the organisation had no central data repository, leading to “data silos” that made it difficult – and sometimes impossible – to access data across its various divisions/departments without assistance. On the other hand, when we worked with a different FSP (Company B) where we placed two data science interns with an internal project mentor and champion, the mentor was able to support them in navigating the organisation, resulting in more effective TA.

This experience taught us an important lesson: It is critical to include both the executives and operational team involved in the TA at the outset, to ensure buy-in across the relevant business units of the FSP – and the support of an internal operations champion is key.

 Share progress early and often

We’ve found that keeping a company focused on longer-term goals throughout the TA process can be tricky. The outcomes from TA programmes we conducted often took longer than both parties expected, leading to frustration. Companies want to see an impact on their bottom line, sooner rather than later. Yet sometimes the companies themselves weren’t clear about their motivations for seeking TA. For example, with Company A, it was unclear at times what the company was hoping to achieve with the additional data capacity we were helping them develop, and what their reason was for undertaking the TA.

To address this, we’ve found that breaking down the data journey into various milestones (e.g. establishing specific use cases) – and delivering and reporting on those goals periodically – helps the company identify ways to utilise TA to create longer-term value. This approach also provides the FSP with a better understanding of their customers and helps them to recognise the value of changing the way they see and work with data.

 More basic = more impactful

When i2i set out to deliver TA programs that advance financial inclusion, we had many ambitious ideas about potential data uses cases – ranging from using call data records to inform mobile money agents, to assessing social media activity to conduct credit scoring. What we quickly learned was that many of the FSPs we are dealing within the region are at the early stages of their data maturity journey. Their data capacity is limited to reporting on critical business operations (e.g. financial performance), and there are generally no formal business intelligence and analytics tools or standards in place at that stage. The majority still use spreadsheets as a primary means of reporting.

Recognising this, we needed to match our desires for advanced use cases with these FSPs’ actual needs, which were largely basic. The services they needed most from us included data cleaning, query-running, and other labour-intensive tasks that would be unnecessarily expensive for a consultant to conduct. We found that offering these basic services effectively, at a low cost, had the potential to make a substantial impact on FSPs’ internal data dealings – and this would deliver more powerful benefits for their long-term data capacity. For example, in our work with one of our TA lite partners (Company C), we placed a data science intern internally to focus on customer segmentation, churn analysis and churn prediction for the first few months of their work. With another TA lite partner (Company D) a database administrator was hired to manage and monitor the performance of the customer database, guided by consultants at a conceptual level.

A key learning for us was that, when conducting data mining and analysis, it is generally far more cost-effective for FSPs to hire internal data analysts that work with external consultants than it is to hire an external consultant. As shown by the experiences of Company C and D, the technical work these processes involve is simple enough that an entry-level data analyst can carry it out under the instruction of a consultant.

Looking ahead, we are optimistic that these learnings can help us and others in the space to more effectively deliver TA in the future. Our key takeaways for organisations considering participating in TA programs are:

  • Get everyone on board: Buy-in and support at all relevant levels is crucial.
  • Share insights with the company often, to keep their eyes on the (longer-term) prize.
  • Start how you aim to finish: In an in-person session, outline the reason and approach for the data work prior to the beginning of the TA so that it moves in the right direction from the start.

If you would like to learn more about the impact of our TA programmes or explore further learnings around them, please reach out Dumisani Dube at

This article was first published on Next Billion as part of the Openi2i series an initiative of Cenfri and Finmark Trust.

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