At the core, our stack uses 4 key technologies: AI, IoT, big data and mobile technologies.
Our IoT devices use mobile Internet to source and transmit big data that we process and store in our data warehouse. Machine learning models analyse the data to predict and detect meaningful insights that help us confidently and reliably advance credit to our customer. Mobile money enables us to issue credit and process repayments from our customers.
Internet of things is a system of uniquely identified devices that have the ability to transfer data over the Internet without requiring human-to-human or human-to-computer interaction.
Our IoT devices track vehicle-based diagnostic and non-diagnostic data e.g. trips, runtime, utilization, odometer reading, fuel consumption, DTCs etc from our assets. This data is then streamed in real-time to our backend and stored in our data warehouse for further processing.
Big data describes a large volume of structured, semi-structured and unstructured data. It is often characterized by the 3Vs: the extreme volume of data, the wide variety of data and the velocity at which the data is processed.
We collect petabyte sized data from our assets. We use the latest tools to pipeline, organise and store the data. Which is then mined for information and used in our machine learning models and other advanced analytics applications.
Machine learning is the use of algorithms and statistical models to perform a task without explicit instructions. It enables computer programs to access data and use it to gain knowledge by themselves.
We deploy machine learning to make sense of the huge troves of data collected from our assets. Our typical use cases are: We build predictive models to maintain quality of our loan portfolio. We analyze and detect anomalies in asset behavior to detect and mitigate fraud.
Mobile internet enables the transmission of data through the Internet using mobile infrastructure. Mobile money is the transfer of e-value from one party to another using mobile infrastructure.
Mobile Internet enables us to collect data from our assets in the field. And mobile money enables us to disburse our products and collect repayments from our assets. This enables us to service our customers from anywhere without on the the ground presence.
A loan paid directly to a dealer to purchase a motorcycle, helmet and vest, boots or a mobile phone. The loan amount is between USD 1,000 - USD 1,500 to be repaid within 1 year.
A loan paid directly to the insurance company or agent to obtain insurance cover. The loan amount is between USD 50 - USD 100 to be repaid within 2 months.
A loan paid directly to the fuel station to purchase fuel products like fuel and engine oil. The loan amount is between USD 2 - USD 10 to be repaid within 7 days.
A loan paid directly to the service station to obtain minor and major service and spare parts. The loan amount is between USD 10 - USD 50 to be repaid within 30 days.
We are solving a tough problem, where our typical customer is unbanked with little or no credit history. We collect alternative data from various sources to assist in assesing the credit risk of our customer.