Funding Source: UK

Problem: Companies and financial institutions have difficulties identifying which company information is important to develop frameworks that consolidate such information to reveal the patterns that can act as relevant and reliable future predictors. They need help to convert their data into actionable intelligence, to understand customer behavior and trends while controlling credit risk and identifying opportunities. They need to make decisions based on logical evidence rather than their own intuition to ensure profitably and run through worst-case scenarios to prepare themselves for future risks.
Solution: RISKROBOT combines Artificial Intelligence, Machine Learning and Big Data with expert advice and judgement to produce the predictive analytics required for making credit decisions and to help people manage risks. It can support all types of credit risk models, all types of retail portfolios and products and comes encoded with all required and current credit risk regulation. It still consists of expert judgement since human expertise is trusted, and so that they aren’t replaced by AI.

Problem: Most of the youth find managing their finances as a daunting task due to a lack of financial awareness and education. Most financial service applications use technical jargon and impersonalized services which are even more off-putting. Financial service apps need to be made easier-to-use and need to keep users actively engaged in making better financial decisions. They also need to provide the right money management advice based on the needs of the users.

Solution: Cleo is a financial services app that provides an AI assistant that uses simplicity and humor to help users deal with their finances better. It helps users create a personalized budget, based on income, bills and necessary expenses and it calculates how much users can spend and save.

Problem: To build intelligent products so businesses can leverage Open Banking to enhance affordability and risk assessments.
To create predictive insights that are built by combining transaction and loan outcome data and are available through underwriting platform to improve accuracy in decision-making, and support customers.
Solution: To build predictive features and risk models that drive better outcomes for the customers. Open Banking and loan outcome data was combined to build decision-ready, predictive features and scores that have proven results – reducing defaults and increasing acceptances. The classification engine uses state of the art modelling techniques and millions of real transactions to categorize a report’s financial data.
The tool helps businesses in risk assessments. The tool provides predictive insights that are built by combining transaction and loan outcome data and are available through API or underwriting platform.