The LoanHub

Mobility Lending in Africa is Broken

> 50 mln Riders (commercial vehicle drivers) move the African continent every day, using noisy and polluting ICE (Internal Combustion Engine) 2 and 3-wheelers. They represent a cornerstone of the continent’s economy and job generator #2.  Most Riders do not have the cash to pay upfront, so virtually all (80%, Mckinsey) 2 and 3 wheelers in Africa are lease-to-own. Riders pay interest rates between 70% and 200%.

Riders net take-home rate varies between 1$ and 10$, for rural Riders and Urban Riders,  some working for Ride Hailing companies respectively. More than 80% of these Boda Riders are keen on e-Bodas, which has a theoretical lower Total Cost of Ownership. There are 2 major barriers: 

1. The high CAPEX low OPEX model does not work well with high interest rates / short tenure loans. Current e-Boda fleets are either subsidized or owned by the fleet operator / fleet manager.  

2.Lenders do not have profiles of Riders, and beyond those Riders associated with Ride Hailing companies, they don't know to which Riders they can sell EVs. They don't know how to scale beyond a pilot (We have learnt this from leading tech companies in Africa like MKopa, Bolt, Bboxx). 

Mobility Lending in Africa is broken. In our view, vehicle financing is the largest obstacle for large-scale unsubsidized adoption of e-Bodas and e-Taxis. For reference, interest rates in India are <20% which is 3x the national bank rate. EVs are taking up very quickly. Kenya is looking at a +6x national bank interest rate.

InCharge understands Lending space, Mobility, and AI. We developed a  Loan Management system: the LoanHub

We ran + 300 hours of interviews with Lenders (we call this a "Data Safari"). We analyzed Lenders organization and KPIs, and came to the conclusions that we the single most important KPI and biggest driver of cost (and therefore the interest rate) is the # of vehicles per Loan Officer. Our LoanHub will increase this by a target +2X. Here's how:

Platform specifically for this and it consist of 5 services:

1. Data-integration (GPS, payments, KYC, etc) & scalable data infrastructure services laying core foundations for leveraging data insights. 
2. Our AI profiles, scores, and segments Riders to provide predictive insights.
3. The action Engine automates follow-up processes, using a Tech & Touch approach. It suggests and automates actions. 
4. A Whitelabel Rider App. The App has a loan dashboard. The Rider can earn discounts on timely paid installments, which can be used for service, grace days, or a new vehicle. 
5. We provide 1 interface to the Lender by consolidating the collected loans

I'm writing this message from Nairobi, and Im quoting a taxi Rider George  out: "This will change our lives!"

How?

- Riders are currently living form loan to loan, due to the high interest and short lifetime of EVs

- Riders typically work extremely long working hours of at least 12 hours in a very hectic unhealthy environment

- Access to EVs at low-cost loans implies that Riders can save 3 - 5 USD per day on a bike, and 10 USD per day on taxi. In both cases, this will roughly double their income. 

According to research from 2019 less than half of the taxi drivers were earning above the poverty line, Boda (motorcycle) riders tend to earn less than taxi drivers.  

The longer lifetime of the vehicle will create an ecosystem where the drivers are much less dependent on finance. 

Finally, in a country with 70% renewable electricity and lots of sun all year round, its better to be depending on electricity  prices than fuel prices. 

Roland has extensive experience in e-mobility and fintech. Roland founded a company called etukfactory and has extensive experience in EVs, battery tech, charge infra, etc.  Roland is also co-founder of Dot.ai  a Nigerian fintech focusing at the underbanked in Nigera, 

Anshul has set up a company called Bboxx, providing Solar Home Systems and other products to over 3 million customers in over 8 countries in Africa. Bboxx has raised over 300 million.  

Michiel has founded & exited an IT company that grew to 200 FTE. Michiel has set up an AI consultancy firm (he is an AI expert himself) & has been CTO  of Dot.ai. 

We bring a unique mix to the table, understanding Lending in Africa (Anshul) e-mobility (Roland), AI (Michiel) and fintech (all of us).

All the relevant experience aside: the most important thing is we are passionate about e-mobility and solving problems our end-customers: African drivers hustling every to make a living: a largely overlooked sector Africa until now. 

Our product is 80% ready. 

We tested our app with a few drivers

1. We can now ready and understand phone data such as GPS, SMS, calls to provide a rich raeal time identity to the driver and which groups they operate in.
2. The phone as a basic loan dash (which believe it or not, lesees currently do not have) and a wallet. We  

We also tested our collect bot. Based on payment data, GPS data, and drivers KYC data & personality we can automatically create engaging & convincing messages motivating the rider to make the right financial choices & life choices. We can learn from the outcomes.  

Now need to take it to a small scale pilot. Small is ~100 drivers. 

We want to meet with great minds in tech, AI, fintech. Our main objective is to find the perfect product market combination that allows us to unlock local institutional capital on behalf of our clients (who are considered unbankable and therefore rely on microfinance and the according rates) though technology. 

We have learned at the Tenity incubator, which consisted of 4 weeks in Talinn, that the external export chats were extremely valuable.  

In general, people in Africa accept the high margins as a given. 

The underlying assumption is that that Lessees in the informal sector are untrustworthy and therefore risky.  And that Lending therefore needs to be high margin. 

We believe this is a result of the high interest rate of 70% to 200%. Lenders typically maximize their interest rate by looking at average income minus coat of living. Combined with a lease-product that requires daily payment for 1 or 2 years, this bound to lead to issues in case for sickness, accidents, funerals, school fees, etc.

Lenders that use low interest rate have much lower default rate (between 2% and 4%), but don't scale quickly as its much harder to find funding for them. 

Though we fully accept that there are bad apples out there, we mostly believe in bad luck. This is a different approach all together. It has lead to:

1. We want to create a deep understanding of the lessees, so we can help him though the loan and eg save for school fees. Our onboarding tool is AI based goes beyond cracking the financial statement or submitting basic KYC. We make sure we understand tech savviness, financial literacy, ability to learn, personality, family situation, and rider group dynamics.  
2. We want to keep real time track of Riders and groups through an App offer them a transparent interface and  to benchmark them and help them understand which behaviour traits are successful.  
3. Riders already de-risk each other, and saving groups do occasionally exist. We  use tech to accommodate exactly that behaviour and to create 1 interface between a group of riders and the lender. 
4. We put our collections in context (eg how many km did you drive, what did your peers do) to create ultra relevant messages based on personality, and context

Our software is available to Lenders as SaaS, and it will also work on other lending verticals. Our longer term goal is to help financial institutions lower their interest rate towards Indian rates (3x central bank vs 6x). 

As mentioned before, we found out during our "Data Safari" Lenders design their product based on the number of vehicles a loan manager can handle. We also learned that this outweighs the risk of defaults by far and is their core challenge. 

We know we can increase the number of loans per loan manager by at least 2X. 

We also believe that this will lead to lower interest rates (in time, as this is ultimately a Lender decision).

We also believe that this will lower interest rates will increase affordability. We believe lower interest rates combined with a personal approach data based nudging & educative collections strategy, will decrease dafault and risk profile.

We also believe that lower market interest rates will lead to healthy competition.

Finally we  believe that proving our software and opening that up for the market will democratize loans and achieve our overall mission, not just for mobility. 

We are currently not measuring any SDGs. We have great contact in ESG reporting space and do not expect this to be an issue. 

InCharge is a company that is born in the 'Golden Age of AI'. We do things differently and  use AI for everything we do. Set up a training for EV? We have 10 questionnaires based on YouTube video in seconds. KYC: 

Most of these tech has not been built to scale, it has been built to prove that it can be done. 

Here's what we have & how we approach tech

1. We have a basic Android App that is able to track GPS, data, SMS, calls, etc. We have do AI analysis on the data we gather. 
2. Instead of having a multiple choice , we allow Rider to record a video. We get rioch . TO make it understandable for humans, AI can make a table out of this, but   
3. We have a low-code no-code platform called Retool and our LoanHb is working on client data and & synthesised data. 
4. We use AI to provide training & generate training modules.
5. We have a chatbot that uses our own LLM and tevh to provide the right context to the AI. Tthe entire prompt can get close to 1 A4, which is exactly why a human cant make 200 relevant calls per day. 

NB: Lessees are allowed to opt out of any App permissions they have given us. 

3,5 FTE

1 year exactly 

The entire core team (Michiel, Anshul & Roland) is fully focused & responsible for making this a success. Our team consists of experienced entrepreneurs who are passionately committed to solving big tangible problems. We also aim to hire Cas Bolwerk, an AI student from TU Eindhoven who wants to work in the impact space. 

We will also hire a local team, consisting of a GM / Business Developer / Customer Care and Product Manager. Anshul has built a very diverse local Kenyan team before with Bboxx. We are looking at hiring Winnie to become (one of) the first women-led local company in the EV Space (Automotive still is very much male dominated). Winnie Wekesa has experience as fleet manager of Nopea, and has indicated she is keen to join us.

Secondly, specifically for taxi drivers, we have partnered with a local NGO promoting female drivers for EVs. 

We offer Lenders a service of de-risking loans & automating collection management. We then consolidate collections, for a success-based 5% collection fee.

We have excellent traction with Lenders, and now partnered up with MyBoda and Platcorp. Platcorp is the largest microfinance institution of East Africa. 

We know this problem of collections is real for virtually every Lender in Africa, particularly for the informal segment.  

We decided not to engage more Lenders and prove our concept first. We are confident we can get 2X as we have good understanding  of the average day of an average loan manager. His primary task is calling & texting  for collection, and according loan manager themselves 70% of these calls can be automated (replaced by SMS or AI voice message)  and even improved as they have 200 interactions per day to complete.