aable has build software that addresses various aspects of the Customer credit cycle. Allowing our partners to fast-track their entry into Lending. Our software is organized in Software containers. Allowing us to deploy a dedicated Lending platform on premise of in the cloud within hours.


Machine learning

Naming our software after famous Swiss physicians, Einstein is a set of Machine learning components that allow to learn from data and calculate Score values for a large array of business processes. These Scores allow us to control Credit risk within Loan origination, Loan extension, and Collections.

Our software allows to process both structured and unstructured information. Enabling us to extract information from any type of data available. To accommodate the specifics of particular data sets, Einstein leverages three software sub-components:

Picasso is used to integrate Credit bureau and Big data sources into the scoring process. It connects flexibly to a large variety of data sources and performs inquiries at high speed.

Transact extracts information from transaction data and provides it to other scoring components. Summarizing transactions wrongly leads to a loss in predictive power. Using Machine learning our Feature engineering is optimized to avoid such loss. Leading to our scoring being able to pick up even small signals in data.

LINX extends Feature engineering to relationships between data elements. Think of the information captured by Linkedin or Facebook of “who knowns whom”. Taking into account the social or professional network of a customer can help to profile them. Using Social Network Analysis such relations can be formally described and used for Model building. Powerful insights for populations where traditional credit data may be unavailable.

Scorex serves as the central scoring component that consumes the information provided by Picasso, Transact, or LINX. Leveraging latest Machine learning algorithms it translates information into probabilities that can used within our Decision strategies.


Decision strategy

To translate score values into business decisions we’ve implemented a high-performance Decision engine. Consuming a large variety of inputs the engine allows to configure business strategies. Think of a Credit strategy for origination or how to best contact customers in Collections.

Using a Decision engine allows to change business strategies – e.g. Risk cut-offs for lending decisions – without having to write source code. This puts us in a position to make swift changes to how the business is run if needed.


Lending system

To administrate loans a Lending system is needed. This system will track the amount owed by the customer, due dates, and payments received. To meet customer demands loan products of different tenor, interest rates, and fees must be supported.

aable’s lending system Volta provides such flexibility and serves as a central system for managing customers. Being connected to the Decision engine all key actions for lending are based on statistical models. Ensuring the business is run efficiently and profitable.

Many operations of the system can be controlled through APIs. Allowing partners to connect to Volta easily. The system is designed as a white-label solution: Meaning it can branded according to the need of our partners.


Customer self-service

In the age of Digitalization customers expect convenient control over their account. The lending system Volta allows them to self service for the most common account operations.

A Smartphone APP for Android and iOS is being worked on currently. Completing the options for customers to manage their account “on the go” if desired.