Use cases

Predictive models built using limited data sources don't always give a holistic view of the applicant. This can be challenging when assessing credit risk. Robust models benefit from a comprehensive set of variables brought together to generate a single or suite scores.

To offer personalised products and services at the onboarding stage, businesses need to understand their customer's financial circumstances.

Typically this process starts by requesting a PDF statement from the customer. Bank transactions are then manually categorised to understand how and where the customer spends their money. Affordability and financial health are calculated to assess the risk of onboarding the customer.

This process can be costly, time consuming, and provides a window for the customer to consider competitor offerings.

Businesses need to ensure that the people, merchants, or companies that they are paying are the correct ones.

In industries where fraud presents a high risk to revenue, verifying a customer's account ownership has never been more important.

Historically credit risk decisions have been made on self-declared information provided by the customer combined with information from credit reference agencies.

While decisions can be made, underwriters often lack the comprehensive, real-time view they need to make confident decisions.

Risk managers need the most accurate data to keep their portfolios healthy and provide a personalised service to their customer.

Understanding the customer's buying behaviours, financial health, and unique requirements, helps drive the lifetime value of each customer and reduces churn.

Collections & recoveries teams need to understand their customer's repayment potential to make informed decisions on when and how much to collect. Agents need data that is real-time and accurate to successfully rehabilitate a customer or to optimise recoveries.

This process is manually intensive and often requires each customer to submit PDF statements and self-declare data. Agents then have to judge the customer's affordability.